business performance research paper

• Open access
• Published: 18 October 2016

Business process performance measurement: a structured literature review of indicators, measures and metrics

• Amy Van Looy   ORCID: orcid.org/0000-0002-7992-1528 1 &
• Aygun Shafagatova 1  

SpringerPlus volume  5 , Article number:  1797 ( 2016 ) Cite this article

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Measuring the performance of business processes has become a central issue in both academia and business, since organizations are challenged to achieve effective and efficient results. Applying performance measurement models to this purpose ensures alignment with a business strategy, which implies that the choice of performance indicators is organization-dependent. Nonetheless, such measurement models generally suffer from a lack of guidance regarding the performance indicators that exist and how they can be concretized in practice. To fill this gap, we conducted a structured literature review to find patterns or trends in the research on business process performance measurement. The study also documents an extended list of 140 process-related performance indicators in a systematic manner by further categorizing them into 11 performance perspectives in order to gain a holistic view. Managers and scholars can consult the provided list to choose the indicators that are of interest to them, considering each perspective. The structured literature review concludes with avenues for further research.

Since organizations endeavor to measure what they manage, performance measurement is a central issue in both the literature and in practice (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Performance measurement is a multidisciplinary topic that is highly studied by both the management and information systems domains (business process management or BPM in particular). Different performance measurement models, systems and frameworks have been developed by academia and practitioners (Cross and Lynch 1988 ; Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Kueng 2000 ; Neely et al. 2000 ). While measurement models were initially limited to financial performance (e.g., traditional controlling models), a more balanced and integrated approach was needed beginning in the 1990s due to the challenges of the rapidly changing society and technology; this approach resulted in multi-dimensional models. Perhaps the best known multi-dimensional performance measurement model is the Balanced Scorecard (BSC) developed by Kaplan and Norton ( 1996 , 2001 ), which takes a four-dimensional approach to organizational performance: (1) financial perspective, (2) customer perspective, (3) internal business process perspective, and (4) “learning and growth” perspective. The BSC helps translate an organization’s strategy into operational performance indicators (also called performance measures or metrics) and objectives with targets for each of these performance perspectives. Even today, the BSC is by far the most used performance measurement approach in the business world (Bain Company 2015 ; Sullivan 2001 ; Ulfeder 2004 ).

Equally important for measuring an organization’s performance is process-oriented management or business process management (BPM), which is “about managing entire chains of events, activities and decisions that ultimately add value to the organization and its customers. These ‘chains of events, activities and decisions’ are called processes” (Dumas et al. 2013 : p. 1). In particular, an organization can do more with its current resources by boosting the effectiveness and efficiency of its way of working (i.e., its business processes) (Sullivan 2001 ). In this regard, academic research also suggests a strong link between business process performance and organizational performance, either in the sense of a causal relationship (Melville et al. 2004 ; Smith and Reece 1999 ) or as distinctive indicators that co-exist, as in the BSC (Kaplan and Norton 1996 , 2001 ).

Nonetheless, performance measurement models tend to give little guidance on how business (process) performance indicators can be chosen and operationalized (Shah et al. 2012 ). They are limited to mainly defining performance perspectives, possibly with some examples or steps to derive performance indicators (Neely et al. 2000 ), but without offering concrete indicators. Whereas fairly large bodies of research exist for both performance models and business processes, no structured literature review of (process) performance measurement has been carried out thus far. To the best of our knowledge, existing reviews cover one or another aspect of performance measurement; for instance, reviews on measurement models or evaluation criteria for performance indicators (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Despite the considerable importance of a comprehensive and holistic approach to business (process) performance measurement, little is known regarding the state of the research on alternative performance indicators and their operationalization with respect to evaluating the performance of an organization’s work routines. To some extent, this lack of guidance can be explained by the fact that performance indicators are considered organization-dependent, given that strategic alignment is claimed by many measurement models such as the BSC (Kaplan and Norton 1996 , 2001 ). Although the selection of appropriate performance indicators is challenging for practitioners due to the lack of best practices, it is also highly relevant for performance measurement.

The gap that we are studying is the identification and, in particular, the concretization/operationalization of process-related performance indicators. This study enhances the information systems literature, which focuses on the design and development of measurement systems without paying much attention to essential indicators. To fill this gap, our study presents a structured literature review in order to describe the current state of business process performance measurement and related performance indicators. The choice to focus on the business process management (BPM) discipline is motivated by the close link between organizational performance and business process performance, as well as to ensure a clear scope (specifically targeting an organization’s way of working). Accordingly, the study addresses the following research questions.

RQ1. What is the current state of the research on business process performance measurement?

RQ2. Which indicators, measures and metrics are used or mentioned in the current literature related to business process performance?

The objective of RQ1 is to identify patterns in the current body of knowledge and to note weaknesses, whereas RQ2 mainly intends to develop an extended list of measurable process performance indicators, categorized into recognized performance perspectives, which can be tailored to diverse purposes. This list could, for instance, serve as a supplement to existing performance measurement models. Practitioners can use the list as a source for best practice indicators from academic research to find and select a subset of performance indicators that fit their strategy. The study will thus not address the development of specific measurement systems but rather the indicators to be used within such systems. To make our intended list system-independent, we will begin with the BSC approach and extend its performance perspectives. Given this generic approach, the research findings can also be used by scholars when building and testing theoretical models in which process performance is one of the factors that must be concretized.

The remainder of this article is structured as follows. “ Theoretical background ” section describes the theoretical background of performance measurement models and performance indicators. Next, the methodology for our structured literature review is detailed in “ Methods ” section. The subsequent sections present the results for RQ1 (“ Results for RQ1 ” section) and RQ2 (“ Results for RQ2 ” section). The discussion of the results in provided in “ Discussion ” section, followed by concluding comments (“ Conclusion ” section).

Theoretical background

This section addresses the concepts of performance measurement models and performance indicators separately in order to be able to differentiate them further in the study.

Performance measurement models

According to overviews in the performance literature (Heckl and Moormann 2010; Neely 2005 ; Richard et al. 2009 ), some of the most cited performance measurement models are the Balanced Scorecard (Kaplan and Norton 1996 , 2001 ), self-assessment excellence models such as the EFQM ( 2010 ), and the models by Cross and Lynch ( 1988 ), Kueng ( 2000 ) and Neely et al. ( 2000 ). A distinction should, however, be made between models focusing on the entire business (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ) and models focusing on a single business process (Kueng 2000 ; Neely et al. 2000 ).

Organizational performance measurement models

Organizational performance measurement models typically intend to provide a holistic view of an organization’s performance by considering different performance perspectives. As mentioned earlier, the BSC provides four perspectives for which objectives and performance indicators ensure alignment between strategies and operations (Fig.  1 ) (Kaplan and Norton 1996 , 2001 ). Other organizational performance measurement models provide similar perspectives. For instance, Cross and Lynch ( 1988 ) offer a four-level performance pyramid: (1) a top level with a vision, (2) a second level with objectives per business unit in market and financial terms, (3) a third level with objectives per business operating system in terms of customer satisfaction, flexibility and productivity, and (4) a bottom level with operational objectives for quality, delivery, process time and costs. Another alternative view on organizational performance measurement is given in business excellence models, which focus on an evaluation through self-assessment rather than on strategic alignment, albeit by also offering performance perspectives. For instance, the EFQM ( 2010 ) distinguishes enablers [i.e., (1) leadership, (2) people, (3) strategy, (4) partnerships and resources, and (5) processes, products and services] from results [i.e., (1) people results, (2) customer results, (3) society results, and (4) key results], and a feedback loop for learning, creativity and innovation.

An overview of the performance perspectives in Kaplan and Norton ( 1996 , 2001 )

Since the BSC is the most used performance measurement model, we have chosen it as a reference model to illustrate the function of an organizational performance measurement model (Kaplan and Norton 1996 , 2001 ). The BSC is designed to find a balance between financial and non-financial performance indicators, between the interests of internal and external stakeholders, and between presenting past performance and predicting future performance. The BSC encourages organizations to directly derive (strategic) long-term objectives from the overall strategy and to link them to (operational) short-term targets. Concrete performance measures or indicators should be defined to periodically measure the objectives. These indicators are located on one of the four performance perspectives in Fig.  1 (i.e., ideally with a maximum of five indicators per perspective).

Table  1 illustrates how an organizational strategy can be translated into operational terms using the BSC.

During periodical measurements using the BSC, managers can assign color-coded labels according to actual performance on short-term targets: (1) a green label if the organization has achieved the target, (2) an orange label if it is almost achieved, or (3) a red label if it is not achieved. Orange and red labels thus indicate areas for improvement.

Furthermore, the BSC assumes a causal or logical relationship between the four performance perspectives. An increase in the competences of employees (i.e., performance related to “learning and growth”) is expected to positively affect the quality of products and services (i.e., internal business process performance), which in turn will lead to improved customer perceptions (i.e., customer performance). The results for the previous perspectives will then contribute to financial performance to ultimately realize the organization’s strategy, mission and vision (Kaplan and Norton 1996 , 2001 ). Hence, indicators belonging to the financial and customer perspectives are assumed to measure performance outcomes, whereas indicators from the perspectives of internal business processes and “learning and growth” are considered as typical performance drivers (Kaplan and Norton 2004 ).

Despite its widespread use and acceptance, the BSC is also criticized for appearing too general by managers who are challenged to adapt it to the culture of their organization (Butler et al. 1997 ) or find suitable indicators to capture the various aspects of their organization’s strategy (Shah et al. 2012 ; Vaivio 1999 ). Additionally, researchers question the choice of four distinct performance perspectives (i.e., which do not include perspectives related to inter-organizational performance or sustainability issues) (EFQM 2010 ; Hubbard 2009 , Kueng 2000 ). Further, the causal relationship among the BSC perspectives has been questioned (Norreklit 2000 ). To some degree, Kaplan and Norton ( 2004 ) responded to this criticism by introducing strategy maps that focus more on the causal relationships and the alignment of intangible assets.

Business process performance measurement models

In addition to organizational models, performance measurement can also focus on a single business process, such as statistical process control, workflow-based monitoring or process performance measurement systems (Kueng 2000 ; Neely et al. 2000 ). The approach taken in business process performance measurement is generally less holistic than the BSC. For instance, in an established BPM handbook, Dumas et al. ( 2013 ) position time, cost, quality and flexibility as the typical performance perspectives of business process performance measurement (Fig.  2 ). Similar to organizational performance measurement, concrete performance measures or indicators should be defined for each process performance perspective. In this sense, the established perspectives of Dumas et al. ( 2013 ) seem to further refine the internal business process performance perspective of the BSC.

An overview of the performance perspectives in Dumas et al. ( 2013 )

Neely et al. ( 2000 ), on the other hand, present ten steps to develop or define process performance indicators. The process performance measurement system of Kueng ( 2000 ) is also of high importance, which is visualized as a “goal and performance indicator tree” with five process performance perspectives: (1) financial view, (2) customer view, (3) employee view, (4) societal view, and (5) innovation view. Kueng ( 2000 ) thus suggests a more holistic approach towards process performance, similar to organizational performance, given the central role of business processes in an organization. He does so by focusing more on the different stakeholders involved in certain business processes.

Performance indicators

Section “ Performance measurement models ” explained that performance measurement models typically distinguish different performance perspectives for which performance indicators should be further defined. We must, however, note that we consider performance measures, performance metrics and (key) performance indicators as synonyms (Dumas et al. 2013 ). For reasons of conciseness, this work will mainly refer to performance indicators without mentioning the synonyms. In addition to a name, each performance indicator should also have a concretization or operationalization that describes exactly how it is measured and that can result in a value to be compared against a target. For instance, regarding the example in Table  1 , the qualitative statements to measure customer satisfaction constitute an operationalization. Nonetheless, different ways of operationalization can be applied to measure the same performance indicator. Since organizations can profit from reusing existing performance indicators and the related operationalization instead of inventing new ones (i.e., to facilitate benchmarking and save time), this work investigates which performance indicators are used or mentioned in the literature on business process performance and how they are operationalized.

Neely et al. ( 2000 ) and Richard et al. ( 2009 ) both present evaluation criteria for performance indicators (i.e., in the sense of desirable characteristics or review implications), which summarize the general consensus in the performance literature. First, the literature strongly agrees that performance indicators are organization-dependent and should be derived from an organization’s objectives, strategy, mission and vision. Secondly, consensus in the literature also exists regarding the need to combine financial and non-financial performance indicators. Nonetheless, disagreement still seems to exist in terms of whether objective and subjective indicators need to be combined, with objective indicators preferred by most advocates. Although subjective (or quasi-objective) indicators face challenges from bias, their use has some advantages; for instance, to include stakeholders in an assessment, to address latent constructs or to facilitate benchmarking when a fixed reference point is missing (Hubbard 2009 ; Richard et al. 2009 ). Moreover, empirical research has shown that subjective (or quasi-objective) indicators are more or less correlated with objective indicators, depending on the level of detail of the subjective question (Richard et al. 2009 ). For instance, a subjective question can be made more objective by using clear definitions or by selecting only well-informed respondents to reduce bias.

We conducted a structured literature review (SLR) to find papers dealing with performance measurement in the business process literature. SLR can be defined as “a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest” (Kitchenham 2007 : p. vi). An SLR is a meta study that identifies and summarizes evidence from earlier research (King and He 2005 ) or a way to address a potentially large number of identified sources based on a strict protocol used to search and appraise the literature (Boellt and Cecez-Kecmanovic 2015 ). It is systematic in the sense of a systematic approach to finding relevant papers and a systematic way of classifying the papers. Hence, according to Boellt and Cecez-Kecmanovic ( 2015 ), SLR as a specific type of literature review can only be used when two conditions are met. First, the topic should be well-specified and closely formulated (i.e., limited to performance measurement in the context of business processes) to potentially identify all relevant literature based on inclusion and exclusion criteria. Secondly, the research questions should be answered by extracting and aggregating evidence from the identified literature based on a high-level summary or bibliometric-type of content analysis. Furthermore, King and He ( 2005 ) also refer to a statistical analysis of existing literature.

Informed by the established guidelines proposed by Kitchenham ( 2007 ), we undertook the review in distinct stages: (1) formulating the research questions and the search strategy, (2) filtering and extracting data based on inclusion and exclusion criteria, and (3) synthesizing the findings. The remainder of this section describes the details of each stage.

Formulating the research questions and search strategy

A comprehensive and unbiased search is one of the fundamental factors that distinguish a systematic review from a traditional literature review (Kitchenham 2007 ). For this purpose, a systematic search begins with the identification of keywords and search terms that are derived from the research questions. Based on the research questions stipulated in the introduction, the SLR protocol (Boellt and Cecez-Kecmanovic 2015 ) for our study was defined, as shown in Table  2 .

The ISI Web of Science (WoS) database was searched using predetermined search terms in November 2015. This database was selected because it is used by many universities and results in the most outstanding publications, thus increasing the quality of our findings. An important requirement was that the papers focus on “business process*” (BP). This keyword was used in combination with at least one of the following: (1) “performance indicator*”, (2) “performance metric*”, (3) “performance measur*”. All combinations of “keyword in topic” (TO) and “keyword in title” (TI) have been used.

Table  3 shows the degree to which the initial sample sizes varied, with 433 resulting papers for the most permissive search query (TOxTO) and 19 papers for the most restrictive one (TIxTI). The next stage started with the most permissive search query in an effort to select and assess as many relevant publications as possible.

Filtering and extracting data

Figure  3 summarizes the procedure for searching and selecting the literature to be reviewed. The list of papers found in the previous stage was filtered by deleting 35 duplicates, and the remaining 398 papers were further narrowed to 153 papers by evaluating their title and abstract. After screening the body of the texts, 76 full-text papers were considered relevant for our scope and constituted the final sample (“Appendix 1 ”).

Exclusion of papers and number of primary studies

More specifically, studies were excluded if their main focus was not business process performance measurement or if they did not refer to indicators, measures or metrics for business performance. The inclusion of studies was not restricted to any specific type of intervention or outcome. The SLR thus included all types of research studies that were written in English and published up to and including November 2015. Furthermore, publication by peer-reviewed publication outlets (e.g., journals or conference proceedings) was considered as a quality criterion to ensure the academic level of the research papers.

Synthesizing the findings

The analysis of the final sample was performed by means of narrative and descriptive analysis techniques. For RQ1, the 76 papers were analyzed on the basis of bibliometric data (e.g., publication type, publication year, geography) and general performance measurement issues by paying attention to the methodology and focus of the study. Details are provided in “Appendix 2 ”.

For RQ2, all the selected papers were screened to identify concrete performance indicators in order to generate a comprehensive list or checklist. The latter was done in different phases. In the first phase, the structured literature review allowed us to analyze which performance indicators are mainly used in the process literature and how they are concretized (e.g., in a question or mathematical formulation), resulting in an unstructured list of potential performance indicators. The indicators were also synthesized by combining similar indicators and rephrasing them into more generic terms.

The next phase was a comparative study to categorize the output of phase 1 into the commonly used measurement models in the performance literature (see “ Theoretical background ” section). For the purpose of this study, we specifically looked for those organizational performance models, mentioned in “ Theoretical background ” section, that are cited the most and that suggest categories, dimensions or performance perspectives that can be re-used (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ; Kueng 2000 ). Since the BSC (Kaplan and Norton 1996 , 2001 ) is the most commonly used of these measurement models, we began with the BSC as the overall framework to categorize the observed indicators related to business (process) performance, supplemented with an established view on process performance from the process literature (Dumas et al. 2013 ). Subsequently, a structured list of potential performance indicators was obtained.

In the third and final phase, an evaluation study was performed to validate whether the output of phase 2 is sufficiently comprehensive according to other performance measurement models, i.e., not included in our sample and differing from the most commonly used performance measurement models. Therefore, we investigated the degree to which our structured list covers the items in two variants or concretizations of the BSC. Hence, a validation by other theoretical models is provided. We note that a validation by subject-matter experts is out of scope for a structured literature review but relates to an opportunity for further research.

Results for RQ1

The final sample of 76 papers consists of 46 journal papers and 30 conference papers (Fig.  4 ), indicating a wide variety of outlets to reach the audience via operations and production-related journals in particular or in lower-ranked (Recker 2013 ) information systems journals.

The distribution of the sampled papers per publication type (N = 76)

When considering the chronological distribution of the sampled papers, Fig.  5 indicates an increase in the uptake of the topic in recent years, particularly for conference papers but also for journal publications since 2005.

The chronological distribution of the sampled papers per publication type (N = 76)

This uptake seems particularly situated in the Western world and Asia (Fig.  6 ). The countries with five or more papers in our sample are Germany (12 papers), the US (6 papers), Spain (5 papers), Croatia (5 papers) and China (5 papers). Figure  6 shows that business process performance measurement is a worldwide topic, with papers across the different continents. Nonetheless, a possible explanation for the higher coverage in the Western world could be due to its long tradition of measuring work (i.e., BSC origins).

The geographical distribution of the sampled papers per continent, based on a paper’s first author (N = 76)

The vast majority of the sampled papers address artifacts related to business (process) performance measurement. When looking at the research paradigm in which the papers are situated (Fig.  7 ), 71 % address design-science research, whereas 17 % conduct research in behavioral science and 12 % present a literature review. This could be another explanation for the increasing uptake in the Western world, as many design-science researchers are from Europe or North America (March and Smith 1995 ; Peffers et al. 2012 ).

The distribution of the sampled journal papers per research paradigm (N = 76)

Figure  8 supplements Fig.  7 by specifying the research methods used in the papers. For the behavioral-science papers, case studies and surveys are equally used. The 54 papers that are situated within the design-science paradigm explicitly refer to models, meta-models, frameworks, methods and/or tools. When mapping these 54 papers to the four artifact types of March and Smith ( 1995 ), the vast majority present (1) methods in the sense of steps to perform a task (e.g., algorithms or guidelines for performance measurement) and/or (2) models to describe solutions for the topic. The number of papers dealing with (3) constructs or a vocabulary and/or (4) instantiations or tools is much more limited, with 14 construct-related papers and 9 instantiations in our sample. We also looked at which evaluation methods, defined by Peffers et al. ( 2012 ), are typically used in the sampled design-science papers. While 7 of the 54 design-science papers do not seem to report on any evaluation effort, our sample confirms that most papers apply one or another evaluation method. Case studies and illustrative scenarios appear to be the most frequently used methods to evaluate design-science research on business (process) performance measurement.

The distribution of the sampled journal papers per research method (N = 76)

The sampled design-science research papers typically build and test performance measurement frameworks, systems or models or suggest meta-models and generic templates to integrate performance indicators into the process models of an organization. Such papers can focus on the process level, organizational level or even cross-organizational level. Nonetheless, the indicators mentioned in those papers are illustrative rather than comprehensive. An all-inclusive list of generic performance indicators seems to be missing. Some authors propose a set of indicators, but those indicators are specific to a certain domain or sector instead of being generic. For instance, Table  4 shows that 36 of the 76 sampled papers are dedicated to a specific domain or sector, such as technology-related aspects or supply chain management.

Furthermore, the reviewed literature was analyzed with regard to its (1) scope, (2) functionalities, (3) terminology, and (4) foundations.

Starting with scope, it is observed that nearly two-thirds of the sampled papers can be categorized as dealing with process-oriented performance measurement, whereas one-third focuses more on general performance measurement and management issues. Nonetheless, most of the studies of process performance also include general performance measurement as a supporting concept. A minor cluster of eight research papers specifically focuses on business process reengineering and measurement systems to evaluate the results of reengineering efforts. Furthermore, other researchers focus on the measurement and assessment of interoperability issues and supply chain management measurements.

Secondly, while analyzing the literature, two groups of papers were identified based on their functionalities: (1) focusing on performance measurement systems or frameworks, and (2) focusing on certain performance indicators and their categorization. Regarding the first group, it should be mentioned that while the process of building or developing a performance measurement system (PMS) or framework is well-researched, only a small number of papers explicitly address process performance measurement systems (PPMS). The papers in this first group typically suggest concrete steps or stages to be followed by particular organizations or discuss the conceptual characteristics and design of a performance measurement system. Regarding the second group of performance indicators, we can differentiate two sub-groups. Some authors focus on the process of defining performance indicators by listing requirements or quality characteristics that an indicator should meet. However, many more authors are interested in integrating performance indicators into the process models or the whole architecture of an organization, and they suggest concrete solutions to do so. Compared to the first group of papers, this second group deals more with the categorization of performance indicators into domains (financial/non-financial, lag/lead, external/internal, BSC dimensions) or levels (strategic, tactical, operational).

Thirdly, regarding terminology, different terms are used by different authors to discuss performance measurement. Performance “indicator” is the most commonly used term among the reviewed papers. For instance, it is frequently used in reference to a key performance indicator (KPI), a KPI area or a performance indicator (PI). The concept of a process performance indicator (PPI) is also used, mainly in the process-oriented literature. Performance “measure” is another prevalent term in the papers. The least-used term is performance “metric” (i.e., in only nine papers). Although the concepts of performance indicators, measures and metrics are used interchangeably throughout most of the papers, the concepts are sometimes defined in different ways. For instance, paper 17 defines a performance indicator as a metric, and paper 49 defines a performance measure as an indicator. On the other hand, paper 7 defines a performance indicator as a set of measures. Yet another perspective is taken in paper 74, which defines a performance measure as “a description of something that can be directly measured (e.g., number of reworks per day)”, while defining a performance indicator as “a description of something that is calculated from performance measures (e.g., percentage reworks per day per direct employee” (p. 386). Inconsistencies exist not only in defining indicators but also in describing performance goals. For instance, some authors include a sign (e.g., minus or plus) or a verb (e.g., decrease or increase) in front of an indicator. Other authors attempt to describe performance goals in a SMART way—for instance, by including a time indication (e.g., “within a certain period”) and/or target (e.g., “5 % of all orders”)—whereas most of the authors are less precise. Hence, a great degree of ambiguity exists in the formulation of performance objectives among to the reviewed papers.

Finally, regarding the papers’ foundations, “ Performance measurement models ” section already indicated that the BSC plays an important role in the general literature on performance management systems (PMS), while Kueng ( 2000 ) also offers influential arguments on process performance measurement systems (PPMS). In our literature review, we observed that the BSC was mentioned in 43 of the 76 papers and that the results of 19 papers were mainly based on the BSC (Fig.  9 ). This finding provides additional evidence that the BSC can be considered the most frequently used performance model in academia as well. However, the measurement model of Kueng ( 2000 ) was also mentioned in the sampled papers on PPMS, though less frequently (i.e., in six papers).

The importance of the BSC according to the sampled papers (N = 76)

Interestingly, the BSC is also criticized by the sampled papers for not being comprehensive; for instance, due to the exclusion of environmental aspects, supply chain management aspects or cross-organizational processes. In response, some of the sampled papers also define sector-specific BSC indicators or suggest additional steps or indicators to make the process or business more sustainable (see Table  4 ). Nonetheless, the majority of the papers agree on the need for integrated and multidimensional measurement systems, such as the BSC, and on the importance of directly linking performance measurement to an organization’s strategy. However, while these papers mention the required link with strategy, the prioritization of indicators according to their strategic importance has been studied very little thus far.

Results for RQ2

For RQ2, the sampled papers were reviewed to distinguish papers with performance indicators from papers without performance indicators. A further distinction was made between indicators found with operationalization (i.e., concretization by means of a question or formula) and those without operationalization. We note that for many indicators, no operationalization was available. We discovered that only 30 of the 76 sampled papers contained some type of performance indicator (namely 3, 5, 6, 7, 11, 16, 17, 18, 20, 22, 26, 27, 30, 35, 37, 40, 43, 46, 49, 51, 52, 53, 55, 57, 58, 59, 60, 66, 71, 73). In total, approximately 380 individual indicators were found throughout all the sampled papers (including duplicates), which were combined based on similarities and modified to use more generic terms. This resulted in 87 indicators with operationalization (“Appendix 3 ”) and 48 indicators without operationalization (“Appendix 4 ”).

The 87 indicators with operationalization were then categorized according to the four perspectives of the BSC (i.e., financial, customer, business processes, and “learning and growth”) (Kaplan and Norton 1996 , 2001 ) and the four established dimensions of process performance (i.e., time, cost, quality, and flexibility) (Dumas et al. 2013 ). In particular, based in the identified indicators, we revealed 11 sub-perspectives within the initial BSC perspectives to better emphasize the focus of the indicators and the different target groups (Table  5 ): (1) financial performance for shareholders and top management, (2) customer-related performance, (3) supplier-related performance, (4) society-related performance, (5) general process performance, (6) time-related process performance, (7) cost-related process performance, (8) process performance related to internal quality, (9) flexibility-related process performance, (10) (digital) innovation performance, and (11) employee-related performance.

For reasons of objectivity, the observed performance indicators were assigned to a single perspective starting from recognized frameworks (Kaplan and Norton 1996 , 2001 ; Dumas et al. 2013 ). Bias was further reduced by following the definitions of Table  5 . Furthermore, the authors of this article first classified the indicators individually and then reached consensus to obtain a more objective categorization.

Additional rationale for the identification of 11 performance perspectives is presented in Table  6 , which compares our observations with the perspectives adopted by the most commonly used performance measurement models (see “ Theoretical background ” section). This comparison allows us to highlight similarities and differences with other respected models. In particular, Table  6 shows that we did not observe a dedicated perspective for strategy (EFQM 2010 ) and that we did not differentiate between financial indicators and market indicators (Cross and Lynch 1988 ). Nonetheless, the similarities in Table  6 prevail. For instance, Cross and Lynch ( 1988 ) also acknowledge different process dimensions. Further, Kueng ( 2000 ) and the EFQM ( 2010 ) also differentiate employee performance from innovation performance, and they both add a separate perspective for results related to the entire society.

Figure  10 summarizes the number of performance indicators that we identified in the process literature per observed performance perspective. Not surprisingly, the initial BSC perspective of internal business process performance contains most of the performance indicators: 29 of 87 indicators. However, the other initial BSC perspectives are also covered by a relatively high number of indicators: 16 indicators for both financial performance and customer-related performance and 26 indicators for “learning and growth”. This result confirms the close link between process performance and organizational performance, as mentioned in the introduction.

The number of performance indicators with operationalization per performance perspective

A more detailed comparison of the perspectives provides interesting refinements to the state of the research. More specifically, Fig.  10 shows that five performance perspectives have more than ten indicators in the sample, indicating that academic research focuses more on financial performance for shareholders and top management and performance related to customers, process time, innovation and employees. On the other hand, fewer than five performance indicators were found in the sample for the perspectives related to suppliers, society, process costs and process flexibility, indicating that the literature focuses less on those perspectives. The latter remains largely overlooked by academic research, possibly due to the newly emerging character of these perspectives.

We must, however, note that the majority of the performance indicators are mentioned in only a few papers. For instance, 59 of the 87 indicators were cited in a single paper, whereas the remainder are mentioned in more than one paper. Eleven performance indicators are frequently mentioned in the process literature (i.e., by five or more papers). These indicators include four indicators of customer-related performance (i.e., customer complaints, perceived customer satisfaction, query time, and delivery reliability), three indicators of time-related process performance (i.e., process cycle time, sub-process turnaround time, and process waiting time), one cost-related performance indicator (i.e., process cost), two indicators of process performance related to internal quality (i.e., quality of internal outputs and deadline adherence), and one indicator of employee performance (i.e., perceived employee satisfaction).

Consistent with “ Performance indicators ” section, the different performance perspectives are a combination of financial or cost-related indicators with non-financial data. The latter also take the upper hand in our sample. Furthermore, the sample includes a combination of objective and subjective indicators, and the vast majority are objective indicators. Only eight indicators explicitly refer to qualitative scales; for instance, to measure the degree of satisfaction of the different stakeholder groups. For all the other performance indicators, a quantifiable alternative is provided.

It is important to remember that a distinction was made between the indicators with operationalization and those without operationalization. The list of 87 performance indicators, as given in “Appendix 3 ”, can thus be extended with those indicators for which operationalization is missing in the reviewed literature. Specifically, we found 48 additional performance indicators (“Appendix 4 ”) that mainly address supplier performance, process performance related to costs and flexibility, and the employee-related aspects of digital innovation. Consequently, this structured literature review uncovered a total of 135 performance indicators that are directly or indirectly linked to business process performance.

Finally, the total list of 135 performance indicators was evaluated for its comprehensiveness by comparing the identified indicators with other BSC variants that were not included in our sample. More specifically, based on a random search, we looked for two BSC variants in the Web of Science that did not fit the search strategy of this structured literature review: one that did not fit the search term of “business process*” (Hubbard 2009 ) and another that did not fit any of the performance-related search terms of “performance indicator*”, “performance metric*” or “performance measur*” (Bronzo et al. 2013 ). These two BSC variants cover 30 and 17 performance indicators, respectively, and are thus less comprehensive than the extended list presented in this study. Most of the performance indicators suggested by the two BSC variants are either directly covered in our findings or could be derived after recalculations. Only five performance indicators could not be linked to our list of 135 indicators, and these suggest possible refinements regarding (1) the growth potential of employees, (2) new markets, (3) the social performance of suppliers, (4) philanthropy, or (5) industry-specific events.

This structured literature review culminated in an extended list of 140 performance indicators: 87 indicators with operationalization, 48 indicators without operationalization and 5 refinements derived from two other BSC variants. The evaluation of our findings against two BSC variants validated our work in the sense that we present a more exhaustive list of performance indicators, with operationalization for most, and that only minor refinements could be added. However, the comprehensiveness of our findings can be claimed only to a certain extent given the limitations of our predefined search strategy and the lack of empirical validation by subject-matter experts or organizations. Notwithstanding these limitations, conclusions can be drawn from the large sample of 76 papers to respond to the research questions (RQs).

Regarding RQ1 on the state of the research on business process performance measurement, the literature review provided additional evidence for the omnipresence of the BSC. Most of the sampled papers mentioned or used the BSC as a starting point and basis for their research and analysis. The literature study also showed a variety of research topics, ranging from behavioral-science to design-science research and from a focus on performance measurement models to a focus on performance indicators. In addition to inconsistencies in the terminology used to describe performance indicators and targets, the main weakness uncovered in this literature review deals with the concretization of performance indicators supplementing performance measurement systems. The SLR results suggest that none of the reviewed papers offers a comprehensive measurement framework, specifically one that includes and extends the BSC perspectives, is process-driven and encompasses as many concrete performance indicators as possible. Such a comprehensive framework could be used as a checklist or a best practice for reference when defining specific performance indicators. Hence, the current literature review offers a first step towards such a comprehensive framework by means of an extended list of possible performance indicators bundled in 11 performance perspectives (RQ2).

Regarding RQ2 on process performance indicators, the literature study revealed that scholars measure performance in many different ways and without sharing much detail regarding the operationalization of the measurement instruments, which makes a comparison of research results more difficult. As such, the extended list of performance indicators is our main contribution and fills a gap in the literature by providing a detailed overview of performance indicators mentioned or used in the literature on business process performance. Another novel aspect is that we responded to the criticism of missing perspectives in the original BSC (EFQM 2010 ; Hubbard 2009 ; Kueng 2000 ) and identified the narrow view of performance typically taken in the process literature (Dumas et al. 2013 ). Figures  1 and 2 are now combined and extended in a more exhaustive way, namely by means of more perspectives than are offered by other attempts (Table  6 ), by explicitly differentiating between performance drivers (or lead indicators) and performance outcomes (or lag indicators), and by considering concrete performance indicators.

Our work also demonstrated that all perspectives in the BSC (Kaplan and Norton 1996 , 2001 ) relate to business process performance to some degree. In other words, while the BSC is a strategic tool for organizational performance measurement, it is actually based on indicators that originate from business processes. More specifically, in addition to the perspective of internal business processes, the financial performance perspective typically refers to sales or revenues gained while doing business, particularly after executing business processes. The customer perspective relates to the implications of product or service delivery, specifically to the interactions throughout business processes, whereas the “learning and growth” perspective relates to innovations in the way of working (i.e., business processes) and the degree to which employees are prepared to conduct and innovate business processes. The BSC, however, does not present sub-perspectives and thus takes a more high-level view of performance. Hence, the BSC can be extended based on other categorizations made in the reviewed literature; for instance, related to internal/external, strategic/operational, financial/non-financial, or cost/time/quality/flexibility.

Therefore, this study refined the initial BSC perspectives into eleven performance perspectives (Fig.  11 ) by applying three other performance measurement models (Cross and Lynch 1988 ; EFQM 2010 ; Kueng 2000 ) and the respected Devil’s quadrangle for process performance (Dumas et al. 2013 ). Additionally, a more holistic view of business process performance can be obtained by measuring each performance perspective of Fig.  11 than can be achieved by using the established dimensions of time, cost, quality and flexibility as commonly proposed in the process literature (Dumas et al. 2013 ). As such, this study demonstrated a highly relevant synergy between the disciplines of process management, organization management and performance management.

An overview of the observed performance perspectives in the business process literature

We also found out that not all the performance perspectives in Fig.  11 are equally represented in the studied literature. In particular, the perspectives related to suppliers, society, process costs and process flexibility seem under-researched thus far.

The eleven performance perspectives (Fig.  11 ) can be used by organizations and scholars to measure the performance of business processes in a more holistic way, considering the implications for different target groups. For each perspective, performance indicators can be selected that fit particular needs. Thus, we do not assert that every indicator in the extended list of 140 performance indicators should always be measured, since “ Theoretical background ” section emphasized the need for organization-dependent indicators aligned with an organization’s strategy. Instead, our extended list can be a starting point for finding and using appropriate indicators for each performance perspective, without losing much time reflecting on possible indicators or ways to concretize those indicators. Similarly, the list can be used by scholars, since many studies in both the process literature and management literature intend to measure the performance outcomes of theoretical constructs or developed artifacts.

Consistent with the above, we acknowledge that the observed performance indicators originate from different models and paradigms or can be specific to certain processes or sectors. Since our intention is to provide an exhaustive list of indicators that can be applied to measure business process performance, the indicators are not necessarily fully compatible. Instead, our findings allow the recognition of the role of a business context (i.e., the peculiarities of a business activity, an organization or other circumstances). For instance, a manufacturing organization might choose different indicators from our list than a service or non-profit organization (e.g., manufacturing lead time versus friendliness, or carbon dioxide emission versus stakeholder satisfaction).

Another point of discussion is dedicated to the difference between the performance of specific processes (known as “process performance”) and the performance of the entire process portfolio (also called “BPM performance”). While some indicators in our extended list clearly go beyond a single process (e.g., competence-related indicators or employee absenteeism), it is our opinion that the actual performance of multiple processes can be aggregated to obtain BPM performance (e.g., the sum of process waiting times). This distinction between (actual) process performance and BPM performance is useful; for instance, for supplementing models that try to predict the (expected) performance based on capability development, such as process maturity models (e.g., CMMI) and BPM maturity models (Hammer 2007 ; McCormack and Johnson 2001 ). Nonetheless, since this study has shown a close link between process performance, BPM performance, and organizational performance, it seems better to refer to different performance perspectives than to differentiate between such performance types.

In future research, the comprehensiveness of the extended list of performance indicators can be empirically validated by subject-matter experts. Additionally, case studies can be conducted in which organizations apply the list as a supplement to performance measurement models in order to facilitate the selection of indicators for their specific business context. The least covered perspectives in the academic research also seem to be those that are newly emerging (namely, the perspectives related to close collaboration with suppliers, society/sustainability and process flexibility or agility), and these need more attention in future research. Another research avenue is to elaborate on the notion of a business context; for instance, by investigating what it means to have a strategic fit (Venkatraman 1989 ) in terms of performance measurement and which strategies (Miller and Friesen 1986 ; Porter 2008 ; Treacy and Wiersema 1993 ) are typically associated with which performance indicators. Additionally, the impact of environmental aspects, such as market velocity (Eisenhardt and Martin 2000 ), on the choice of performance indicators can be taken into account in future research.

Business quotes such as “If you cannot measure it, you cannot manage it” or “What is measured improves” (P. Drucker) are sometimes criticized because not all important things seem measurable (Ryan 2014 ). Nonetheless, given the perceived need of managers to measure their business and the wide variety of performance indicators (i.e., ranging from quantitative to qualitative and from financial to non-financial), this structured literature review has presented the status of the research on business process performance measurement. This structured approach allowed us to detect weaknesses or inadequacies in the current literature, particularly regarding the definition and concretization of possible performance indicators. We continued by taking a holistic view of the categorization of the observed performance indicators (i.e., measures or metrics) into 11 performance perspectives based on relevant performance measurement models and established process performance dimensions.

The identified performance indicators within the 11 perspectives constitute an extended list from which practitioners and researchers can select appropriate indicators depending on their needs. In total, the structured literature review resulted in 140 possible performance indicators: 87 indicators with operationalization, 48 additional indicators that need further concretization, and 5 refinements based on other Balanced Scorecard (BSC) variants. As such, the 11 performance perspectives with related indicators can be considered a conceptual framework that was derived from the current process literature and theoretically validated by established measurement approaches in organization management.

Future research can empirically validate the conceptual framework by involving subject-matter experts to assess the comprehensiveness of the extended list and refine the missing concretizations, and by undertaking case studies in which the extended list can be applied by specific organizations. Other research avenues exist to investigate the link between actual process performance and expected process performance (as measured in maturity models) or the impact of certain strategic or environmental aspects on the choice of specific performance indicators. Such findings are needed to supplement and enrich existing performance measurement systems.

Abbreviations

behavioral science

business process management

balanced scorecard

design-science

research question

structured literature review

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Authors’ contributions

AVL initiated the conception and design of the study, while AS was responsible for the collection of data (sampling) and identification of performance indicators. The analysis and interpretation of the data was conducted by both authors. AVL was involved in drafting and coordinating the manuscript, and AS in reviewing it critically. Both authors read and approved the final manuscript.

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Appendix 2: The mapping of the structured literature review

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Van Looy, A., Shafagatova, A. Business process performance measurement: a structured literature review of indicators, measures and metrics. SpringerPlus 5 , 1797 (2016). https://doi.org/10.1186/s40064-016-3498-1

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A Literature Analysis on Business Performance for SMEs: Subjective or Objective Measures?

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9 Pages Posted: 22 Jun 2011 Last revised: 25 Jun 2011

Siti Nur 'Atikah Zulkiffli

University of Wollongong; Universiti Malaysia Terengganu (UMT)

Nelson Perera

University of Wollongong, Australia

Date Written: 2011

The study examines the basic research methodologies and approaches for assessing business performance. It provides a critical literature analysis on how perception-based evaluation can be used to evaluate performance, specifically for SMEs. The analysis of the literature covers articles from major journals related to the topic. The methodology followed during the conduct of this paper involves starting with the broad case of articles in general business performance measurement, then focusing on the indicators used to study SMEs. Next, the review screens the list, focusing on the differences between subjective and objective measures. The validity issue related to subjective measures is also discussed.

Keywords: business performance, subjective measures, objective measures, small and medium enterprises

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Business process performance measurement: a structured literature review of indicators, measures and metrics

Amy van looy.

Faculty of Economics and Business Administration – Department of Business Informatics and Operations Management, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgium

Aygun Shafagatova

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The datasets supporting the conclusions of this article are included within the article (and its additional files).

Measuring the performance of business processes has become a central issue in both academia and business, since organizations are challenged to achieve effective and efficient results. Applying performance measurement models to this purpose ensures alignment with a business strategy, which implies that the choice of performance indicators is organization-dependent. Nonetheless, such measurement models generally suffer from a lack of guidance regarding the performance indicators that exist and how they can be concretized in practice. To fill this gap, we conducted a structured literature review to find patterns or trends in the research on business process performance measurement. The study also documents an extended list of 140 process-related performance indicators in a systematic manner by further categorizing them into 11 performance perspectives in order to gain a holistic view. Managers and scholars can consult the provided list to choose the indicators that are of interest to them, considering each perspective. The structured literature review concludes with avenues for further research.

Since organizations endeavor to measure what they manage, performance measurement is a central issue in both the literature and in practice (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Performance measurement is a multidisciplinary topic that is highly studied by both the management and information systems domains (business process management or BPM in particular). Different performance measurement models, systems and frameworks have been developed by academia and practitioners (Cross and Lynch 1988 ; Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Kueng 2000 ; Neely et al. 2000 ). While measurement models were initially limited to financial performance (e.g., traditional controlling models), a more balanced and integrated approach was needed beginning in the 1990s due to the challenges of the rapidly changing society and technology; this approach resulted in multi-dimensional models. Perhaps the best known multi-dimensional performance measurement model is the Balanced Scorecard (BSC) developed by Kaplan and Norton ( 1996 , 2001 ), which takes a four-dimensional approach to organizational performance: (1) financial perspective, (2) customer perspective, (3) internal business process perspective, and (4) “learning and growth” perspective. The BSC helps translate an organization’s strategy into operational performance indicators (also called performance measures or metrics) and objectives with targets for each of these performance perspectives. Even today, the BSC is by far the most used performance measurement approach in the business world (Bain Company 2015 ; Sullivan 2001 ; Ulfeder 2004 ).

Equally important for measuring an organization’s performance is process-oriented management or business process management (BPM), which is “about managing entire chains of events, activities and decisions that ultimately add value to the organization and its customers. These ‘chains of events, activities and decisions’ are called processes” (Dumas et al. 2013 : p. 1). In particular, an organization can do more with its current resources by boosting the effectiveness and efficiency of its way of working (i.e., its business processes) (Sullivan 2001 ). In this regard, academic research also suggests a strong link between business process performance and organizational performance, either in the sense of a causal relationship (Melville et al. 2004 ; Smith and Reece 1999 ) or as distinctive indicators that co-exist, as in the BSC (Kaplan and Norton 1996 , 2001 ).

Nonetheless, performance measurement models tend to give little guidance on how business (process) performance indicators can be chosen and operationalized (Shah et al. 2012 ). They are limited to mainly defining performance perspectives, possibly with some examples or steps to derive performance indicators (Neely et al. 2000 ), but without offering concrete indicators. Whereas fairly large bodies of research exist for both performance models and business processes, no structured literature review of (process) performance measurement has been carried out thus far. To the best of our knowledge, existing reviews cover one or another aspect of performance measurement; for instance, reviews on measurement models or evaluation criteria for performance indicators (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Despite the considerable importance of a comprehensive and holistic approach to business (process) performance measurement, little is known regarding the state of the research on alternative performance indicators and their operationalization with respect to evaluating the performance of an organization’s work routines. To some extent, this lack of guidance can be explained by the fact that performance indicators are considered organization-dependent, given that strategic alignment is claimed by many measurement models such as the BSC (Kaplan and Norton 1996 , 2001 ). Although the selection of appropriate performance indicators is challenging for practitioners due to the lack of best practices, it is also highly relevant for performance measurement.

The gap that we are studying is the identification and, in particular, the concretization/operationalization of process-related performance indicators. This study enhances the information systems literature, which focuses on the design and development of measurement systems without paying much attention to essential indicators. To fill this gap, our study presents a structured literature review in order to describe the current state of business process performance measurement and related performance indicators. The choice to focus on the business process management (BPM) discipline is motivated by the close link between organizational performance and business process performance, as well as to ensure a clear scope (specifically targeting an organization’s way of working). Accordingly, the study addresses the following research questions.

• RQ1. What is the current state of the research on business process performance measurement?
• RQ2. Which indicators, measures and metrics are used or mentioned in the current literature related to business process performance?

The objective of RQ1 is to identify patterns in the current body of knowledge and to note weaknesses, whereas RQ2 mainly intends to develop an extended list of measurable process performance indicators, categorized into recognized performance perspectives, which can be tailored to diverse purposes. This list could, for instance, serve as a supplement to existing performance measurement models. Practitioners can use the list as a source for best practice indicators from academic research to find and select a subset of performance indicators that fit their strategy. The study will thus not address the development of specific measurement systems but rather the indicators to be used within such systems. To make our intended list system-independent, we will begin with the BSC approach and extend its performance perspectives. Given this generic approach, the research findings can also be used by scholars when building and testing theoretical models in which process performance is one of the factors that must be concretized.

The remainder of this article is structured as follows. “ Theoretical background ” section describes the theoretical background of performance measurement models and performance indicators. Next, the methodology for our structured literature review is detailed in “ Methods ” section. The subsequent sections present the results for RQ1 (“ Results for RQ1 ” section) and RQ2 (“ Results for RQ2 ” section). The discussion of the results in provided in “ Discussion ” section, followed by concluding comments (“ Conclusion ” section).

Theoretical background

This section addresses the concepts of performance measurement models and performance indicators separately in order to be able to differentiate them further in the study.

Performance measurement models

According to overviews in the performance literature (Heckl and Moormann 2010; Neely 2005 ; Richard et al. 2009 ), some of the most cited performance measurement models are the Balanced Scorecard (Kaplan and Norton 1996 , 2001 ), self-assessment excellence models such as the EFQM ( 2010 ), and the models by Cross and Lynch ( 1988 ), Kueng ( 2000 ) and Neely et al. ( 2000 ). A distinction should, however, be made between models focusing on the entire business (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ) and models focusing on a single business process (Kueng 2000 ; Neely et al. 2000 ).

Organizational performance measurement models

Organizational performance measurement models typically intend to provide a holistic view of an organization’s performance by considering different performance perspectives. As mentioned earlier, the BSC provides four perspectives for which objectives and performance indicators ensure alignment between strategies and operations (Fig.  1 ) (Kaplan and Norton 1996 , 2001 ). Other organizational performance measurement models provide similar perspectives. For instance, Cross and Lynch ( 1988 ) offer a four-level performance pyramid: (1) a top level with a vision, (2) a second level with objectives per business unit in market and financial terms, (3) a third level with objectives per business operating system in terms of customer satisfaction, flexibility and productivity, and (4) a bottom level with operational objectives for quality, delivery, process time and costs. Another alternative view on organizational performance measurement is given in business excellence models, which focus on an evaluation through self-assessment rather than on strategic alignment, albeit by also offering performance perspectives. For instance, the EFQM ( 2010 ) distinguishes enablers [i.e., (1) leadership, (2) people, (3) strategy, (4) partnerships and resources, and (5) processes, products and services] from results [i.e., (1) people results, (2) customer results, (3) society results, and (4) key results], and a feedback loop for learning, creativity and innovation.

An overview of the performance perspectives in Kaplan and Norton ( 1996 , 2001 )

Since the BSC is the most used performance measurement model, we have chosen it as a reference model to illustrate the function of an organizational performance measurement model (Kaplan and Norton 1996 , 2001 ). The BSC is designed to find a balance between financial and non-financial performance indicators, between the interests of internal and external stakeholders, and between presenting past performance and predicting future performance. The BSC encourages organizations to directly derive (strategic) long-term objectives from the overall strategy and to link them to (operational) short-term targets. Concrete performance measures or indicators should be defined to periodically measure the objectives. These indicators are located on one of the four performance perspectives in Fig.  1 (i.e., ideally with a maximum of five indicators per perspective).

Table  1 illustrates how an organizational strategy can be translated into operational terms using the BSC.

Table 1

An example of translating an organizational strategy into operational terms using the BSC

During periodical measurements using the BSC, managers can assign color-coded labels according to actual performance on short-term targets: (1) a green label if the organization has achieved the target, (2) an orange label if it is almost achieved, or (3) a red label if it is not achieved. Orange and red labels thus indicate areas for improvement.

Furthermore, the BSC assumes a causal or logical relationship between the four performance perspectives. An increase in the competences of employees (i.e., performance related to “learning and growth”) is expected to positively affect the quality of products and services (i.e., internal business process performance), which in turn will lead to improved customer perceptions (i.e., customer performance). The results for the previous perspectives will then contribute to financial performance to ultimately realize the organization’s strategy, mission and vision (Kaplan and Norton 1996 , 2001 ). Hence, indicators belonging to the financial and customer perspectives are assumed to measure performance outcomes, whereas indicators from the perspectives of internal business processes and “learning and growth” are considered as typical performance drivers (Kaplan and Norton 2004 ).

Despite its widespread use and acceptance, the BSC is also criticized for appearing too general by managers who are challenged to adapt it to the culture of their organization (Butler et al. 1997 ) or find suitable indicators to capture the various aspects of their organization’s strategy (Shah et al. 2012 ; Vaivio 1999 ). Additionally, researchers question the choice of four distinct performance perspectives (i.e., which do not include perspectives related to inter-organizational performance or sustainability issues) (EFQM 2010 ; Hubbard 2009 , Kueng 2000 ). Further, the causal relationship among the BSC perspectives has been questioned (Norreklit 2000 ). To some degree, Kaplan and Norton ( 2004 ) responded to this criticism by introducing strategy maps that focus more on the causal relationships and the alignment of intangible assets.

Business process performance measurement models

In addition to organizational models, performance measurement can also focus on a single business process, such as statistical process control, workflow-based monitoring or process performance measurement systems (Kueng 2000 ; Neely et al. 2000 ). The approach taken in business process performance measurement is generally less holistic than the BSC. For instance, in an established BPM handbook, Dumas et al. ( 2013 ) position time, cost, quality and flexibility as the typical performance perspectives of business process performance measurement (Fig.  2 ). Similar to organizational performance measurement, concrete performance measures or indicators should be defined for each process performance perspective. In this sense, the established perspectives of Dumas et al. ( 2013 ) seem to further refine the internal business process performance perspective of the BSC.

An overview of the performance perspectives in Dumas et al. ( 2013 )

Neely et al. ( 2000 ), on the other hand, present ten steps to develop or define process performance indicators. The process performance measurement system of Kueng ( 2000 ) is also of high importance, which is visualized as a “goal and performance indicator tree” with five process performance perspectives: (1) financial view, (2) customer view, (3) employee view, (4) societal view, and (5) innovation view. Kueng ( 2000 ) thus suggests a more holistic approach towards process performance, similar to organizational performance, given the central role of business processes in an organization. He does so by focusing more on the different stakeholders involved in certain business processes.

Performance indicators

Section “ Performance measurement models ” explained that performance measurement models typically distinguish different performance perspectives for which performance indicators should be further defined. We must, however, note that we consider performance measures, performance metrics and (key) performance indicators as synonyms (Dumas et al. 2013 ). For reasons of conciseness, this work will mainly refer to performance indicators without mentioning the synonyms. In addition to a name, each performance indicator should also have a concretization or operationalization that describes exactly how it is measured and that can result in a value to be compared against a target. For instance, regarding the example in Table  1 , the qualitative statements to measure customer satisfaction constitute an operationalization. Nonetheless, different ways of operationalization can be applied to measure the same performance indicator. Since organizations can profit from reusing existing performance indicators and the related operationalization instead of inventing new ones (i.e., to facilitate benchmarking and save time), this work investigates which performance indicators are used or mentioned in the literature on business process performance and how they are operationalized.

Neely et al. ( 2000 ) and Richard et al. ( 2009 ) both present evaluation criteria for performance indicators (i.e., in the sense of desirable characteristics or review implications), which summarize the general consensus in the performance literature. First, the literature strongly agrees that performance indicators are organization-dependent and should be derived from an organization’s objectives, strategy, mission and vision. Secondly, consensus in the literature also exists regarding the need to combine financial and non-financial performance indicators. Nonetheless, disagreement still seems to exist in terms of whether objective and subjective indicators need to be combined, with objective indicators preferred by most advocates. Although subjective (or quasi-objective) indicators face challenges from bias, their use has some advantages; for instance, to include stakeholders in an assessment, to address latent constructs or to facilitate benchmarking when a fixed reference point is missing (Hubbard 2009 ; Richard et al. 2009 ). Moreover, empirical research has shown that subjective (or quasi-objective) indicators are more or less correlated with objective indicators, depending on the level of detail of the subjective question (Richard et al. 2009 ). For instance, a subjective question can be made more objective by using clear definitions or by selecting only well-informed respondents to reduce bias.

We conducted a structured literature review (SLR) to find papers dealing with performance measurement in the business process literature. SLR can be defined as “a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest” (Kitchenham 2007 : p. vi). An SLR is a meta study that identifies and summarizes evidence from earlier research (King and He 2005 ) or a way to address a potentially large number of identified sources based on a strict protocol used to search and appraise the literature (Boellt and Cecez-Kecmanovic 2015 ). It is systematic in the sense of a systematic approach to finding relevant papers and a systematic way of classifying the papers. Hence, according to Boellt and Cecez-Kecmanovic ( 2015 ), SLR as a specific type of literature review can only be used when two conditions are met. First, the topic should be well-specified and closely formulated (i.e., limited to performance measurement in the context of business processes) to potentially identify all relevant literature based on inclusion and exclusion criteria. Secondly, the research questions should be answered by extracting and aggregating evidence from the identified literature based on a high-level summary or bibliometric-type of content analysis. Furthermore, King and He ( 2005 ) also refer to a statistical analysis of existing literature.

Informed by the established guidelines proposed by Kitchenham ( 2007 ), we undertook the review in distinct stages: (1) formulating the research questions and the search strategy, (2) filtering and extracting data based on inclusion and exclusion criteria, and (3) synthesizing the findings. The remainder of this section describes the details of each stage.

Formulating the research questions and search strategy

A comprehensive and unbiased search is one of the fundamental factors that distinguish a systematic review from a traditional literature review (Kitchenham 2007 ). For this purpose, a systematic search begins with the identification of keywords and search terms that are derived from the research questions. Based on the research questions stipulated in the introduction, the SLR protocol (Boellt and Cecez-Kecmanovic 2015 ) for our study was defined, as shown in Table  2 .

Table 2

The structured literature review protocol for this study, based on Boellt and Cecez-Kecmanovic ( 2015 )

The ISI Web of Science (WoS) database was searched using predetermined search terms in November 2015. This database was selected because it is used by many universities and results in the most outstanding publications, thus increasing the quality of our findings. An important requirement was that the papers focus on “business process*” (BP). This keyword was used in combination with at least one of the following: (1) “performance indicator*”, (2) “performance metric*”, (3) “performance measur*”. All combinations of “keyword in topic” (TO) and “keyword in title” (TI) have been used.

Table  3 shows the degree to which the initial sample sizes varied, with 433 resulting papers for the most permissive search query (TOxTO) and 19 papers for the most restrictive one (TIxTI). The next stage started with the most permissive search query in an effort to select and assess as many relevant publications as possible.

Table 3

The number of papers in the web of science per search query (until November 2015)

Filtering and extracting data

Figure  3 summarizes the procedure for searching and selecting the literature to be reviewed. The list of papers found in the previous stage was filtered by deleting 35 duplicates, and the remaining 398 papers were further narrowed to 153 papers by evaluating their title and abstract. After screening the body of the texts, 76 full-text papers were considered relevant for our scope and constituted the final sample (“Appendix 1 ”).

Exclusion of papers and number of primary studies

More specifically, studies were excluded if their main focus was not business process performance measurement or if they did not refer to indicators, measures or metrics for business performance. The inclusion of studies was not restricted to any specific type of intervention or outcome. The SLR thus included all types of research studies that were written in English and published up to and including November 2015. Furthermore, publication by peer-reviewed publication outlets (e.g., journals or conference proceedings) was considered as a quality criterion to ensure the academic level of the research papers.

Synthesizing the findings

The analysis of the final sample was performed by means of narrative and descriptive analysis techniques. For RQ1, the 76 papers were analyzed on the basis of bibliometric data (e.g., publication type, publication year, geography) and general performance measurement issues by paying attention to the methodology and focus of the study. Details are provided in “Appendix 2 ”.

For RQ2, all the selected papers were screened to identify concrete performance indicators in order to generate a comprehensive list or checklist. The latter was done in different phases. In the first phase, the structured literature review allowed us to analyze which performance indicators are mainly used in the process literature and how they are concretized (e.g., in a question or mathematical formulation), resulting in an unstructured list of potential performance indicators. The indicators were also synthesized by combining similar indicators and rephrasing them into more generic terms.

The next phase was a comparative study to categorize the output of phase 1 into the commonly used measurement models in the performance literature (see “ Theoretical background ” section). For the purpose of this study, we specifically looked for those organizational performance models, mentioned in “ Theoretical background ” section, that are cited the most and that suggest categories, dimensions or performance perspectives that can be re-used (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ; Kueng 2000 ). Since the BSC (Kaplan and Norton 1996 , 2001 ) is the most commonly used of these measurement models, we began with the BSC as the overall framework to categorize the observed indicators related to business (process) performance, supplemented with an established view on process performance from the process literature (Dumas et al. 2013 ). Subsequently, a structured list of potential performance indicators was obtained.

In the third and final phase, an evaluation study was performed to validate whether the output of phase 2 is sufficiently comprehensive according to other performance measurement models, i.e., not included in our sample and differing from the most commonly used performance measurement models. Therefore, we investigated the degree to which our structured list covers the items in two variants or concretizations of the BSC. Hence, a validation by other theoretical models is provided. We note that a validation by subject-matter experts is out of scope for a structured literature review but relates to an opportunity for further research.

Results for RQ1

The final sample of 76 papers consists of 46 journal papers and 30 conference papers (Fig.  4 ), indicating a wide variety of outlets to reach the audience via operations and production-related journals in particular or in lower-ranked (Recker 2013 ) information systems journals.

The distribution of the sampled papers per publication type (N = 76)

When considering the chronological distribution of the sampled papers, Fig.  5 indicates an increase in the uptake of the topic in recent years, particularly for conference papers but also for journal publications since 2005.

The chronological distribution of the sampled papers per publication type (N = 76)

This uptake seems particularly situated in the Western world and Asia (Fig.  6 ). The countries with five or more papers in our sample are Germany (12 papers), the US (6 papers), Spain (5 papers), Croatia (5 papers) and China (5 papers). Figure  6 shows that business process performance measurement is a worldwide topic, with papers across the different continents. Nonetheless, a possible explanation for the higher coverage in the Western world could be due to its long tradition of measuring work (i.e., BSC origins).

The geographical distribution of the sampled papers per continent, based on a paper’s first author (N = 76)

The vast majority of the sampled papers address artifacts related to business (process) performance measurement. When looking at the research paradigm in which the papers are situated (Fig.  7 ), 71 % address design-science research, whereas 17 % conduct research in behavioral science and 12 % present a literature review. This could be another explanation for the increasing uptake in the Western world, as many design-science researchers are from Europe or North America (March and Smith 1995 ; Peffers et al. 2012 ).

The distribution of the sampled journal papers per research paradigm (N = 76)

Figure  8 supplements Fig.  7 by specifying the research methods used in the papers. For the behavioral-science papers, case studies and surveys are equally used. The 54 papers that are situated within the design-science paradigm explicitly refer to models, meta-models, frameworks, methods and/or tools. When mapping these 54 papers to the four artifact types of March and Smith ( 1995 ), the vast majority present (1) methods in the sense of steps to perform a task (e.g., algorithms or guidelines for performance measurement) and/or (2) models to describe solutions for the topic. The number of papers dealing with (3) constructs or a vocabulary and/or (4) instantiations or tools is much more limited, with 14 construct-related papers and 9 instantiations in our sample. We also looked at which evaluation methods, defined by Peffers et al. ( 2012 ), are typically used in the sampled design-science papers. While 7 of the 54 design-science papers do not seem to report on any evaluation effort, our sample confirms that most papers apply one or another evaluation method. Case studies and illustrative scenarios appear to be the most frequently used methods to evaluate design-science research on business (process) performance measurement.

The distribution of the sampled journal papers per research method (N = 76)

The sampled design-science research papers typically build and test performance measurement frameworks, systems or models or suggest meta-models and generic templates to integrate performance indicators into the process models of an organization. Such papers can focus on the process level, organizational level or even cross-organizational level. Nonetheless, the indicators mentioned in those papers are illustrative rather than comprehensive. An all-inclusive list of generic performance indicators seems to be missing. Some authors propose a set of indicators, but those indicators are specific to a certain domain or sector instead of being generic. For instance, Table  4 shows that 36 of the 76 sampled papers are dedicated to a specific domain or sector, such as technology-related aspects or supply chain management.

Table 4

The number of sampled papers dedicated to a specific domain or sector (N = 76)

Furthermore, the reviewed literature was analyzed with regard to its (1) scope, (2) functionalities, (3) terminology, and (4) foundations.

Starting with scope, it is observed that nearly two-thirds of the sampled papers can be categorized as dealing with process-oriented performance measurement, whereas one-third focuses more on general performance measurement and management issues. Nonetheless, most of the studies of process performance also include general performance measurement as a supporting concept. A minor cluster of eight research papers specifically focuses on business process reengineering and measurement systems to evaluate the results of reengineering efforts. Furthermore, other researchers focus on the measurement and assessment of interoperability issues and supply chain management measurements.

Secondly, while analyzing the literature, two groups of papers were identified based on their functionalities: (1) focusing on performance measurement systems or frameworks, and (2) focusing on certain performance indicators and their categorization. Regarding the first group, it should be mentioned that while the process of building or developing a performance measurement system (PMS) or framework is well-researched, only a small number of papers explicitly address process performance measurement systems (PPMS). The papers in this first group typically suggest concrete steps or stages to be followed by particular organizations or discuss the conceptual characteristics and design of a performance measurement system. Regarding the second group of performance indicators, we can differentiate two sub-groups. Some authors focus on the process of defining performance indicators by listing requirements or quality characteristics that an indicator should meet. However, many more authors are interested in integrating performance indicators into the process models or the whole architecture of an organization, and they suggest concrete solutions to do so. Compared to the first group of papers, this second group deals more with the categorization of performance indicators into domains (financial/non-financial, lag/lead, external/internal, BSC dimensions) or levels (strategic, tactical, operational).

Thirdly, regarding terminology, different terms are used by different authors to discuss performance measurement. Performance “indicator” is the most commonly used term among the reviewed papers. For instance, it is frequently used in reference to a key performance indicator (KPI), a KPI area or a performance indicator (PI). The concept of a process performance indicator (PPI) is also used, mainly in the process-oriented literature. Performance “measure” is another prevalent term in the papers. The least-used term is performance “metric” (i.e., in only nine papers). Although the concepts of performance indicators, measures and metrics are used interchangeably throughout most of the papers, the concepts are sometimes defined in different ways. For instance, paper 17 defines a performance indicator as a metric, and paper 49 defines a performance measure as an indicator. On the other hand, paper 7 defines a performance indicator as a set of measures. Yet another perspective is taken in paper 74, which defines a performance measure as “a description of something that can be directly measured (e.g., number of reworks per day)”, while defining a performance indicator as “a description of something that is calculated from performance measures (e.g., percentage reworks per day per direct employee” (p. 386). Inconsistencies exist not only in defining indicators but also in describing performance goals. For instance, some authors include a sign (e.g., minus or plus) or a verb (e.g., decrease or increase) in front of an indicator. Other authors attempt to describe performance goals in a SMART way—for instance, by including a time indication (e.g., “within a certain period”) and/or target (e.g., “5 % of all orders”)—whereas most of the authors are less precise. Hence, a great degree of ambiguity exists in the formulation of performance objectives among to the reviewed papers.

Finally, regarding the papers’ foundations, “ Performance measurement models ” section already indicated that the BSC plays an important role in the general literature on performance management systems (PMS), while Kueng ( 2000 ) also offers influential arguments on process performance measurement systems (PPMS). In our literature review, we observed that the BSC was mentioned in 43 of the 76 papers and that the results of 19 papers were mainly based on the BSC (Fig.  9 ). This finding provides additional evidence that the BSC can be considered the most frequently used performance model in academia as well. However, the measurement model of Kueng ( 2000 ) was also mentioned in the sampled papers on PPMS, though less frequently (i.e., in six papers).

The importance of the BSC according to the sampled papers (N = 76)

Interestingly, the BSC is also criticized by the sampled papers for not being comprehensive; for instance, due to the exclusion of environmental aspects, supply chain management aspects or cross-organizational processes. In response, some of the sampled papers also define sector-specific BSC indicators or suggest additional steps or indicators to make the process or business more sustainable (see Table  4 ). Nonetheless, the majority of the papers agree on the need for integrated and multidimensional measurement systems, such as the BSC, and on the importance of directly linking performance measurement to an organization’s strategy. However, while these papers mention the required link with strategy, the prioritization of indicators according to their strategic importance has been studied very little thus far.

Results for RQ2

For RQ2, the sampled papers were reviewed to distinguish papers with performance indicators from papers without performance indicators. A further distinction was made between indicators found with operationalization (i.e., concretization by means of a question or formula) and those without operationalization. We note that for many indicators, no operationalization was available. We discovered that only 30 of the 76 sampled papers contained some type of performance indicator (namely 3, 5, 6, 7, 11, 16, 17, 18, 20, 22, 26, 27, 30, 35, 37, 40, 43, 46, 49, 51, 52, 53, 55, 57, 58, 59, 60, 66, 71, 73). In total, approximately 380 individual indicators were found throughout all the sampled papers (including duplicates), which were combined based on similarities and modified to use more generic terms. This resulted in 87 indicators with operationalization (“Appendix 3 ”) and 48 indicators without operationalization (“Appendix 4 ”).

The 87 indicators with operationalization were then categorized according to the four perspectives of the BSC (i.e., financial, customer, business processes, and “learning and growth”) (Kaplan and Norton 1996 , 2001 ) and the four established dimensions of process performance (i.e., time, cost, quality, and flexibility) (Dumas et al. 2013 ). In particular, based in the identified indicators, we revealed 11 sub-perspectives within the initial BSC perspectives to better emphasize the focus of the indicators and the different target groups (Table  5 ): (1) financial performance for shareholders and top management, (2) customer-related performance, (3) supplier-related performance, (4) society-related performance, (5) general process performance, (6) time-related process performance, (7) cost-related process performance, (8) process performance related to internal quality, (9) flexibility-related process performance, (10) (digital) innovation performance, and (11) employee-related performance.

Table 5

A description of the observed performance perspectives, linked to the Balanced scorecard (Kaplan and Norton 1996 , 2001 )

For reasons of objectivity, the observed performance indicators were assigned to a single perspective starting from recognized frameworks (Kaplan and Norton 1996 , 2001 ; Dumas et al. 2013 ). Bias was further reduced by following the definitions of Table  5 . Furthermore, the authors of this article first classified the indicators individually and then reached consensus to obtain a more objective categorization.

Additional rationale for the identification of 11 performance perspectives is presented in Table  6 , which compares our observations with the perspectives adopted by the most commonly used performance measurement models (see “ Theoretical background ” section). This comparison allows us to highlight similarities and differences with other respected models. In particular, Table  6 shows that we did not observe a dedicated perspective for strategy (EFQM 2010 ) and that we did not differentiate between financial indicators and market indicators (Cross and Lynch 1988 ). Nonetheless, the similarities in Table  6 prevail. For instance, Cross and Lynch ( 1988 ) also acknowledge different process dimensions. Further, Kueng ( 2000 ) and the EFQM ( 2010 ) also differentiate employee performance from innovation performance, and they both add a separate perspective for results related to the entire society.

Table 6

The comparison of our observed performance perspectives with the perspectives taken in the most commonly used performance measurement models in the literature (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Kueng 2000 ; Cross and Lynch 1988 )

Figure  10 summarizes the number of performance indicators that we identified in the process literature per observed performance perspective. Not surprisingly, the initial BSC perspective of internal business process performance contains most of the performance indicators: 29 of 87 indicators. However, the other initial BSC perspectives are also covered by a relatively high number of indicators: 16 indicators for both financial performance and customer-related performance and 26 indicators for “learning and growth”. This result confirms the close link between process performance and organizational performance, as mentioned in the introduction.

The number of performance indicators with operationalization per performance perspective

A more detailed comparison of the perspectives provides interesting refinements to the state of the research. More specifically, Fig.  10 shows that five performance perspectives have more than ten indicators in the sample, indicating that academic research focuses more on financial performance for shareholders and top management and performance related to customers, process time, innovation and employees. On the other hand, fewer than five performance indicators were found in the sample for the perspectives related to suppliers, society, process costs and process flexibility, indicating that the literature focuses less on those perspectives. The latter remains largely overlooked by academic research, possibly due to the newly emerging character of these perspectives.

We must, however, note that the majority of the performance indicators are mentioned in only a few papers. For instance, 59 of the 87 indicators were cited in a single paper, whereas the remainder are mentioned in more than one paper. Eleven performance indicators are frequently mentioned in the process literature (i.e., by five or more papers). These indicators include four indicators of customer-related performance (i.e., customer complaints, perceived customer satisfaction, query time, and delivery reliability), three indicators of time-related process performance (i.e., process cycle time, sub-process turnaround time, and process waiting time), one cost-related performance indicator (i.e., process cost), two indicators of process performance related to internal quality (i.e., quality of internal outputs and deadline adherence), and one indicator of employee performance (i.e., perceived employee satisfaction).

Consistent with “ Performance indicators ” section, the different performance perspectives are a combination of financial or cost-related indicators with non-financial data. The latter also take the upper hand in our sample. Furthermore, the sample includes a combination of objective and subjective indicators, and the vast majority are objective indicators. Only eight indicators explicitly refer to qualitative scales; for instance, to measure the degree of satisfaction of the different stakeholder groups. For all the other performance indicators, a quantifiable alternative is provided.

It is important to remember that a distinction was made between the indicators with operationalization and those without operationalization. The list of 87 performance indicators, as given in “Appendix 3 ”, can thus be extended with those indicators for which operationalization is missing in the reviewed literature. Specifically, we found 48 additional performance indicators (“Appendix 4 ”) that mainly address supplier performance, process performance related to costs and flexibility, and the employee-related aspects of digital innovation. Consequently, this structured literature review uncovered a total of 135 performance indicators that are directly or indirectly linked to business process performance.

Finally, the total list of 135 performance indicators was evaluated for its comprehensiveness by comparing the identified indicators with other BSC variants that were not included in our sample. More specifically, based on a random search, we looked for two BSC variants in the Web of Science that did not fit the search strategy of this structured literature review: one that did not fit the search term of “business process*” (Hubbard 2009 ) and another that did not fit any of the performance-related search terms of “performance indicator*”, “performance metric*” or “performance measur*” (Bronzo et al. 2013 ). These two BSC variants cover 30 and 17 performance indicators, respectively, and are thus less comprehensive than the extended list presented in this study. Most of the performance indicators suggested by the two BSC variants are either directly covered in our findings or could be derived after recalculations. Only five performance indicators could not be linked to our list of 135 indicators, and these suggest possible refinements regarding (1) the growth potential of employees, (2) new markets, (3) the social performance of suppliers, (4) philanthropy, or (5) industry-specific events.

This structured literature review culminated in an extended list of 140 performance indicators: 87 indicators with operationalization, 48 indicators without operationalization and 5 refinements derived from two other BSC variants. The evaluation of our findings against two BSC variants validated our work in the sense that we present a more exhaustive list of performance indicators, with operationalization for most, and that only minor refinements could be added. However, the comprehensiveness of our findings can be claimed only to a certain extent given the limitations of our predefined search strategy and the lack of empirical validation by subject-matter experts or organizations. Notwithstanding these limitations, conclusions can be drawn from the large sample of 76 papers to respond to the research questions (RQs).

Regarding RQ1 on the state of the research on business process performance measurement, the literature review provided additional evidence for the omnipresence of the BSC. Most of the sampled papers mentioned or used the BSC as a starting point and basis for their research and analysis. The literature study also showed a variety of research topics, ranging from behavioral-science to design-science research and from a focus on performance measurement models to a focus on performance indicators. In addition to inconsistencies in the terminology used to describe performance indicators and targets, the main weakness uncovered in this literature review deals with the concretization of performance indicators supplementing performance measurement systems. The SLR results suggest that none of the reviewed papers offers a comprehensive measurement framework, specifically one that includes and extends the BSC perspectives, is process-driven and encompasses as many concrete performance indicators as possible. Such a comprehensive framework could be used as a checklist or a best practice for reference when defining specific performance indicators. Hence, the current literature review offers a first step towards such a comprehensive framework by means of an extended list of possible performance indicators bundled in 11 performance perspectives (RQ2).

Regarding RQ2 on process performance indicators, the literature study revealed that scholars measure performance in many different ways and without sharing much detail regarding the operationalization of the measurement instruments, which makes a comparison of research results more difficult. As such, the extended list of performance indicators is our main contribution and fills a gap in the literature by providing a detailed overview of performance indicators mentioned or used in the literature on business process performance. Another novel aspect is that we responded to the criticism of missing perspectives in the original BSC (EFQM 2010 ; Hubbard 2009 ; Kueng 2000 ) and identified the narrow view of performance typically taken in the process literature (Dumas et al. 2013 ). Figures  1 and ​ and2 2 are now combined and extended in a more exhaustive way, namely by means of more perspectives than are offered by other attempts (Table  6 ), by explicitly differentiating between performance drivers (or lead indicators) and performance outcomes (or lag indicators), and by considering concrete performance indicators.

Our work also demonstrated that all perspectives in the BSC (Kaplan and Norton 1996 , 2001 ) relate to business process performance to some degree. In other words, while the BSC is a strategic tool for organizational performance measurement, it is actually based on indicators that originate from business processes. More specifically, in addition to the perspective of internal business processes, the financial performance perspective typically refers to sales or revenues gained while doing business, particularly after executing business processes. The customer perspective relates to the implications of product or service delivery, specifically to the interactions throughout business processes, whereas the “learning and growth” perspective relates to innovations in the way of working (i.e., business processes) and the degree to which employees are prepared to conduct and innovate business processes. The BSC, however, does not present sub-perspectives and thus takes a more high-level view of performance. Hence, the BSC can be extended based on other categorizations made in the reviewed literature; for instance, related to internal/external, strategic/operational, financial/non-financial, or cost/time/quality/flexibility.

Therefore, this study refined the initial BSC perspectives into eleven performance perspectives (Fig.  11 ) by applying three other performance measurement models (Cross and Lynch 1988 ; EFQM 2010 ; Kueng 2000 ) and the respected Devil’s quadrangle for process performance (Dumas et al. 2013 ). Additionally, a more holistic view of business process performance can be obtained by measuring each performance perspective of Fig.  11 than can be achieved by using the established dimensions of time, cost, quality and flexibility as commonly proposed in the process literature (Dumas et al. 2013 ). As such, this study demonstrated a highly relevant synergy between the disciplines of process management, organization management and performance management.

An overview of the observed performance perspectives in the business process literature

We also found out that not all the performance perspectives in Fig.  11 are equally represented in the studied literature. In particular, the perspectives related to suppliers, society, process costs and process flexibility seem under-researched thus far.

The eleven performance perspectives (Fig.  11 ) can be used by organizations and scholars to measure the performance of business processes in a more holistic way, considering the implications for different target groups. For each perspective, performance indicators can be selected that fit particular needs. Thus, we do not assert that every indicator in the extended list of 140 performance indicators should always be measured, since “ Theoretical background ” section emphasized the need for organization-dependent indicators aligned with an organization’s strategy. Instead, our extended list can be a starting point for finding and using appropriate indicators for each performance perspective, without losing much time reflecting on possible indicators or ways to concretize those indicators. Similarly, the list can be used by scholars, since many studies in both the process literature and management literature intend to measure the performance outcomes of theoretical constructs or developed artifacts.

Consistent with the above, we acknowledge that the observed performance indicators originate from different models and paradigms or can be specific to certain processes or sectors. Since our intention is to provide an exhaustive list of indicators that can be applied to measure business process performance, the indicators are not necessarily fully compatible. Instead, our findings allow the recognition of the role of a business context (i.e., the peculiarities of a business activity, an organization or other circumstances). For instance, a manufacturing organization might choose different indicators from our list than a service or non-profit organization (e.g., manufacturing lead time versus friendliness, or carbon dioxide emission versus stakeholder satisfaction).

Another point of discussion is dedicated to the difference between the performance of specific processes (known as “process performance”) and the performance of the entire process portfolio (also called “BPM performance”). While some indicators in our extended list clearly go beyond a single process (e.g., competence-related indicators or employee absenteeism), it is our opinion that the actual performance of multiple processes can be aggregated to obtain BPM performance (e.g., the sum of process waiting times). This distinction between (actual) process performance and BPM performance is useful; for instance, for supplementing models that try to predict the (expected) performance based on capability development, such as process maturity models (e.g., CMMI) and BPM maturity models (Hammer 2007 ; McCormack and Johnson 2001 ). Nonetheless, since this study has shown a close link between process performance, BPM performance, and organizational performance, it seems better to refer to different performance perspectives than to differentiate between such performance types.

In future research, the comprehensiveness of the extended list of performance indicators can be empirically validated by subject-matter experts. Additionally, case studies can be conducted in which organizations apply the list as a supplement to performance measurement models in order to facilitate the selection of indicators for their specific business context. The least covered perspectives in the academic research also seem to be those that are newly emerging (namely, the perspectives related to close collaboration with suppliers, society/sustainability and process flexibility or agility), and these need more attention in future research. Another research avenue is to elaborate on the notion of a business context; for instance, by investigating what it means to have a strategic fit (Venkatraman 1989 ) in terms of performance measurement and which strategies (Miller and Friesen 1986 ; Porter 2008 ; Treacy and Wiersema 1993 ) are typically associated with which performance indicators. Additionally, the impact of environmental aspects, such as market velocity (Eisenhardt and Martin 2000 ), on the choice of performance indicators can be taken into account in future research.

Business quotes such as “If you cannot measure it, you cannot manage it” or “What is measured improves” (P. Drucker) are sometimes criticized because not all important things seem measurable (Ryan 2014 ). Nonetheless, given the perceived need of managers to measure their business and the wide variety of performance indicators (i.e., ranging from quantitative to qualitative and from financial to non-financial), this structured literature review has presented the status of the research on business process performance measurement. This structured approach allowed us to detect weaknesses or inadequacies in the current literature, particularly regarding the definition and concretization of possible performance indicators. We continued by taking a holistic view of the categorization of the observed performance indicators (i.e., measures or metrics) into 11 performance perspectives based on relevant performance measurement models and established process performance dimensions.

The identified performance indicators within the 11 perspectives constitute an extended list from which practitioners and researchers can select appropriate indicators depending on their needs. In total, the structured literature review resulted in 140 possible performance indicators: 87 indicators with operationalization, 48 additional indicators that need further concretization, and 5 refinements based on other Balanced Scorecard (BSC) variants. As such, the 11 performance perspectives with related indicators can be considered a conceptual framework that was derived from the current process literature and theoretically validated by established measurement approaches in organization management.

Future research can empirically validate the conceptual framework by involving subject-matter experts to assess the comprehensiveness of the extended list and refine the missing concretizations, and by undertaking case studies in which the extended list can be applied by specific organizations. Other research avenues exist to investigate the link between actual process performance and expected process performance (as measured in maturity models) or the impact of certain strategic or environmental aspects on the choice of specific performance indicators. Such findings are needed to supplement and enrich existing performance measurement systems.

Authors’ contributions

AVL initiated the conception and design of the study, while AS was responsible for the collection of data (sampling) and identification of performance indicators. The analysis and interpretation of the data was conducted by both authors. AVL was involved in drafting and coordinating the manuscript, and AS in reviewing it critically. Both authors read and approved the final manuscript.

Acknowledgements

We thank American Journal Experts (AJE) for English language editing.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Consent for publication.

Not applicable.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Abbreviations

See Table  7 .

Table 7

The final list of sampled papers (N = 76)

Appendix 2: The mapping of the structured literature review

The mapping details per sampled paper can be found here.

https://drive.google.com/file/d/0B_2VpjwsRLrlRHhfRHJ4ZFBWdEE/view?usp=sharing .

See Table  8 .

Table 8

The list of performance indicators with operationalization

See Table  9 .

Table 9

Additional list of performance indicators without operationalization

Contributor Information

Amy Van Looy, Phone: +32 9 264 95 36, Email: [email protected] .

Aygun Shafagatova, Email: [email protected] .

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• DOI: 10.34069/ai/2023.63.03.1
• Corpus ID: 258898259

The impact of debt and equity decisions on business performance: Evidence from International Airline Corporation

• Qaiser Aman , Sultan Altass
• Published in Revista Amazonía investiga 30 April 2023
• Business, Economics

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Build a Corporate Culture That Works

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their culture in such a way that the words become an organizational reality that molds employee behavior as intended.

All too often a culture is described as a set of anodyne norms, principles, or values, which do not offer decision-makers guidance on how to make difficult choices when faced with conflicting but equally defensible courses of action.

The trick to making a desired culture come alive is to debate and articulate it using dilemmas. If you identify the tough dilemmas your employees routinely face and clearly state how they should be resolved—“In this company, when we come across this dilemma, we turn left”—then your desired culture will take root and influence the behavior of the team.

To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value statement.

Start by thinking about the dilemmas your people will face.

Idea in Brief

The problem.

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their corporate culture in such a way that the words become an organizational reality that molds employee behavior as intended.

What Usually Happens

How to fix it.

Follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value.

At the beginning of my career, I worked for the health-care-software specialist HBOC. One day, a woman from human resources came into the cafeteria with a roll of tape and began sticking posters on the walls. They proclaimed in royal blue the company’s values: “Transparency, Respect, Integrity, Honesty.” The next day we received wallet-sized plastic cards with the same words and were asked to memorize them so that we could incorporate them into our actions. The following year, when management was indicted on 17 counts of conspiracy and fraud, we learned what the company’s values really were.

• EM Erin Meyer is a professor at INSEAD, where she directs the executive education program Leading Across Borders and Cultures. She is the author of The Culture Map: Breaking Through the Invisible Boundaries of Global Business (PublicAffairs, 2014) and coauthor (with Reed Hastings) of No Rules Rules: Netflix and the Culture of Reinvention (Penguin, 2020). ErinMeyerINSEAD

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• 07 May 2024
• Cold Call Podcast

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Ferran Adrià, chef at legendary Barcelona-based restaurant elBulli, was facing two related decisions. First, he and his team must continue to develop new and different dishes for elBulli to guarantee a continuous stream of innovation, the cornerstone of the restaurant's success. But they also need to focus on growing the restaurant’s business. Can the team balance both objectives? Professor Michael I. Norton discusses the connections between creativity, emotions, rituals, and innovation – and how they can be applied to other domains – in the case, “elBulli: The Taste of Innovation,” and his new book, The Ritual Effect.

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Delivering through diversity

Awareness of the business case for inclusion and diversity is on the rise. While social justice typically is the initial impetus behind these efforts, companies have increasingly begun to regard inclusion and diversity as a source of competitive advantage, and specifically as a key enabler of growth. Yet progress on diversification initiatives has been slow. And companies are still uncertain about how they can most effectively use diversity and inclusion to support their growth and value-creation goals.

Our latest study of diversity in the workplace, Delivering through diversity , reaffirms the global relevance of the link between diversity—defined as a greater proportion of women and a more mixed ethnic and cultural composition in the leadership of large companies—and company financial outperformance. The new analysis expands on our 2015 report, Why diversity matters , by drawing on an enlarged data set of more than 1,000 companies covering 12 countries, measuring not only profitability (in terms of earnings before interest and taxes, or EBIT) but also longer-term value creation (or economic profit), exploring diversity at different levels of the organization, considering a broader understanding of diversity (beyond gender and ethnicity), and providing insight into best practices.

Diversity and financial performance in 2017

In the original research, using 2014 diversity data, we found that companies in the top quartile for gender diversity on their executive teams were 15 percent more likely to experience above-average profitability than companies in the fourth quartile. In our expanded 2017 data set this number rose to 21 percent and continued to be statistically significant. For ethnic and cultural diversity, the 2014 finding was a 35 percent likelihood of outperformance, comparable to the 2017 finding of a 33 percent likelihood of outperformance on EBIT margin; both were also statistically significant (Exhibit 1).

Several other findings on gender diversity, ethnic diversity, and diversity around the world are also interesting.

Gender diversity

Gender diversity is correlated with both profitability and value creation. In our 2017 data set, we found a positive correlation between gender diversity on executive teams and both our measures of financial performance: top-quartile companies on executive-level gender diversity worldwide had a 21 percent likelihood of outperforming their fourth-quartile industry peers on EBIT margin, and they also had a 27 percent likelihood of outperforming fourth-quartile peers on longer-term value creation, as measured using an economic-profit (EP) margin (Exhibit 2).

For gender, the executive team shows the strongest correlation. We found that having gender diversity on executive teams, specifically, to be consistently positively correlated with higher profitability across geographies in our data set, underpinning the role that executive teams—where the bulk of strategic and operational decisions are made—play in the financial performance of a company.

Executive teams of outperforming companies have more women in line roles versus staff roles. We tested the hypothesis that having more women executives in line roles (typically revenue generating) is more closely correlated with financial outperformance. We know from research, such as our Women in the Workplace 2017 report, that women are underrepresented in line roles. In our data set, this holds true even for top-quartile gender-diverse companies experiencing above-average financial performance. Yet these top-quartile companies also have a greater proportion of women in line roles than do their fourth-quartile peers: 10 percent versus 1 percent of total executives, respectively (Exhibit 3).

Ethnic and cultural diversity

Top-team ethnic and cultural diversity is correlated with profitability. In our 2017 data set, we looked at racial and cultural diversity in six countries where the definition of ethnic diversity was consistent and our data were reliable. 1 1. Brazil, Mexico, Singapore, South Africa, United Kingdom, and United States. As in 2014, we found that companies with the most ethnically diverse executive teams—not only with respect to absolute representation but also of variety or mix of ethnicities 2 2. We measure ethnic diversity using the normalized Herfindahl-Hirschman Index (NHHI). —are 33 percent more likely to outperform their peers on profitability. That’s comparable to the 35 percent outperformance reported in 2014, with both figures being statistically significant (Exhibit 4).

The penalty for not being diverse on both measures persists. Now, as previously, companies in the fourth quartile on both gender and ethnic diversity are more likely to underperform their industry peers on profitability: 29 percent in our 2017 data set.

Ethnic and cultural diversity on executive teams is low. We focused on our US and UK data sets to examine ethnically and culturally diverse representation among US and UK companies, considering the pipeline starting with university graduates. Black Americans comprise 10 percent of US graduates but hold only 4 percent of senior-executive positions, Hispanics and Latinos comprise 8 percent of graduates versus 4 percent of executives, and for Asian Americans, the numbers are 7 percent of graduates versus 5 percent of executives. In the United Kingdom, the disparity is even greater: 22 percent of university students identify as black and minority ethnic, yet only 8 percent of UK executives in our sample do.

Black women executives are underrepresented in line roles and may face a harder path to CEO. As discussed, within our US and UK data sets, overall representation of women on executive teams shows an apparent bias toward staff roles. Among our US sample, not only do women hold a disproportionately small share of line roles on executive teams but also women of color (including Asian, black, and Latina women) hold an even smaller share.

Line roles versus staff roles on executive teams tend to differ in their ability to propel individuals to the CEO position, with line roles the more likely incubators of future CEOs. In our US sample, black female executives, specifically, are more than twice as likely to be in staff roles than in line roles, and our sample denotes an absence of black female CEOs. Other studies have found that black women suffer a double burden of bias that keeps them from the uppermost levels of corporate leadership. Underrepresentation on executive teams in general, and in line roles in particular, could be an important piece of this story.

Diversity around the world

The correlation between gender and ethnic diversity and financial performance generally hold true across geographies, though with some variations in certain regions. Our data yielded some noteworthy findings concerning the country-level differences in executive-team diversity:

Australian companies lead the way when it comes to the women’s share of executive roles (21 percent). The share in the United States is 19 percent and in the United Kingdom is 15 percent. The same holds true for board positions, with Australian companies at 30 percent, US companies at 26 percent, and UK companies at 22 percent—and for women at the whole company level. The disparity among these countries is interesting, given that women’s participation in the workforce is similar in all three and given that they dominate among top performers, representing 47 percent of the data set but more than 70 percent of the top-quartile companies.

The picture on ethnic and cultural diversity on executive teams is nuanced. Among our sample, South Africa has the highest levels of diverse representation on executive teams, with 16 percent of executive positions held by blacks. However, this must be understood in the context of local demographics: South Africa’s population is 79 percent black, but among large corporations, the impact of South Africa’s complicated social history means that the large majority of global and national corporate entities are led by white executives (69 percent in our sample). As our work considers the local context with respect to ethnicity, we therefore evaluated South Africa’s diversity from this perspective, defining black South Africans as the minority. Singapore, the United Kingdom, and the United States follow South Africa with 11 to 12 percent of ethnically diverse executives.

When considering ethnic-minority representation in the broader population, British executive teams seem closer to achieving a “fair share.” This, however, masks huge variations within the UK data set, in which a large proportion of companies have no ethnic minorities on their executive teams (or boards) and a handful of companies have particularly international executive teams. Ethnically diverse representation on UK and US executive teams increased by an average of six and five percentage points, respectively, since 2014. However, this was offset by declines in other geographies, leading to an overall lower increase of one percentage point across regions (Exhibit 5).

Delivering impact through diversity

Our research confirms that gender, ethnic, and cultural diversity, particularly within executive teams, continue to be correlated to financial performance across multiple countries worldwide. In our 2015 report, our hypotheses about what drives this correlation were that more diverse companies are better able to attract top talent ; to improve their customer orientation , employee satisfaction, and decision making ; and to secure their license to operate — all of which we believe continue to be relevant.

Companies report that materially improving the representation of diverse talent within their ranks, as well as effectively utilizing inclusion and diversity as an enabler of business impact, are particularly challenging goals. Despite this, multiple companies worldwide have succeeded in making sizable improvements to inclusion and diversity across their organizations, and they have been reaping tangible benefits for their efforts.

We found that these companies all developed inclusion and diversity (I&D) strategies that reflected their business ethos and priorities, ones that they were strongly committed to. Four imperatives emerged as being crucial:

Articulate and cascade CEO commitment to galvanize the organization. Companies increasingly recognize that commitment to inclusion and diversity starts at the top, with many companies publicly committing to an I&D agenda. Leading companies go further, cascading this commitment throughout their organizations, particularly to middle management. They promote ownership by their core businesses, encourage role modeling, hold their executives and managers to account, and ensure efforts are sufficiently resourced and supported centrally.

Define inclusion and diversity priorities that are based on the drivers of the business-growth strategy. Top-performing companies invest in internal research to understand which specific strategies best support their business-growth priorities. Such strategies include attracting and retaining the right talent and strengthening decision-making capabilities. Leading companies also identify the mix of inherent traits (such as ethnicity) and acquired traits (such as educational background and experience) that are most relevant for their organization, using advanced business and people analytics .

Craft a targeted portfolio of inclusion and diversity initiatives to transform the organization. Leading companies use targeted thinking to prioritize the I&D initiatives in which they invest, and they ensure there is alignment with the overall growth strategy. They recognize the necessity of building an inclusive organizational culture, and they use a combination of “hard” and “soft” wiring to create a coherent narrative and program that resonates with employees and stakeholders, helping to drive sustainable change.

Tailor the strategy to maximize local impact. Top and rapidly improving companies recognize the need to adapt their approach — to different parts of the business, to various geographies, and to sociocultural contexts.

Paying rigorous attention to all four imperatives (Exhibit 6) helps to ensure that inclusion and diversity will support a company’s growth agenda. In our experience, companies tend to fall short on leadership accountability for meeting goals, on building the business case, and on the coherence and prioritization of the resulting action plan.

It is worth noting that while progress on representation can be brought about relatively rapidly with the right set of initiatives, embedding inclusion within the organization can take many years and often requires action outside the organization. Companies that do this well can create a strong corporate ethos that resonates across employee, customer, supplier, investor, and broader stakeholder groups.

This work sheds light on how companies can use inclusion and diversity as an enabler of business impact. It is important to note, however, that correlation does not demonstrate causality, which would be challenging to demonstrate. While not causal, we observe a real relationship between diversity and performance that has persisted over time and scale, and across geographies. There are clear and compelling hypotheses for why this relationship persists including improved access to talent, enhanced decision making and depth of consumer insight and strengthened employee engagement and license to operate. We encourage businesses to examine the case for inclusion and diversity at a more granular level to craft an approach that is tailored to their business, learning from leading diverse companies around the world as to ways to do this with high impact.

The business case for diversity continues to be compelling and to have global relevance. There’s an opportunity for promoting diversity in senior decision-making roles, and specifically in line roles on executive teams. Although levels of diverse representation in top teams are still highly variable globally—with progress being slow overall—there are practical lessons from successful companies that have made inclusion and diversity work. Creating an effective inclusion and diversity strategy is no small effort and requires strong, sustained, and inclusive leadership. But we, and many of the companies we studied, believe the potential benefits of stronger business performance are well worth it.

Download Delivering through diversity , the full report on which this article is based (PDF—7 MB).

Vivian Hunt is a senior partner in McKinsey’s London office , where Sundiatu Dixon-Fyle is a senior expert; Sara Prince is a partner in the Atlanta office ; Lareina Yee is a senior partner in the San Francisco office .

The authors wish to thank Treina Fabré and Saif Hameed for their contributions to this report.

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B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024 [Research]

• by Stephanie Stahl
• | Published: October 18, 2023
• | Trends and Research

Creating standards, guidelines, processes, and workflows for content marketing is not the sexiest job.

But setting standards is the only way to know if you can improve anything (with AI or anything else).

Here’s the good news: All that non-sexy work frees time and resources (human and tech) you can apply to bring your brand’s strategies and plans to life.  

But in many organizations, content still isn’t treated as a coordinated business function. That’s one of the big takeaways from our latest research, B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024, conducted with MarketingProfs and sponsored by Brightspot .

A few symptoms of that reality showed up in the research:

• Marketers cite a lack of resources as a top situational challenge, the same as they did the previous year.
• Nearly three-quarters (72%) say they use generative AI, but 61% say their organization lacks guidelines for its use.
• The most frequently cited challenges include creating the right content, creating content consistently, and differentiating content.

I’ll walk you through the findings and share some advice from CMI Chief Strategy Advisor Robert Rose and other industry voices to shed light on what it all means for B2B marketers. There’s a lot to work through, so feel free to use the table of contents to navigate to the sections that most interest you.

Note: These numbers come from a July 2023 survey of marketers around the globe. We received 1,080 responses. This article focuses on answers from the 894 B2B respondents.

Table of contents

• Team structure
• Content marketing challenges

Content types, distribution channels, and paid channels

• Social media

Content management and operations

• Measurement and goals
• Overall success
• Budgets and spending
• Top content-related priorities for 2024
• Content marketing trends for 2024

Action steps

Methodology, ai: 3 out of 4 b2b marketers use generative tools.

Of course, we asked respondents how they use generative AI in content and marketing. As it turns out, most experiment with it: 72% of respondents say they use generative AI tools.

But a lack of standards can get in the way.

“Generative AI is the new, disruptive capability entering the realm of content marketing in 2024,” Robert says. “It’s just another way to make our content process more efficient and effective. But it can’t do either until you establish a standard to define its value. Until then, it’s yet just another technology that may or may not make you better at what you do.”

So, how do content marketers use the tools today? About half (51%) use generative AI to brainstorm new topics. Many use the tools to research headlines and keywords (45%) and write drafts (45%). Fewer say they use AI to outline assignments (23%), proofread (20%), generate graphics (11%), and create audio (5%) and video (5%).

Some marketers say they use AI to do things like generate email headlines and email copy, extract social media posts from long-form content, condense long-form copy into short form, etc.

Only 28% say they don’t use generative AI tools.

Most don’t pay for generative AI tools (yet)

Among those who use generative AI tools, 91% use free tools (e.g., ChatGPT ). Thirty-eight percent use tools embedded in their content creation/management systems, and 27% pay for tools such as Writer and Jasper.

AI in content remains mostly ungoverned

Asked if their organizations have guidelines for using generative AI tools, 31% say yes, 61% say no, and 8% are unsure.

We asked Ann Handley , chief content officer of MarketingProfs, for her perspective. “It feels crazy … 61% have no guidelines? But is it actually shocking and crazy? No. It is not. Most of us are just getting going with generative AI. That means there is a clear and rich opportunity to lead from where you sit,” she says.

“Ignite the conversation internally. Press upon your colleagues and your leadership that this isn’t a technology opportunity. It’s also a people and operational challenge in need of thoughtful and intelligent response. You can be the AI leader your organization needs,” Ann says.

Why some marketers don’t use generative AI tools

While a lack of guidelines may deter some B2B marketers from using generative AI tools, other reasons include accuracy concerns (36%), lack of training (27%), and lack of understanding (27%). Twenty-two percent cite copyright concerns, and 19% have corporate mandates not to use them.

How AI is changing SEO

We also wondered how AI’s integration in search engines shifts content marketers’ SEO strategy. Here’s what we found:

• 31% are sharpening their focus on user intent/answering questions.
• 27% are creating more thought leadership content.
• 22% are creating more conversational content.

Over one-fourth (28%) say they’re not doing any of those things, while 26% say they’re unsure.

AI may heighten the need to rethink your SEO strategy. But it’s not the only reason to do so, as Orbit Media Studios co-founder and chief marketing officer Andy Crestodina points out: “Featured snippets and people-also-ask boxes have chipped away at click-through rates for years,” he says. “AI will make that even worse … but only for information intent queries . Searchers who want quick answers really don’t want to visit websites.

“Focus your SEO efforts on those big questions with big answers – and on the commercial intent queries,” Andy continues. “Those phrases still have ‘visit website intent’ … and will for years to come.”

Will the AI obsession ever end?

Many B2B marketers surveyed predict AI will dominate the discussions of content marketing trends in 2024. As one respondent says: “AI will continue to be the shiny thing through 2024 until marketers realize the dedication required to develop prompts, go through the iterative process, and fact-check output . AI can help you sharpen your skills, but it isn’t a replacement solution for B2B marketing.”

Back to table of contents

Team structure: How does the work get done?

Generative AI isn’t the only issue affecting content marketing these days. We also asked marketers about how they organize their teams .

Among larger companies (100-plus employees), half say content requests go through a centralized content team. Others say each department/brand produces its own content (23%), and the departments/brand/products share responsibility (21%).

Content strategies integrate with marketing, comms, and sales

Seventy percent say their organizations integrate content strategy into the overall marketing sales/communication/strategy, and 2% say it’s integrated into another strategy. Eleven percent say content is a stand-alone strategy for content used for marketing, and 6% say it’s a stand-alone strategy for all content produced by the company. Only 9% say they don’t have a content strategy. The remaining 2% say other or are unsure.

Employee churn means new teammates; content teams experience enlightened leadership

Twenty-eight percent of B2B marketers say team members resigned in the last year, 20% say team members were laid off, and about half (49%) say they had new team members acclimating to their ways of working.

While team members come and go, the understanding of content doesn’t. Over half (54%) strongly agree, and 30% somewhat agree the leader to whom their content team reports understands the work they do. Only 11% disagree. The remaining 5% neither agree nor disagree.

And remote work seems well-tolerated: Only 20% say collaboration was challenging due to remote or hybrid work.

Content marketing challenges: Focus shifts to creating the right content

We asked B2B marketers about both content creation and non-creation challenges.

Content creation

Most marketers (57%) cite creating the right content for their audience as a challenge. This is a change from many years when “creating enough content” was the most frequently cited challenge.

One respondent points out why understanding what audiences want is more important than ever: “As the internet gets noisier and AI makes it incredibly easy to create listicles and content that copy each other, there will be a need for companies to stand out. At the same time, as … millennials and Gen Z [grow in the workforce], we’ll begin to see B2B become more entertaining and less boring. We were never only competing with other B2B content. We’ve always been competing for attention.”

Other content creation challenges include creating it consistently (54%) and differentiating it (54%). Close to half (45%) cite optimizing for search and creating quality content (44%). About a third (34%) cite creating enough content to keep up with internal demand, 30% say creating enough content to keep up with external demand, and 30% say creating content that requires technical skills.

Other hurdles

The most frequently cited non-creation challenge, by far, is a lack of resources (58%), followed by aligning content with the buyer’s journey (48%) and aligning content efforts across sales and marketing (45%). Forty-one percent say they have issues with workflow/content approval, and 39% say they have difficulty accessing subject matter experts. Thirty-four percent say it is difficult to keep up with new technologies/tools (e.g., AI). Only 25% cite a lack of strategy as a challenge, 19% say keeping up with privacy rules, and 15% point to tech integration issues.

We asked content marketers about the types of content they produce, their distribution channels , and paid content promotion. We also asked which formats and channels produce the best results.

Popular content types and formats

As in the previous year, the three most popular content types/formats are short articles/posts (94%, up from 89% last year), videos (84%, up from 75% last year), and case studies/customer stories (78%, up from 67% last year). Almost three-quarters (71%) use long articles, 60% produce visual content, and 59% craft thought leadership e-books or white papers. Less than half of marketers use brochures (49%), product or technical data sheets (45%), research reports (36%), interactive content (33%), audio (29%), and livestreaming (25%).

Effective content types and formats

Which formats are most effective? Fifty-three percent say case studies/customer stories and videos deliver some of their best results. Almost as many (51%) names thought leadership e-books or white papers, 47% short articles, and 43% research reports.

Popular content distribution channels

Regarding the channels used to distribute content, 90% use social media platforms (organic), followed by blogs (79%), email newsletters (73%), email (66%), in-person events (56%), and webinars (56%).

Channels used by the minority of those surveyed include:

• Digital events (44%)
• Podcasts (30%)
• Microsites (29%)
• Digital magazines (21%)
• Branded online communities (19%)
• Hybrid events (18%)
• Print magazines (16%)
• Online learning platforms (15%)
• Mobile apps (8%)
• Separate content brands (5%)

Effective content distribution channels

Which channels perform the best? Most marketers in the survey point to in-person events (56%) and webinars (51%) as producing better results. Email (44%), organic social media platforms (44%), blogs (40%) and email newsletters (39%) round out the list.

Popular paid content channels

When marketers pay to promote content , which channels do they invest in? Eighty-six percent use paid content distribution channels.

Of those, 78% use social media advertising/promoted posts, 65% use sponsorships, 64% use search engine marketing (SEM)/pay-per-click, and 59% use digital display advertising. Far fewer invest in native advertising (35%), partner emails (29%), and print display ads (21%).

Effective paid content channels

SEM/pay-per-click produces good results, according to 62% of those surveyed. Half of those who use paid channels say social media advertising/promoted posts produce good results, followed by sponsorships (49%), partner emails (36%), and digital display advertising (34%).

Social media use: One platform rises way above

When asked which organic social media platforms deliver the best value for their organization, B2B marketers picked LinkedIn by far (84%). Only 29% cite Facebook as a top performer, 22% say YouTube, and 21% say Instagram. Twitter and TikTok see 8% and 3%, respectively.

So it makes sense that 72% say they increased their use of LinkedIn over the last 12 months, while only 32% boosted their YouTube presence, 31% increased Instagram use, 22% grew their Facebook presence, and 10% increased X and TikTok use.

Which platforms are marketers giving up? Did you guess X? You’re right – 32% of marketers say they decreased their X use last year. Twenty percent decreased their use of Facebook, with 10% decreasing on Instagram, 9% pulling back on YouTube, and only 2% decreasing their use of LinkedIn.

Interestingly, we saw a significant rise in B2B marketers who use TikTok: 19% say they use the platform – more than double from last year.

To explore how teams manage content, we asked marketers about their technology use and investments and the challenges they face when scaling their content .

Content management technology

When asked which technologies they use to manage content, marketers point to:

• Analytics tools (81%)
• Social media publishing/analytics (72%)
• Email marketing software (69%)
• Content creation/calendaring/collaboration/workflow (64%)
• Content management system (50%)
• Customer relationship management system (48%)

But having technology doesn’t mean it’s the right technology (or that its capabilities are used). So, we asked if they felt their organization had the right technology to manage content across the organization.

Only 31% say yes. Thirty percent say they have the technology but aren’t using its potential, and 29% say they haven’t acquired the right technology. Ten percent are unsure.

Content tech spending will likely rise

Even so, investment in content management technology seems likely in 2024: 45% say their organization is likely to invest in new technology, whereas 32% say their organization is unlikely to do so. Twenty-three percent say their organization is neither likely nor unlikely to invest.

Scaling content production

We introduced a new question this year to understand what challenges B2B marketers face while scaling content production .

Almost half (48%) say it’s “not enough content repurposing.” Lack of communication across organizational silos is a problem for 40%. Thirty-one percent say they have no structured content production process, and 29% say they lack an editorial calendar with clear deadlines. Ten percent say scaling is not a current focus.

Among the other hurdles – difficulty locating digital content assets (16%), technology issues (15%), translation/localization issues (12%), and no style guide (11%).

For those struggling with content repurposing, content standardization is critical. “Content reuse is the only way to deliver content at scale. There’s just no other way,” says Regina Lynn Preciado , senior director of content strategy solutions at Content Rules Inc.

“Even if you’re not trying to provide the most personalized experience ever or dominate the metaverse with your omnichannel presence, you absolutely must reuse content if you are going to deliver content effectively,” she says.

“How to achieve content reuse ? You’ve probably heard that you need to move to modular, structured content. However, just chunking your content into smaller components doesn’t go far enough. For content to flow together seamlessly wherever you reuse it, you’ve got to standardize your content. That’s the personalization paradox right there. To personalize, you must standardize.

“Once you have your content standards in place and everyone is creating content in alignment with those standards, there is no limit to what you can do with the content,” Regina explains.

Why do content marketers – who are skilled communicators – struggle with cross-silo communication? Standards and alignment come into play.

“I think in the rush to all the things, we run out of time to address scalable processes that will fix those painful silos, including taking time to align on goals, roles and responsibilities, workflows, and measurement,” says Ali Orlando Wert , senior director of content strategy at Appfire. “It takes time, but the payoffs are worth it. You have to learn how to crawl before you can walk – and walk before you can run.”

Measurement and goals: Generating sales and revenue rises

Almost half (46%) of B2B marketers agree their organization measures content performance effectively. Thirty-six percent disagree, and 15% neither agree nor disagree. Only 3% say they don’t measure content performance.

The five most frequently used metrics to assess content performance are conversions (73%), email engagement (71%), website traffic (71%), website engagement (69%), and social media analytics (65%).

About half (52%) mention the quality of leads, 45% say they rely on search rankings, 41% use quantity of leads, 32% track email subscribers, and 29% track the cost to acquire a lead, subscriber, or customer.

The most common challenge B2B marketers have while measuring content performance is integrating/correlating data across multiple platforms (84%), followed by extracting insights from data (77%), tying performance data to goals (76%), organizational goal setting (70%), and lack of training (66%).

Regarding goals, 84% of B2B marketers say content marketing helped create brand awareness in the last 12 months. Seventy-six percent say it helped generate demand/leads; 63% say it helped nurture subscribers/audiences/leads, and 58% say it helped generate sales/revenue (up from 42% the previous year).

Success factors: Know your audience

To separate top performers from the pack, we asked the B2B marketers to assess the success of their content marketing approach.

Twenty-eight percent rate the success of their organization’s content marketing approach as extremely or very successful. Another 57% report moderate success and 15% feel minimally or not at all successful.

The most popular factor for successful marketers is knowing their audience (79%).

This makes sense, considering that “creating the right content for our audience” is the top challenge. The logic? Top-performing content marketers prioritize knowing their audiences to create the right content for those audiences.

Top performers also set goals that align with their organization’s objectives (68%), effectively measure and demonstrate content performance (61%), and show thought leadership (60%). Collaboration with other teams (55%) and a documented strategy (53%) also help top performers reach high levels of content marketing success.

We looked at several other dimensions to identify how top performers differ from their peers. Of note, top performers:

• Are backed by leaders who understand the work they do.
• Are more likely to have the right content management technologies.
• Have better communication across organizational silos.
• Do a better job of measuring content effectiveness.
• Are more likely to use content marketing successfully to generate demand/leads, nurture subscribers/audiences/leads, generate sales/revenue, and grow a subscribed audience.

Little difference exists between top performers and their less successful peers when it comes to the adoption of generative AI tools and related guidelines. It will be interesting to see if and how that changes next year.

Budgets and spending: Holding steady

To explore budget plans for 2024, we asked respondents if they have knowledge of their organization’s budget/budgeting process for content marketing. Then, we asked follow-up questions to the 55% who say they do have budget knowledge.

Content marketing as a percentage of total marketing spend

Here’s what they say about the total marketing budget (excluding salaries):

• About a quarter (24%) say content marketing takes up one-fourth or more of the total marketing budget.
• Nearly one in three (29%) indicate that 10% to 24% of the marketing budget goes to content marketing.
• Just under half (48%) say less than 10% of the marketing budget goes to content marketing.

Content marketing budget outlook for 2024

Next, we asked about their 2024 content marketing budget. Forty-five percent think their content marketing budget will increase compared with 2023, whereas 42% think it will stay the same. Only 6% think it will decrease.

Where will the budget go?

We also asked where respondents plan to increase their spending.

Sixty-nine percent of B2B marketers say they would increase their investment in video, followed by thought leadership content (53%), in-person events (47%), paid advertising (43%), online community building (33%), webinars (33%), audio content (25%), digital events (21%), and hybrid events (11%).

The increased investment in video isn’t surprising. The focus on thought leadership content might surprise, but it shouldn’t, says Stephanie Losee , director of executive and ABM content at Autodesk.

“As measurement becomes more sophisticated, companies are finding they’re better able to quantify the return from upper-funnel activities like thought leadership content ,” she says. “At the same time, companies recognize the impact of shifting their status from vendor to true partner with their customers’ businesses.

“Autodesk recently launched its first global, longitudinal State of Design & Make report (registration required), and we’re finding that its insights are of such value to our customers that it’s enabling conversations we’ve never been able to have before. These conversations are worth gold to both sides, and I would imagine other B2B companies are finding the same thing,” Stephanie says.

Top content-related priorities for 2024: Leading with thought leadership

We asked an open-ended question about marketers’ top three content-related priorities for 2024. The responses indicate marketers place an emphasis on thought leadership and becoming a trusted resource.

Other frequently mentioned priorities include:

• Better understanding of the audience
• Discovering the best ways to use AI
• Increasing brand awareness
• Lead generation
• Using more video
• Better use of analytics
• Conversions
• Repurposing existing content

Content marketing predictions for 2024: AI is top of mind

In another open-ended question, we asked B2B marketers, “What content marketing trends do you predict for 2024?” You probably guessed the most popular trend: AI.

Here are some of the marketers’ comments about how AI will affect content marketing next year:

• “We’ll see generative AI everywhere, all the time.”
• “There will be struggles to determine the best use of generative AI in content marketing.”
• “AI will likely result in a flood of poor-quality, machine-written content. Winners will use AI for automating the processes that support content creation while continuing to create high-quality human-generated content.”
• “AI has made creating content so easy that there are and will be too many long articles on similar subjects; most will never be read or viewed. A sea of too many words. I predict short-form content will have to be the driver for eyeballs.”

Other trends include:

• Greater demand for high-quality content as consumers grow weary of AI-generated content
• Importance of video content
• Increasing use of short video and audio content
• Impact of AI on SEO

Among the related comments:

• “Event marketing (webinars and video thought leadership) will become more necessary as teams rely on AI-generated written content.”
• “AI will be an industry sea change and strongly impact the meaning of SEO. Marketers need to be ready to ride the wave or get left behind.”
• “Excitement around AI-generated content will rise before flattening out when people realize it’s hard to differentiate, validate, verify, attribute, and authenticate. New tools, processes, and roles will emerge to tackle this challenge.”
• “Long-form reports could start to see a decline. If that is the case, we will need a replacement. Logically, that could be a webinar or video series that digs deeper into the takeaways.”

What does this year’s research suggest B2B content marketers do to move forward?

I asked CMI’s Robert Rose for some insights. He says the steps are clear: Develop standards, guidelines, and playbooks for how to operate – just like every other function in business does.

“Imagine if everyone in your organization had a different idea of how to define ‘revenue’ or ‘profit margin,’” Robert says. “Imagine if each salesperson had their own version of your company’s customer agreements and tried to figure out how to write them for every new deal. The legal team would be apoplectic. You’d start to hear from sales how they were frustrated that they couldn’t figure out how to make the ‘right agreement,’ or how to create agreements ‘consistently,’ or that there was a complete ‘lack of resources’ for creating agreements.”

Just remember: Standards can change along with your team, audiences, and business priorities. “Setting standards doesn’t mean casting policies and templates in stone,” Robert says. “Standards only exist so that we can always question the standard and make sure that there’s improvement available to use in setting new standards.”

He offers these five steps to take to solidify your content marketing strategy and execution:

• Direct. Create an initiative that will define the scope of the most important standards for your content marketing. Prioritize the areas that hurt the most. Work with leadership to decide where to start. Maybe it’s persona development. Maybe you need a new standardized content process. Maybe you need a solid taxonomy. Build the list and make it a real initiative.
• Define . Create a common understanding of all the things associated with the standards. Don’t assume that everybody knows. They don’t. What is a white paper? What is an e-book? What is a campaign vs. an initiative? What is a blog post vs. an article? Getting to a common language is one of the most powerful things you can do to coordinate better.
• Develop . You need both policies and playbooks. Policies are the formal documentation of your definitions and standards. Playbooks are how you communicate combinations of policies so that different people can not just understand them but are ready, willing, and able to follow them.
• Distribute . If no one follows the standards, they’re not standards. So, you need to develop a plan for how your new playbooks fit into the larger, cross-functional approach to the content strategy. You need to deepen the integration into each department – even if that is just four other people in your company.
• Distill . Evolve your standards. Make them living documents. Deploy technology to enforce and scale the standards. Test. If a standard isn’t working, change it. Sometimes, more organic processes are OK. Sometimes, it’s OK to acknowledge two definitions for something. The key is acknowledging a change to an existing standard so you know whether it improves things.

For their 14 th annual content marketing survey, CMI and MarketingProfs surveyed 1,080 recipients around the globe – representing a range of industries, functional areas, and company sizes — in July 2023. The online survey was emailed to a sample of marketers using lists from CMI and MarketingProfs.

This article presents the findings from the 894 respondents, mostly from North America, who indicated their organization is primarily B2B and that they are either content marketers or work in marketing, communications, or other roles involving content.

Thanks to the survey participants, who made this research possible, and to everyone who helps disseminate these findings throughout the content marketing industry.

Cover image by Joseph Kalinowski/Content Marketing Institute

About Content Marketing Institute

Content Marketing Institute (CMI) exists to do one thing: advance the practice of content marketing through online education and in-person and digital events. We create and curate content experiences that teach marketers and creators from enterprise brands, small businesses, and agencies how to attract and retain customers through compelling, multichannel storytelling. Global brands turn to CMI for strategic consultation, training, and research. Organizations from around the world send teams to Content Marketing World, the largest content marketing-focused event, the Marketing Analytics & Data Science (MADS) conference, and CMI virtual events, including ContentTECH Summit. Our community of 215,000+ content marketers shares camaraderie and conversation. CMI is organized by Informa Connect. To learn more, visit www.contentmarketinginstitute.com .

About MarketingProfs

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More than 600,000 marketing professionals worldwide rely on MarketingProfs for B2B Marketing training and education backed by data science, psychology, and real-world experience. Access free B2B marketing publications, virtual conferences, podcasts, daily newsletters (and more), and check out the MarketingProfs B2B Forum–the flagship in-person event for B2B Marketing training and education at MarketingProfs.com.

About Brightspot

Brightspot , the content management system to boost your business.

Why Brightspot? Align your technology approach and content strategy with Brightspot, the leading Content Management System for delivering exceptional digital experiences. Brightspot helps global organizations meet the business needs of today and scale to capitalize on the opportunities of tomorrow. Our Enterprise CMS and world-class team solves your unique business challenges at scale. Fast, flexible, and fully customizable, Brightspot perfectly harmonizes your technology approach with your content strategy and grows with you as your business evolves. Our customer-obsessed teams walk with you every step of the way with an unwavering commitment to your long-term success. To learn more, visit www.brightspot.com .

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Global Growth Is Stabilizing for the First Time in Three Years

But 80% of world population will experience slower growth than in pre-COVID decade

WASHINGTON, June 11, 2024 — The global economy is expected to stabilize for the first time in three years in 2024—but at a level that is weak by recent historical standards, according to the World Bank’s latest Global Economic Prospects report.

Global growth is projected to hold steady at 2.6% in 2024 before edging up to an average of 2.7% in 2025-26. That is well below the 3.1% average in the decade before COVID-19. The forecast implies that over the course of 2024-26 countries that collectively account for more than 80% of the world’s population and global GDP would still be growing more slowly than they did in the decade before COVID-19.

Overall, developing economies are projected to grow 4% on average over 2024-25, slightly slower than in 2023. Growth in low-income economies is expected to accelerate to 5% in 2024 from 3.8% in 2023. However, the forecasts for 2024 growth reflect downgrades in three out of every four low-income economies since January. In advanced economies, growth is set to remain steady at 1.5% in 2024 before rising to 1.7% in 2025.

“Four years after the upheavals caused by the pandemic, conflicts, inflation, and monetary tightening, it appears that global economic growth is steadying,” said Indermit Gill, the World Bank Group’s Chief Economist and Senior Vice President. “ However, growth is at lower levels than before 2020. Prospects for the world’s poorest economies are even more worrisome. They face punishing levels of debt service, constricting trade possibilities, and costly climate events. Developing economies will have to find ways to encourage private investment, reduce public debt, and improve education, health, and basic infrastructure. The poorest among them—especially the 75 countries eligible for concessional assistance from the International Development Association—will not be able to do this without international support.”

This year, one in four developing economies is expected to remain poorer than it was on the eve of the pandemic in 2019. This proportion is twice as high for countries in fragile- and conflict-affected situations. Moreover, the income gap between developing economies and advanced economies is set to widen in nearly half of developing economies over 2020-24 —the highest share since the 1990s. Per capita income in these economies—an important indicator of living standards—is expected to grow by 3.0% on average through 2026, well below the average of 3.8% in the decade before COVID-19.

Global inflation is expected to moderate to 3.5% in 2024 and 2.9% in 2025, but the pace of decline is slower than was projected just six months ago. Many central banks, as a result, are expected to remain cautious in lowering policy interest rates. Global interest rates are likely to remain high by the standards of recent decades—averaging about 4% over 2025-26, roughly double the 2000-19 average.

“Although food and energy prices have moderated across the world, core inflation remains relatively high—and could stay that way,” said Ayhan Kose, the World Bank’s Deputy Chief Economist and Director of the Prospects Group . “That could prompt central banks in major advanced economies to delay interest-rate cuts. An environment of ‘higher-for-longer’ rates would mean tighter global financial conditions and much weaker growth in developing economies.”

The latest Global Economic Prospects report also features two analytical chapters of topical importance. The first outlines how public investment can be used to accelerate private investment and promote economic growth. It finds that public investment growth in developing economies has halved since the global financial crisis, dropping to an annual average of 5% in the past decade. Yet public investment can be a powerful policy lever. For developing economies with ample fiscal space and efficient government spending practices, scaling up public investment by 1% of GDP can increase the level of output by up to 1.6% over the medium term.

The second analytical chapter explores why small states—those with a population of around 1.5 million or less—suffer chronic fiscal difficulties. Two-fifths of the 35 developing economies that are small states are at high risk of debt distress or already in it. That’s roughly twice the share for other developing economies. Comprehensive reforms are needed to address the fiscal challenges of small states. Revenues could be drawn from a more stable and secure tax base. Spending efficiency could be improved —especially in health, education, and infrastructure. Fiscal frameworks could be adopted to manage the higher frequency of natural disasters and other shocks. Targeted and coordinated global policies can also help put these countries on a more sustainable fiscal path.

Download the full report: https://bit.ly/GEP-June-2024-FullReport

Download growth data:   https://bit.ly/GEP-June-2024-Data

Download charts: https://bit.ly/GEP-June-2024-Charts

Regional Outlooks:

East Asia and Pacific:  Growth is expected to decelerate to 4.8% in 2024 and to 4.2% in 2025. For more, see  regional overview.

Europe and Central Asia:  Growth is expected to edge down to 3.0% in 2024 before moderating to 2.9% in 2025. For more, see  regional overview .

Latin America and the Caribbean:  Growth is expected to decline to 1.8% in 2024 before picking up to 2.7% in 2025. For more, see  regional overview .

Middle East and North Africa:  Growth is expected to pick up to 2.8% in 2024 and 4.2% in 2025. For more, see  regional overview.

South Asia:  Growth is expected to slow to 6.2% in 2024 and remain steady at 6.2% in 2025. For more, see regional overview.

Sub-Saharan Africa: Growth is expected to pick up to 3.5% in 2024 and to 3.9% in 2025. For more, see  regional overview.

Website:  www.worldbank.org/gep

Facebook:  facebook.com/worldbank

X (Twitter):  twitter.com/worldbank

YouTube:  youtube.com/worldbank

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