literature review on business strategy

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

LITERATURE REVIEW ON BUSINESS STRATEGY AND COMPETITIVE ANALYSIS MANAGEMENT.docx

Related Papers

sajid rahman

The present study aims to investigate the relationship between high-performance work system (HPWS) and competitive advantage (CA) through a mediating role of collective human capital (CHC) in the banking organizations of Pakistan. The sample of the study consists of 76 branch managers of both public and private sectors banks operated in Rawalpindi and Islamabad. The finding shows that collective human capital fully mediate the link between high-performance work system and competitive advantage. The study also found that private sector banks managers highly practice HR practices in their respective branches as compared to their public sector banks managers. Also, female managers practice more HR practices as compared to their male counterpart, and managers of higher experience practice more HR practices in their respective organizations. Implications of the results, limitations and future directions have also been discussed.

The International Journal of Human Resource Management

Abagail McWilliams , Patrick Wright

Meryem Aybas

High Performance Work Systems (HPWS) have become highly important as a source of competitive advantage in today's competitive business environment. Human resource management capabilities are important for attracting, selecting, retaining, motivating and developing the workforce in an organization. Meanwhile organizational culture, considered as a form of organizational capital may also be a driver for sustained competitive advantage. Organizational values, as the reflection of organization culture are asserted to influence the strategic issues such as strategic change, management decision making and also shape employee commitment and organization's interaction with external stakeholders. These two firm-specific resources may complement each other and further leveraging organizational performance. In consideration with the resource-based view, this paper seeks to identify whether high performance human resource practices are articulated and publicly espoused via organizational values. The research question of the study pertains to whether selected organizations incorporate components of HPWS in their organizational value statements. Summative content analysis is conducted which is supported by the NVivo software program, the findings of which are discussed in the text.

Farhad Alipour

WaDoO MaTHuO

In recent years, resource-based theory has emerged as one of the most promising theoretical frameworks in the field of strategic management. Unfortunately, past articulations of the theory are limited by their inability to incorporate effectively organizational learning, a key organizational capability thought to contribute to competitive advantage. This paper presents a model designed to incorporate organizational learning into the resource-based view and demonstrates the need for such a model through a number of case examples. Our fundamental argument is that organizational learning is a strategic capability and that resource-based theory must be able to incorporate organizational learning if it is to explain adequately the process of achieving sustainable competitive advantage.

Journal of Social Sciences (COES&RJ-JSS) ISSN (E): 2305-9249 ISSN (P): 2305-9494

Journal of Social Sciences COES&RJ-JSS

The most important upbeat facet for any organization in the present day regardless of motive, is Human Resource. This is conceivably so because, it is the starting place of achieving competitive plus. The management of this valuable resource in higher educational institutions (HEI’s) in Nigeria remains even more tasking and challenging than managing other material resources such as finance, technology and etc. To be able to effectively manage human resource, adequate, appropriate and sound human resource management practices need not to only be institutionalized but also interceded. This paper proffered a model that captures this relationship and ensures effective performance management system in Nigerian HEI’s.

Khalid Sahibi

American Journal

This paper analyses the effect of resource rarity on sustainable competitiveness of universities. The analysis is embedded on the Resource Based View model (RBV and the balanced scorecard model of performance). According to RBV of strategic Management, competitive advantage is closely related to companies' internal characteristics (value, rarity, inimitability and non-substitutability). The constructs of sustainable competitiveness were derived from the balanced scorecard (Financial; Teaching/Learning; Service/outreach; Scholarship/ Research and Workplace satisfaction). Although RBV is considered one of the most influential theories of strategic management, this paper unveils the empirical evidence of resource rarity on sustainable competitiveness in the service industry (universities). A sample of 262 was selected from 2 universities in Kenya (one private and one public). Using the regression analysis, findings revealed that the public university was superior in resource rarity than the private university and also that resource rarity contributes to sustainable competitiveness in universities.

Journal of Strategic Marketing

Fahri Karakaya

This research examines the relationships among the barriers to market entry: capital requirements; competitive advantage of incumbent firms; business environment; and firm competence, and their relationship to firm performance. Through a mail survey, data were collected on a sample of 190 companies. A hierarchical regression analysis enabled the assessment of the relationships among barriers to entry and firm performance. In addition, the paper examines the quadratic function of second degree among the variables to see ...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

What Is Strategy, Again?

Andrea Ovans

Looking back at the major schools of thought.

If you read what Peter Drucker had to say about competition back in the late ’50s and early ‘60s, he really only talked about one thing: competition on price. He was hardly alone — that was evidently how most economists thought about competition, too.

AO Andrea Ovans is a former senior editor at Harvard Business Review.

Partner Center

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Original Article
Published: 25 January 2022
Volume 20 , pages 223–255, ( 2022 )

Cite this article

Rajesh Chidananda Reddy 1 ,
Biplab Bhattacharjee ORCID: orcid.org/0000-0002-3886-8409 2 ,
Debasisha Mishra 3 &
Anandadeep Mandal 4

9518 Accesses

10 Citations

Explore all metrics

While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.

Proposing an integrative data-analytics framework for micro, small and medium enterprises: a systematic review substantiated by evidence from two case studies

Navigating Digital Transformation and Knowledge Structures: Insights for Small and Medium-Sized Enterprises

Business Process Management in CEE Countries: A Literature-Based Research Landscape

Avoid common mistakes on your manuscript.

1 Introduction

There is a digital revolution in businesses across sectors and geographies. There is a need for both traditional and new business models to capitalize on the digital technologies to overcome existential threats and gain access to game-changing opportunities. The digitalization revolution has gained momentum on the back of the Covid-19 pandemic which has forced businesses to reshape their business models. The availability of digital technologies and vastly spread Internet connectivity is leading to large-scale digital transformations, enabling organizations to redefine their business models (Van Tonder et al. 2020 ). Digital transformation involves two steps, namely, digitization and digitalization (Verhoef et al. 2021 ). Digitization is the process of transforming information from analogue to the digital mode. Digitalization refers to the process of leveraging digital technologies to redefine business models to benefit from digital business opportunities.

For successful implementation of digital transformation initiatives in shaping the next generation of products and services, organizations need a coherent and well-crafted digital strategy supported by technology and customer experience (Erceg and Zoranović 2020 ; Pappas et al. 2018 ; Sebastian et al. 2017 ; Zaki 2019 ). This strategy has to be enabled by digital capabilities (Westerman 2018 ). Any digital strategy encompasses the exploration, application, and large-scale adoption of technologies, such as Social, Mobile, Analytics, Cloud, and IoT (SMACIT), Virtual Reality, Blockchain, 3D Printing, Drones, and Augmented Reality. Adopting the right digital strategy will enable organizations in remaining competitive, overcoming the challenges of digitalization, and taking advantage of opportunities (Becker and Schmid 2020 ).

The digital strategy augments the organizational strategy by transitioning from predicting and planning to experimenting and responding. It also helps in agile planning, inclusive responsibilities, and utilization of IT capabilities. Digital solutions strategy often includes establishing ‘Data Science’ capabilities (Dremel et al. 2017 ). The rapid increase in digitization is opening up avenues for the large-scale capture, storage, and analysis of business data, which if optimally used with ‘Data Science’ tools and technologies can unlock significant business benefits. ‘Data Science’ plays a vital role in harnessing data from all organizational touchpoints and analyzing it to generate business insights across the domains of R&D, manufacturing, marketing, operations, supply chain, customer relationship management, strategy formulation, finance, human resource management, and others.

Driven by technological advancements, the increased ability to collect and process large amounts of data to extract business insights has led to a rush towards the implementation of ‘Data Science’ initiatives. It is expected that early adopters of Artificial Intelligence (AI) will share a global profit pool of $1 trillion by 2030 (Bughin 2018 ). Though the importance of ‘Data Science’ is widely acknowledged, most organizations are still in the early stages of adoption. About 75% to 80% of organizations (Deloitte’s Analytics Advantage Survey, 2013; Forbes report, 2015) have failed to successfully deploy ‘Data Science’ (Ghasemaghaei et al. 2018 ; Mazzei and Noble 2017 ).

In 2015, more than 75% of organizations either invested or planned to invest in ‘Data Science’ (Sun et al. 2020 ). However, more than 95% (2017 survey by McKinsey Global Institute and Digital@McKinsey) of the companies across domains and geography have not embraced AI to reinventing their business operations (Bughin 2018 ). As of 2017, while more than 80% of the organizations see an opportunity in adopting AI strategically, only 23% of them have adopted AI, and another 23% have started with pilot projects in AI (Ransbotham et al. 2017 ). By 2019, though AI adoption grew by 270% over the last four years (2015–2018), only 37% of the organizations have actually deployed AI or at least have short-term plans of embracing it (Rowell- Jones and Howard, 2019: 2019 Gartner CIO Survey). Amongst the ‘Data Science’ technology adopters, a large section has not achieved the desired Return on Investments (ROI) or repeated success from adoption (Braganza et al. 2017 ).

In the recent past, GE failed to implement a digital strategy planned towards digital transformation and as a result, had to lay off employees supporting its digital strategy (Colvin 2018 ). In similar lines, Nike could not reap benefits from customer digital data that was intended to provide feedback and suggestions on consumer lifestyle (de Swaan Arons et al. 2014 ). Nike had to discontinue its Nike + products (Correani et al. 2020 ). This implies that despite much hype (Fox and Do 2013 ) about ‘Data Science’ and its benefits, the successful implementation in organizations and in public sector (Vydra and Klievink 2019 ), and achieving the desired outcomes remain a challenge (Tabesh et al. 2019 ). We attribute this challenge to the lack of a well-crafted, holistic, distinct and robust 'Data Science Strategy' and/or failure in its implementation.

The evidence from reviewed articles suggests a lack of concise framework for understanding the enablers and barriers of ‘Data Science Strategy’ in enterprises. Therefore, the main purpose of this article is to synthesize all the enablers and barriers affecting the formulation and implementation of ‘Data Science Strategy’ in organizations across different industries, sectors, and geographies. To begin with, the structured literature survey is carried out to summarize the learnings from extant literature. The literary content is then segmented under appropriate themes. The study also proposes a conceptual framework for the successful implementation of ‘Data Science’ projects which would also be of use to future researchers with significant agenda for ‘Data Science Strategy’ research.

The learnings from this “Systematic Literature Review (SLR)” can be used by both academic researchers and practitioners in the field of ‘Data Science’ and ‘Information System’ Strategy. Academic researchers can explore the gap in existing literature in the area of ‘Data Science Strategy’ and attempt empirical studies on areas where the gaps have been identified. Practitioners such as ‘Data Science’ management, ‘Chief Data Science Officers’ can use our findings to understand the challenges, issues, best practices, and the possibilities of implementing ‘Data Science Strategy’ in their organizations.

Specifically, this paper focuses on the following research questions with respect to Data Science: What are the enablers and barriers that affect the strategic implementation and success of ‘Data Science’ in organizations?

The remaining sections of the paper are organized as follows: The background section presents the contextual information on ‘Data Science’ and its constituents, ‘Data Science Strategy’, and Contexts and associated factors. The research method section then discusses the methodology used in our literature review. This is followed by the results section, which presents the synthesized findings and conceptual framework. The discussion section details the various organizational approaches to ‘Data Science’ adoption, theoretical contributions and managerial implications. The limitations and future research section details the limitations of the study and the avenues for future research. Finally, the conclusion section summarizes the findings of the study.

2 Background

The definition of ‘Data Science’ is constantly evolving with the continuous advancement in technological trends. ‘Data Science’ is a cyclical process of capturing the business needs and acquiring the relevant data, storage, security, privacy, preparation, pre-processing, analytics; generating and communicating insights; and finally, actuating these actionable insights (Jägare 2019 ). ‘Data Science’ is an umbrella term that cuts across constituents such as Artificial Intelligence (AI), Machine Learning (ML), Big Data (BD), Big Data Analytics (BDA), Visualization, and Business Intelligence & Analytics (BI&A), Mathematics, Computer Science & Programming Skills, and Domain Knowledge (Fig. 1 ).

‘Data Science’ constituents. The size and shape of each circle (or oval) in Fig. 1 is only representative, drawn in convenience to adjust the text and not indicative of any weightage to the ‘Data Science Constituents.’

‘Data Science Strategy (DSS)’ refers to the overall organizational strategy signifying its ‘Data Science' investment. It includes the overall Data Science objectives, strategic choices, regulatory requirements, data strategy (data management including, acquisition, storage, security, privacy, ethics, and data governance), resource management, competency build-up, infrastructure planning. This strategy also defines the means to track Key Performance Indicators (KPI) and Return on Investments (ROI) (Jägare 2019 ).

The adoption and successful implementation factors of ‘Data Science Strategy’ vary between industries and, more importantly, on the aligned business objectives. Currently, the Covid-19 pandemic is testing the resilience of organizations by challenging organizational design and work practices. In giant organizations (viz. Amazon, Google, AirBnB), Small and Medium Enterprises (SMEs), and start-ups, the key focus is on refining the current organizational strategy to integrate digital-enabled exponential systems (George et al. 2020 ). In context of the growing debate on automation replacing jobs, Soto-Acosta ( 2020 ) note that only 20% of activities are automated and the rest are augmented by digitalization. To make automation possible, more jobs need to be created in the area of ‘Data Science’ in order to perform the majority of tasks remotely. The Covid-19 pandemic is acting as a catalyst for the digital transformation of established firms as well as startups, forcing them to innovate in order to survive. Even traditional companies with no or limited digital experiences are adopting digital technologies in order to stay relevant in the market. This acceleration in the adoption of digital technologies finds reflection in the revised way of techniques for data collection, online appointments and therapies, work-from-home, smart-homes, schooling, learning and social interconnect activities (Hantrais et al. 2020 ; Maalsen and Dowling 2020 ).

Extant literature has studied ‘Data Science Strategy’ in the contexts of dynamic market places, organizational and dynamic capabilities (Knabke & Olbrich, 2018 ), innovations (Mikalef et al. 2018 , 2019a ), and new product development processes (Johnson et al. 2017 ). Studies have been conducted in the context of but not limited to healthcare (Chen and Banerjee 2020 ; Kamble et al. 2019 ; Kemppainen et al. 2019 ; Li et al. 2021 ; Newlands et al. 2020 ; Ramnath et al. 2020 ; Yang et al. 2015 ; Wang and Hajli 2017 ), B2B (Hallikainen et al. 2020 ), construction (Ahmed et al. 2018 ; Ram et al. 2019 ; Sang et al. 2020 ), supply chain management (Ali et al. 2020 ; Arunachalam et al. 2018 ; Brinch et al. 2018 ; Dubey et al. 2019a ; Khan 2019 ; Lai et al. 2018 ; Lamba and Singh 2018 ; Mandal 2019 ; Singh and Singh 2019 ; Wang et al. 2018c ), manufacturing (Popovič et al. 2018 ; Verma 2017 ), consumer goods (Rialti et al. 2018 ); e-commerce (Behl et al. 2019 ; Wamba et al. 2017 ), telecommunications (Saldžiūnas and Skyrius 2017 ; Walker and Brown 2019 ), banking and financial services (Lee et al. 2017 ; Lautenbach et al. 2017 ; Gregory 2011 ), automotive (Dremel et al. 2017 ), and airlines (Holland et al. 2020 ).

Attempts have also been made in the past to conduct SLR to identify various components of Data Science from a strategy point of view. These review articles from the strategic adoption point have focused highly on individual components of ‘Data Science’. In the recent past ‘Big Data’ (Ciampi et al. 2020 ; Günther et al. 2017 ; Mikalef et al. 2016 , 2020a ; Nelson and Olovsson 2016 ; Olszak and Mach-Król 2018 ; Wamba et al. 2015 ; Zhao-hong et al. 2018 ), ‘Big Data and dynamic capabilities’ (Rialti et al. 2019 ), ‘Big Data business models’ (Huberty 2015 ; Wiener et al. 2020 ), and ‘Big Data assessment models’ (Adrian et al. 2016 ) have been explored. Researchers have also focused upon ‘Big Data Analytics’ (Adrian et al. 2017 ; Al-Sai et al. 2020 ; Bogdan and Lungescu 2018 ; Inamdar et al. 2020 ; Maroufkhani et al. 2019 ; Mikalef et al. 2019a ; Singh et al. 2021 ; Sivarajah et al. 2017 ), Artificial Intelligence (Alsheiabni et al. 2020 ; Borges et al. 2021 ; Keding 2020 ; Kitsios and Kamariotou 2021 ; Markus 2017 ), Business Analytics (Cao and Duan 2017 ), and Business Intelligence and Analytics (BI&A) (Chen et al. 2012 ; Eggert and Alberts 2020 ; Lautenbach et al. 2017 ; Llave 2017 ; Moreno et al. 2019 ; Sang et al. 2020 ).

The literature on ‘Data Science Strategy’ under single-unified umbrella term that cuts across different technologies, such as ‘Big Data’, ‘Data Analytics’, ‘Artificial Intelligence’, ‘Machine Learning’, ‘Deep Learning’, ‘Visualization’, and ‘Business Intelligence’ is considerably scarce. In contrast, studies on ‘Data Science’ adoption and implementation in enterprises with corresponding barriers and enablers across various industry sectors and domains are limited. The conceptual framework for holistic ‘Data Science Strategy’ is absent to our limited understanding. To be successful in deriving desired business outcomes organizations need to recognize the need for a well-crafted and all-encompassing ‘Data Science Strategy’ augmenting digitalization and digital transformation.

3 Research method: a systematic literature review

The SLR is focused on identifying and synthesizing the knowledge on enablers and barriers influencing ‘Data Science Strategy’ in organizations. It takes into consideration the accessible scholarly business management research articles in the English language from four databases, namely, EBSCO business source ultimate, Scopus, Science Direct, and Pro-Quest databases. These databases are chosen since they have the most relevant and reputed peer-reviewed list of journals (by renowned publications like Springer, Emerald, IEEE, Elsevier, Taylor and Francis, etc.) where ‘Data Science Strategy’ research is traditionally published. Also, the large number of past SLRs (Mikalef et al., 2018 ; Dam et al., 2019 ; Sivarajah et al., 2017 ; Eggert and Alberts, 2020 ; Arunachalam et al., 2018 ) focused on the AI, ML, BDA domains too have considered these scholarly databases. The articles were identified based on the keyword search in title, abstract, and article keywords sections. In total, the search yielded 573 documents. The duplicates were excluded leaving a total of 480 articles for consideration. In the next step, the number of relevant articles was further narrowed down based on examination of title and abstracts and this brought down the number to 158 articles. The examination was focused towards characterizing factors influencing the ‘Data Science Strategy’ implementation in organizations. The systematic process (Fig. 2 ), based on further in-depth analyses for relevance and quality of articles, resulted in a final literature base comprising of 113 articles, leading to analyses of findings, synthesis, conceptual framework and research agenda for future studies.

Methodology and output

4.1 Findings

This article summarizes the research in the field during the period 1998–2021.However, there have been more articles published on the subject during 2015–2021., Driven by the advancements in ‘Information Technology Capabilities’ and reduction in cost of computations, organizations of all sizes and types in the last quarter of twentieth century began to adopt digital technology to increase their competitiveness (Khosrowpour, 1990 ). Their focus was to implement business applications evolving from technological advancements in data associated fields. The ‘Big Data’ and ‘Data Science’ terms were coined in the year 2007 and 2008, respectively, and ‘Big Data Analytics’ gained importance in the year 2011 (Nguyen et al. 2018 ). As a result, both academic and practitioners shifted attention to the adoption and implementation of ‘Data Science’ strategies.

After the systematic process, as described in the research methodology section, this paper included 79 distinct journals in this literature review. Out of the 113 articles selected for the review, 17 were from A*-category (according to the 2019 ABDC journal quality list), followed by 37 in A category, 15 in B category, and 17 in C category journals. Likewise, 16 articles were indexed in the Scopus database. Further, 11 articles belonged neither to the ABDC quality list nor the Scopus database (Fig. 3 ).

Year-wise distribution of articles with publication ranking

Figure 3 also represents the distribution of articles between the years 1998 and 2021 (till the date of search). The number of articles on ‘Data Science Strategy’ have increased over the years. The year 2019 recorded a maximum number of 31 papers. The steep increase in the number of articles over the past five years is testimony to the importance of the subject and the interest it is generating amongst academicians and practitioners worldwide. With more and more businesses investing in data-driven technologies, clear-cut business objectives are still far from realization. Hence, despite the increased number of articles, this research domain is still an emerging one.

Figure 4 shows the distribution of articles based on the industry type with corresponding research methods. The maximum number of articles, that is 48 articles, deal with multiple industries. Of these, 28 are empirical studies. Individually, the ‘Healthcare’ industry dominates the publications with 09 articles, out of which four are empirical studies, four are case study articles, and one SLR. ‘Financial Services’ and ‘Construction’ industries have received attention in 06 and 05 articles, respectively.

Industry-wise distribution of articles with research methods. *Multi-Industry in the above plot refers to data collected from respondents (by means of interviews, case studies, and empirical investigations) belonging to multiple industries such as Technology & Entertainment, Web Service, Healthcare, Insurance, Manufacturing, Retail, Telecommunications, Agriculture, Banking, Transportation, Oil & Gas, Media, Consumer Goods, and many more

Figure 5 showcases the distribution of articles based on the domain of work with corresponding research methods. The supply chain management domain leads with 15 articles, of which eight articles are empirical studies. Manufacturing and policy studies are credited with 4 articles each Other domains with corresponding articles are also listed.

Domain-wise distribution of articles with research methods

The distinct ‘Enablers and Barriers’ of ‘Data Science Strategy’ for different industry sectors, and domains are segregated in supplementary Table S1. The applicability and intensity of each barrier towards ‘Data Science’ adoption and its success varies based on the industry sector and the domain areas. Table S1 provides a comprehensive list of barriers in each of the major industries and domains varying from airlines to construction, healthcare to financial services, manufacturing to e-commerce, and so on. The underpinning theories used in the reviewed literature along with the key enablers of ‘Data Science Strategy’ implementation are documented in supplementary Table S2. Reviewed literature has drawn upon many theories including Resource-Based View (RBV), Dynamic Capability View (DCV), Knowledge-Based View (KBV), Technology-Organization- Environment (TOE) framework, Organizational Learning Theory (OLT), Diffusion of Innovation Theory (DIT), Technology Acceptance Model (TAM), Task-Technology Fit (TTF), Information Systems Success Model (ISSM), Agency Theory (AT), Stakeholders Theory (ST), Institutional Theory (ITh), and many more.

Different research methods adopted by the reviewed articles are graphically summarized in supplementary Fig. S1. The findings imply that nine different research methods are used in general. The majority of studies involved ‘Analytical or Empirical’ studies followed by ‘Case Study-based’ methodologies and ‘Interviewing’. The other research methods include Theoretical, Conceptual, Focus Group Discussions, Delphi Studies and Expert Viewpoints. The article search on the subject area resulted in articles from 79 distinct reputed journal publications. The journal publications with more than one article are documented in supplementary Fig. S2.’British Journal of Management ‘ has a maximum six number of articles published in the research area followed by five articles each in’Engineering, Construction and Architectural Management’, and’Industrial Marketing Management Journals ‘. Four articles are published in’Information and Management Journal ‘ followed by three articles each in’Information Systems & e-Business Management ‘,’Information Systems Frontiers ‘, and’International Journal of Logistics Management.’ The underpinning theories (49 numbers) for the reviewed articles on ‘Data Science Strategy’ is highlighted in supplementary Fig. S3. Resource-Based View is extensively used by 26% of the published research articles. Dynamic Capability Theory (19%) is the next prominent theory followed by Technology-Organization-Environment, which finds a place in 7% of the articles. Around 39% of the articles are drawn upon by different other theories as highlighted in supplementary Table S2. The study region of reviewed articles is spread across different geographies, as documented in supplementary Fig. S4. The United States of America dominates the list with 15 articles followed by 8 studies based on Indian industries. Europe and China follow the list with 7 articles each studied in the respective regions. Other regions including Asia, Singapore, Canada, Slovenia, Saudi Arabia, Pakistan, Norway, Iran, Finland, Dutch, and Brazil contribute one article each.

4.2 Synthesis of ‘data science’ components

The reviewed articles categorize the ‘Data Science’ components based on underlying theoretical frameworks. Under the lens of RBV, they are classified under Tangible, Human, and Intangible resources (Gupta and George 2016 ; Mikalef and Gupta 2021 ). Tangible resources comprise of data (internal, external, and combined data), technology (Hadoop, NoSQL, etc.), and basic resources, such as time and investments. The technical (pertaining to data-specific) and managerial (analytical and business acumen) skills include human resources. The intangible resources indicate organization culture and learning abilities, which include data-driven approaches and Knowledge-Management Systems. Few studies (Sun et al. 2020 ; Verma and Bhattacharyya 2017 ) have adopted the TOE framework to categorize the ‘Data Science’ components under the context of technology (resources, competence, complexity, compatibility, and relative advantage), organization (Firm size, perceived costs, and organizational support), and environment (competition, partner readiness, industry type, and regulations). Few more studies are carried out under the Motivation-Opportunity-Ability (MOA) theoretical framework (Wang et al. 2018c ). Motivation comprises of perceived ease and usefulness, external pressure, corporate culture and leadership support. Regulatory mechanisms, policies, information level, and associated risk define the opportunity aspect. The ability aspect points toward management capability, data talent, and infrastructure. Considering the merits of all these theoretical foundations and corresponding literature, in this SLR, the factors are synthesized (Fig. 6 ) into the following themes: 'Content' referring to Data Characteristics and Data Governance; 'Context' in technology and environmental aspects; 'Intent' toward aligning with the core strategy of the organization, managerial willingness, organizational agility, leadership, cultural aspects; and 'Outcome' as a business value in terms of 'Key Performance Indicators' (KPIs) and 'Return on Investments’ (RoI).

Synthesized factors associated with ‘Data Science Strategy’ in organizations

4.2.1 Data characteristics

In the successful implementation of ‘Data Science’ in an organization, data characteristics can add value to the firm. Transforming these data characteristics into a valuable proposition relies upon the firms’ understanding of these characteristics and how to deliver value through their use (Wright et al. 2019 ). Ranjan ( 2019 ) analyzed the problems presented by data characteristics under the context of 10 Vs—Volume, Variety, Velocity, Veracity, Variability, Validity, Visualization, Vulnerability, Volatility, and Value. Whereas Ghasemaghaei et al. ( 2018 ) and Wamba et al. ( 2015 ) characterized data using 7Vs, namely Volume, Velocity, Variety, Veracity, Value, Variability, and Visualization. Each of these characteristics is important under different contexts including the industry sector. It is necessary to keep data as driver. Keeping data as driver demands certain business processes to be able to effectively use the data (de Medeiros et al. 2020 ). These business processes and procedures used in collecting and analyzing ‘data’ take the implementation of ‘Data Science Strategy’ further ahead (Rialti et al. 2019 ).

The characteristic of an incidence reflecting the raw facts defines the data quality. The data quality comprises of correctness, completeness, relevancy, timeliness, clarity, consistency, ease of understanding, and accessibility. It greatly affects the results of ‘Data Science’ (Ghasemaghaei et al. 2018 ). The application of ‘Data Science’ would positively influence the business value, once data quality challenges are addressed (Wamba et al. 2019 ). Intention to adopt ‘Data Science’ is positively affected by data quality and an understanding of the benefits of data.

4.2.2 Governance

For warranting the data quality and leveraging its value, data governance is a potential approach (Gregory 2011 ; Grover et al. 2018 ; Mir et al. 2020 ; Braganza et al. 2017 ; Wiener et al. 2020 ). Governance includes areas such as organizational strategy, data quality & security, innovation applications, ‘Data Science’ architecture, and lifecycle (Fakhri et al. 2020 ). Data policies are deployed by organizations to support product innovation (Lacam 2020 ).

The data governance aims at adding value to the enterprise through risk mitigation plans and helps achieving compliance (Gregory 2011 ). Bertot et al. ( 2014 ) highlight the need to develop a data governance model with respect to ‘Data Science’ in the context of privacy, reuse, accuracy, archival, curation, platforms, architecture, and sharing policies across sectors. In the finance sector, in protecting the consumer rights and interests, it is conducive to regulate the use of personal information (Li & Yu 2020 ). The magnitude of influence of data security, privacy, and accuracy are in descending order on ‘Data Science’ adoption and are 54%, 36%, and 8%, respectively in the total measurement (Latif et al. 2019 ). Effective data exchange within and among network partners is a necessity to benefit from ‘Data Science’. Due to security and privacy challenges, the organizations can be hesitant in sharing data with partners (Günther et al. 2017 ). To establish close connections and harmonization with partners there needs to be balanced and controlled sharing of data (Coombs et al. 2020 ; Sivarajah et al. 2017 ). There exists a positive interplay between governance of BDA infrastructure and BDA capabilities (Bertello et al. 2020 ).

4.2.3 Technology

Over the last few years, immense progress is observed in the technology related to ‘Data Science’ (Gupta and George 2016 ). The ‘Data Science’ concept emphasizes effective deployment of technology and talent to capture and manage data in order to generate insights (Mikalef et al. 2020b ). ‘Data Science’ technology capability includes infrastructure and human talent.

The core themes of technology capability in terms of infrastructure are connectivity, compatibility, and modularity (Akter et al. 2016 ). In import and export business enterprises, with the introduction of a new IT system, it is necessary to build an internal management system to improve operational efficiencies (Zhang 2021 ). Investing in infrastructure, such as data lake, analytics portfolio, and human talent generates business value (Grover et al. 2018 ). Bendre and Thool ( 2016 ) have highlighted the use of ‘Data Science’ in different domains and emphasized the need for technological platforms covering lifecycle management including data acquisition, processing and visualization challenges. The challenges arising from data characteristics in computing methods require different strategies and advanced techniques including technology usage on data segregation and accumulation, high-performance computing, incremental learning, scalability, and heuristics (Choi et al. 2018 ; Wang et al. 2018b ).

Amongst large and diverse firms, it is observed that investing in ‘Data Science’ which is IT-intensive and with a highly competitive nature leads to improvement in productivity (Müller et al. 2018 ). Many organizations have transitioned from traditional analytics (Analytics 1.0—business intelligence) to ‘Big Data Analytics’ (Analytics 2.0). Using the ‘Data Science’ platform that can combine the traditional analytics and ‘Big Data’ organizations are transitioning to a new synthesis, known as the Analytics 3.0- data enriched offering (Davenport 2013 ; Harlow 2018 ).

Human talent is arguably the most important and critical element in leveraging ‘Data Science’ investments (Grover et al. 2018 ; Wamba et al. 2015 ). Talent capability includes technical, relational, and business knowledge, along with the ability to manage technology (Akter et al. 2016 ). The challenges in talent management include cognitive and behavioural limitations, and lack of educational programs for the development of both analytical and communication skills with regards to translating data into business insights (de Medeiros et al. 2020 ).

The implementation of a ‘Data Science’ value chain is driven by overall business directions and strategy, including the knowledge management strategy. In this context, ‘knowledge management’ refers to the process of sharing internally held information in an easy and systematic approach for the benefit of the organization. Developing a strategic intent and knowledge management system for ‘Data Science’ will lead to a long-term sustainable competitive advantage by allowing enterprises to implement new business models and develop large-scale ability to experiment (Harlow 2018 ). Relative performance in firms that develop more ‘Data Science’ capabilities than others is higher. Further, knowledge management orientation plays a significant role in amplifying the effect of ‘Data Science’ capabilities (Ferraris et al. 2019 ). Knowledge management capabilities and organizational ambidexterity (exploration and exploitation) influence the relationship between ‘Data Science’ and strategic flexibility (Rialti et al. 2020 ). The synergy between organizational resources and capabilities in complementing ‘Data Science’, improves organizational objectives and outcomes (Wang et al. 2019 ). Poeppelbuss et al. ( 2011 ) studied maturity models describing organizational capabilities from the research, publication and practitioner’s perspectives. Grossman ( 2018 ) introduced the Analytic Processes Maturity Model (APMM) framework to evaluate the analytic maturity of an organization.

4.2.4 Business environment

The business environment reflects industry characteristics and government regulations. The industry is characterized by the firms' partners and competitors, and the government policies influence the macroeconomics context and the regulations (Sun et al. 2020 ). A favourable industry and regulatory environment (infrastructure, legal, regulations, directives, support, competitive pressure, and trading partner readiness) positively affects the ‘Data Science’ adoption intent (Sun et al. 2020 ; Wiener et al. 2020 ). Dubey et al. ( 2019b ) developed and tested the model describing the relation between resources, talent, culture, cost, and operational performance with institutional pressures (coercive, normative, and mimetic).

To successfully adopt ‘Data Science’ in the organizational context, firms should take the internal teams onboard the digital transformation journey, while reinforcing strategic initiatives and influencing employee behaviour towards their successful implementation (Boldosova 2019 ). In addition to its technical capacity, the power dynamics within a firm and its competitive landscape play a significant role in ‘Data Science’ build-up (Jha et al. 2020 ). An organization’s relationships with its stakeholders, including the public sector, and assimilation of social media facilitate organizational learning (Okwechime et al. 2018 ). This further leads to the absorption of ‘Data Science’ technologies (Bharati and Chaudhury 2019 ). Investment in ‘Data Science’ not only affects firms’ equilibrium price, market share, and profit but also the rivals’ performances (Wu et al. 2017 ). With ‘Data Science’ investments, market outcomes also vary based on the competition strategy used, either conservative or expansive. Depending on a fully covered or partially covered market structure, the impact on firms’ competition varies when consumer’s preference of ‘Data Science Strategy’ is heterogeneous.

There are limited studies exploring the factors for adoption of ‘Data Science’ in the start-up environment. These studies bring to light the fact that the key factors affecting adoption are different from established firms (Behl et al. 2019 ). Different enablers of ‘Data Science’ in start-ups are technical support from the vendors, attitude of the top management, competitive environment, infrastructure, skill enhancement, data access, quality of data, and perceived usefulness. Higher performance can be derived from firms when the Strategic Factor Market (SFM) is imperfect due to the differences in the expectation of the future value of strategic resources. The Information Management Capability (IMC) allows the firm to manage market needs and directions in line with core strategy (Macada et al. 2019 ).

4.2.5 Alignment with core strategy

‘Digital strategy’ being the superset of ‘Data Science Strategy’ needs to be aligned with the core organizational strategy. Most ‘digital strategies’ fail because they fail to re-imagine the vision of the organization and the journey of digitization. Alignment of digital strategy with core business strategy needs to be accompanied by the way the organization drives its vision (Trompenaars and Woolliams 2016 ). The digital strategy that an organization should embrace needs to be unique and be difficult enough for the competitors to replicate (Ross et al. 2017 ). Managerial and operational capabilities are necessary for realizing a digital business strategy (Ukko et al. 2019 ).

Adopting ‘Data Science Strategy’ in an organization does not mandatorily require building a new strategy. Rather, it requires a coherent alignment with the planned business objectives (Keding 2020 ). While formulating long-term business strategy, the inclusion of ‘Data Science Strategy’ facilitates business alignment and eventually leads to success (Grover et al. 2018 ).

Successful deployment of ‘Data Science Strategy’ requires good collaboration and alignment between the IT and business departments. This collaboration and alignment require diverse mechanisms of organizational governance (Dremel et al. 2017 ). Several stakeholders within and outside the organization mutually benefit either by combining and/or sharing data (Günther et al. 2017 ). Hence, it is important to align the ‘Data Science Strategy’ of an organization with that of core strategy (Biazzin and Castro-Carvalho 2019 ).

4.2.6 Managerial willingness

Behavioural intention to use ‘Data Science’ in organizations is determined by outcome expectations, social perception, enabling conditions, and resistance to change. Though ‘Data Science’ is perceived to be difficult (effort expectation), the influence of this perception on ‘Data Science’ adoption intention is small. The ‘Data Science’ adoption intention is contained to the relationship between facilitation conditions on behavioural intention (Cabrera-Sánchez and Villarejo-Ramos 2019 ). Based on the understanding of ‘Data Science’, the record of its success, and the logical reasoning provided by the technology itself, the managers' willingness to trust ‘Data Science’ varies (Keding 2020 ). Instead of working in business units in silos, leveraging ‘Data Science’ as a horizontal facilitator leads to an increased awareness at managerial levels. There is a different level of acceptance of ‘Data Science’ based on hierarchical layers in an organization. By customized design of algorithms, the acceptance level can be increased.

Leadership team support is a necessary ingredient in adopting ‘Data Science’. It provides the needed resources by actively promoting, endorsing, and fostering its use. Also, this helps manage change and remove organizational barriers related to Data Science usage (Lautenbach et al. 2017 ). An organization’s ‘Data Science’ adoption decision can be greatly influenced by highly motivated top managers inclined towards innovations. This happens because such personnel can only provide strategic direction, authority, and resources (Sun et al. 2020 ).

4.2.7 Organizational agility

Agility positively mediates the relationship between ‘Data Science’ and firm performance (Rialti et al. 2019 ). Organizational agility must be developed in order to contribute to the emergence of an overall ‘Data Science’ capability (Mikalef and Gupta 2021 ). Agility is often connected to an organization's dynamic capabilities (Rialti et al. 2019 ). For all the business processes including product development, resource allocation, knowledge creation, and/or ‘Data Science’ cycle, there is an association of organizational dynamic capabilities (Božič and Dimovski, 2020 ; Braganza et al. 2017 ). In a highly dynamic business environment, adopting ‘Data Science’ creates competitive advantage (Sun et al. 2020 ).

Work practices need to be realigned continuously to gain from ‘Data Science’ (Günther et al. 2017 ). At the work-practice level, organizations work with ‘Data Science’ in inductive (bottom-up) and deductive (top-down) approaches. For ‘Data Science’ success, both these approaches need to intertwine and complement each other. The intensity of organizational learning is one of the critical intangible resources needed to build ‘Data Science’ capability (Gupta and George 2016 ; Mikalef et al. 2020b ).

4.2.8 Leadership and culture

Cultural differences are a prime aspect in firms' managers' willingness for and resistance to ‘Data Science’ adoption (Keding 2020 ). Leadership with a clear ‘Data Science Strategy’ and vision can articulate the business cases that are likely to succeed (Grover et al. 2018 ).

In capitalizing on ‘Data Science’ capabilities, firms need power shift in the organization structure. The new teams of data and analytics need support from top management teams and also an inclusive data-based decision process (GalbRaith 2014 ). Behavioural change too is required in order to make data-driven decision and stimulating organizational readiness for capability restructuring is the need of the organizations (de Medeiros et al. 2020 ).

Along with increasing proficiency in sustainable design, 'Data Science' capability also directly enhances sustainable growth and performance (Zhang et al. 2020 ). ‘Data Science’ increases sustainable innovativeness in organizations as a measure of successful new product development (Song et al. 2020 ). For sustainable development, assessing the readiness of organizations to adopt 'Data Science' on the temporal dimension is important (Olszak and Mach-Król 2018 ).

As an antecedent of agility, ambidexterity improves organizations’ ability to respond effectively to market changes (Rialti et al. 2019 ). Ambidextrous organizational culture acts as a mediator between ‘Data Science’ capabilities and firm performance. Exploration orientation of a firm has a positive effect on ‘Data Science’ leading to long-term results such as generation of new product revenues (Johnson et al. 2017 ). Organizations can choose strategic pathways based on an assessment of their technical (exploiting suppliers) and analytical (exploring data) capabilities (Najjar and Kettinger 2013 ). ‘Data Science’ capability has a positive and significant effect on innovative capabilities, both incremental and radical. Moderation effect by information governance is significant on radical innovation than on incremental innovation (Mikalef et al. 2020a ). Increased exploration and exploitation capabilities of organizations may be considered related to implementing a 'Data Science capable’ business process management system within ambidextrous organizations (Rialti et al. 2018 ). Firms can realize organizational creativity and performance by fostering ‘Data Science’ (Mikalef and Gupta 2021 ). Organizational ambidexterity, which is data-driven with real-time responsiveness, increases firms’ ability toward new market opportunities.

4.2.9 Business value

‘Data Science’ has the potential to provide companies with high business value in terms of financial/market performance or customer satisfaction (Raguseo and Vitari 2018 ). The outcome of the 'Data Science' project lies in realizing business value, which could be transactional, strategic, and/or transformational (Ji-fan Ren et al. 2017 ). In enhancing business value, system quality and information quality are key factors (Ji-fan Ren et al. 2017 ). The strategic value of ‘Data Science’ could be functional (tangible: financial performance, market performance) and/or symbolic (intangible: reputation, brand image) (Grover et al. 2018 ). Based on industry characteristics and business goals, organizations need to have measures of Key Performance Indicators (KPIs) and RoIs.

The KPIs typically would involve data governance, decision quality, process efficiency, innovation contributions, business expansion, and stakeholder satisfaction (Chakravorty 2020 ; Grover et al. 2018 ). It is equally important to track the intermediate indicators, such as stakeholder sentiment and engagement, along with co-creation and value sharing (Libert et al. 2016 ). RoI refers to revenue, profitability, return on assets, share value, reduced costs, design-cycle optimization, symbolic value (image, reputation, first-mover), forecasting, and prediction (Grover et al. 2018 ; Shim et al. 2015 ). Early adopters of’Data Science', focused on the execution of projects as they knew that they had value to offer (Shim et al. 2015 ). With the integration of 'Data Science' into the mainstream, organizations need to devise the mechanism to substantiate the investments. To derive competitive advantage and add business value to organizations, ‘Data Science’ needs to be an integral part of the entire digital transformation journey and each organization needs a distinct ‘Data Science Strategy’ (DSS).

4.3 Conceptual framework of ‘Enablers and Barriers’ of successful ‘Data Science Strategy’

The SLR summarizes the status of research in the field of ‘Data Science Strategy’. Considering the fact that there is little effort so far in providing an eagle’s eye view on research in ‘Data Science’ encompassing the domains of ‘Big Data’, ‘Artificial Intelligence’, ‘BI&A’, and relevant technologies, this effort is a considerable leap in synthesizing all the enablers and barriers of ‘Data Science Strategy’ adoption in organizations. We propose a conceptual framework for developing and implementing successful ‘Data Science Strategy’ for enterprises (Refer Fig. 7 ).

Conceptual framework for enterprise ‘Data Science Strategy’

The synthesized ‘Data Science’ components under the themes 'Content', 'Context', 'Intent' and 'Outcome' led to the development of a conceptual framework influenced by the 4I model by Crossan et al. ( 1999 ). The 4Is: Intuition, Interpretation, Integration and Institutionalization, take place across individual and organizational levels. Okwechime et al. ( 2018 ) have made use of the 4I model in deploying and integrating ‘Big Data’ and ‘Smart Cities’ from the organizational learning perspective. Mandlik and Kadirov ( 2018 ) studied the ‘Big Data’ ecosystem at the micro-level (Individual behavior), meso-level (Network of firms), and the macro-level (Institutional, Socio-political). The conceptual framework proposed for ‘Data Science’ success, connects the construct ‘Resources’ as the business input leading to ‘Strategic Business Value’ as the outcome. This transformation of input to outcome is driven by the enterprise ‘Data Science Strategy’. The strategy rests on the balancing of two competing constituents of the same continuum, which are ‘Barriers’ and ‘Enablers’ of ‘Data Science’ success. Drawn upon the Stimulus-Organism-Response (S-O-R) model (Mehrabian and Russell 1974 ), Contingency model (Fiedler 1964 ), and Institutional theory (Meyer and Rowan 1977 ), the constituents ‘Barriers’ and ‘Enablers’ are grouped under the themes Individual, Organizational and Institutional. The S-O-R model describes the influence of factors affecting individual, organizational and institutional behaviors that translate inputs in to business outcomes. The contingency model is appropriate in defining the optimal approach to balance the barriers and enablers of ‘Data Science Strategy’. The reference to the institutional theory is appropriate in emphasizing the role of institutional setup beyond the limited individual and organizational functional considerations.

At the individual-level, the tendency of resistance to ‘Data Science’ project execution either by an employee or by a mid-level manager is either due to fear of ‘failure’ or ‘loss of control’ or ‘operational disruption’ (Mikalef et al. 2020c ; Shahbaz et al. 2019 ). The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al. 2020 ; Behl et al. 2019 ; Chaurasia and Verma 2020 ; Foshay et al. 2015 ; Halaweh and Massry 2015 ; Lai et al. 2018 ; Lamba and Singh 2018 ; Lautenbach et al. 2017 ; Popovič et al. 2018 ; Ransbotham et al. 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al. 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success. Creating opportunities to interact with leadership team and adopting a deliberate storytelling technique (Boldosova 2019 ) would be helpful in overcoming the communication gap barriers. There is a significant effect of communicating to internal teams with deliberate storytelling, and reinforcing the strategic initiatives. This in turn influences employee behaviour towards ‘Data Science’ initiatives.

At organization-level barriers for ‘Data Science’ success range from data-related challenges to huge investment requirements to internal politics. Establishing ‘Data Science’ projects require huge investments on skills and infrastructure (Behl et al. 2019 ; De Luca et al. 2020 ; Holland et al. 2020 ; Lee et al. 2017 ; Wu et al. 2017 ), which is quite a big challenge for most organizations. Though it may not be avoided completely, infrastructure flexibility in identifying and using of compatible and complementary resources (Alaskar et al. 2020 ; Chaurasia and Verma 2020 ; Mikalef and Gupta 2021 ; Moreno et al. 2019 ; Shokouhyar et al. 2020 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ) already existing in the organization can considerably reduce the burden on new investments. Lack of skills and knowledge (Ahmed et al. 2018 ; Behl et al. 2019 ; Dubey et al. 2019b ; Lamba and Singh 2018 ; Foshay et al. 2015 ; GalbRaith 2014 ; Mikalef et al. 2020a , 2019b , 2020c ; Rialti et al. 2019 ) required to execute the ‘Data Science’ projects can be addressed by setting up ‘Training & Knowledge Management’ capabilities and processes (Calvard 2016 ; Dam et al. 2019 ; Ferraris et al. 2019 ; Harlow 2018 ; Rialti et al. 2020 ). Acquiring and retaining the right talent in the field of ‘Data Science’ is another challenge (Holland et al. 2020 ; Ransbotham et al. 2017 ) and needs to be addressed by rewarding and recognizing (GalbRaith 2014 ) the contributions in regular and timely manner.

The divergent actions towards ‘Data Science’ projects by different business units, including IT, in the same organization due to working in silos, and non-alignment (Calvard 2016 ; Foshay et al. 2015 ; Walker and Brown 2019 ) to business objectives are barriers for success. “Data Science Strategy’ must be aligned (Biazzin and Castro-Carvalho 2019 ; Comuzzi and Patel 2016 ; GalbRaith 2014 ; Ross et al. 2017 ; Mithas et al. 2013 ; Moreno et al. 2019 ; Trompenaars and Woolliams 2016 ; Ukko et al. 2019 ; Zaki, 2019 ) with ‘Digital Business Strategy’ which, in turn, should be aligned with ‘Organizational Strategy’. Further, there has to be seamless collaboration across the organization (Kache and Seuring 2017 ) to enable desired outcomes. Organizational inertness (Biazzin and Castro-Carvalho 2019 ), resistance to organizational flexibility (Ahmed et al. 2018 ; Cabrera-Sánchez and Villarejo-Ramos 2019 ; Dubey et al. 2019a ; Mikalef et al. 2019a , 2020c ; Wang et al. 2018b ) and denial (no support) to experimentation (Alaskar et al. 2020 ; Mikalef et al. 2020c ; Walker and Brown 2019 ) are other sets of organizational barriers. These challenges need to be addressed with a cultural shift within the enterprise. Organizations developing ability to absorb paradigm shifts (Walker and Brown 2019 ), by developing dynamic capabilities, such as dealing with complexity, facilitating reuse, enabling interoperability, client orientation, creating flexibility, adherence to privacy, facilitating communication, impact evaluation, decision-making support, and migration strategy (Gong and Janssen 2021 ) can overcome organizational inertness. Organizational agility (Dam et al. 2019 ; Verhoef et al. 2021 ) in learning and developing ambidextrous culture in exploring and exploiting the new technological advancements with strong data-driven analytics culture will help overcome the resistance to flexibility and lack of experimentation. The intensity of organizational learning is one of the critical intangible resources needed to build ‘Data Science’ capabilities. Barriers in the form of ‘data challenges’ (Brinch et al. 2018 ; Chakravorty 2020 ; Devasia 2018 ; Fakhri et al. 2020 ; Halaweh and Massry 2015 ; Lamba and Singh 2018 ; Lautenbach et al. 2017 ; Mikalef et al. 2020a ; Shahbaz et al. 2019 ; Saldžiūnas and Skyrius 2017 ; Sivarajah et al. 2017 ; Zaki 2019 ), such as collection, extraction, relevance, refinement, handling, ownership, documentation, communication, management, quality, trust, security, and privacy must be dealt with utmost care and by making deliberate use of characteristics of data (Behl et al. 2019 ; Choi et al. 2018 ; Ranjan 2019 ; Lacam 2020 ; Rialti et al. 2018 ; Sarker et al. 2019 ; Schroeder 2016 ; Wright et al. 2019 ; Yadav 2017 ). A proper data and information governance model (Bertot et al. 2014 ; Chakravorty 2020 ; Fakhri et al. 2020 ; Foshay et al. 2015 ; Gregory 2011 ; Mikalef et al. 2020a ; Wang et al. 2018a ) would help overcome these challenges to a significant effect. The intra-firm power dynamics (Jha et al. 2020 ; Schroeder 2016 ) also play a significant role in the success of ‘Data Science’ projects. The adverse effects must be handled in organizations by encouraging formal and informal network creation amongst the management team and ‘Data Science’ teams.

Barriers at the institutional-level are equally important to be addressed as much as individual and organizational-level in the success of ‘Data Science’ projects. Institutional pressures namely coercive, normative, and mimetic (Dubey et al. 2019b ) can act both as barriers and enablers based on the context and industry characteristics. Awareness of the institutional pressures help in the selection of tangible (infrastructure), intangible (culture), and human resources (‘Big Data’ skills). The competing business models with lack of transparency require compliance requirements (Lautenbach et al. 2017 ; Mikalef et al. 2020c ) to be established by industry-level collaborations along with a clear ‘target market’ definition (Holland et al. 2020 ). Creating effective institutional arrangement in facilitating innovative technologies enables organizations to benefit from ‘Data Science’ (Sang et al. 2020 ). External market factors (Lautenbach et al. 2017 ) adversely affecting the ‘Data Science’ projects need to be dealt by industry-government engagement by formulating a national-level strategy for the ‘Data Science’ domain. Information Management Capability (IMC) of an organization plays an important role in effectively handling external market factors (Macada et al, 2019 ). Lack of competency and support by vendors and alignment between client and service provider (Behl et al. 2019 ; Walker and Brown 2019 ) needs institutional arrangement in addressing the issue. There is also need to create a competence pool in the domain of ‘Data Science’. Dearth of adequate talent in the core domains, along with ‘Data Science’ skillsets (Akter et al. 2016 ; Chatterjee 2020 ; Devasia 2018 ; Halaweh and Massry 2015 ; Jha et al. 2020 ; Kache and Seuring 2017 ; Lautenbach et al. 2017 ; Mikalef et al. 2019a ; Ransbotham et al. 2017 ; Wang et al. 2018c ), is another challenge that organizations face in implementing ‘Data Science’. Collaboration between industry and academia on ‘Data Science’ topics would lead to the development of skilled talent during university education. This would help to address the above-mentioned institutional concerns to a considerable extent.

The proposed conceptual framework was validated and refined through an iterative feedback process based on expert opinions. The conceptual model was shared with ten different experts leading ‘Data Science’ initiatives in their organizations at senior executive levels. These organizations work in the Retail, Media, Healthcare, Fintech, Manufacturing, Automobile, Aerospace, and Telecommunications sectors. Their opinions were given due consideration and the model was appropriately modified.

5 Discussion

In adopting ‘Data Science’, organizational approaches can be either deductive (top-down) or inductive (bottom-up). The traditional approach is the top-down approach where the business problem is defined first followed by a search for the required data. With this approach, there could be challenges with the accessibility of data and a feeble understanding of data collection methods and processes. Unless there exists high data analysis competency in the organization, this approach could be risky. With limited exposure to ‘Data Science’, a bottom-up approach is suggested where available data is first analyzed, and further, the business problem is accordingly defined based. This approach would have the following steps: (i) control of data hygiene, (ii) staff training, (iii) data value estimation, (iv) development of new dimensions for data sets, (v) evaluation of data analysis consequences, and (vi) use of insights (Saldžiūnas and Skyrius 2017 ). On the contrary, Lautenbach et al. ( 2017 ) suggest organizations drive BI&A usage with the top-down approach. The leadership team must keep the employees informed of the value and benefits derived through BI&A use (Lautenbach et al. 2017 ).

‘Data Science Strategy’ by its very nature leads to asymmetric information in the technology market (Mandlik and Kadirov 2018 ). In equity markets, information on firms’ investment in ‘Data Science’ leads to positive stock market reactions. Moreover, investments in small vendors tend to bring in higher returns than big vendors. Also, investors assess ‘Data Science’ investments of big firms as compared to those of small firms (Lee et al. 2017 ). There is a different impact on the size of returns depending on firm size and vendor size. Reliability, ROI, real-time analytics must be kept in mind while dealing with ‘Data Science’ adoption from a business perspective (Yadav 2017 ).

This SLR provides an eagle’s eye view of enablers and challenges of adopting ‘Data Science’ in organizations. In harnessing the power of ‘Data Science’, policies are required that are collaborative and complementary across organizations in creating a stakeholder marketplace, facilitating the generation of annotated data sets, spreading awareness relating to the contribution of AI, and supporting the start-ups (Chatterjee 2020 ).

5.1 Theoretical contributions

The article makes quite a significant contribution to theory in the field of ‘Data Science Strategy’.

This SLR recognizes that prior studies have focused upon different aspects of ‘Data Science’ adoption in organizations predominantly focusing upon ‘Big Data Analytics’ and ‘Artificial Intelligence’. The dearth of studies on ‘Data Science’ as an umbrella term that cuts across different technologies, such as Big Data, Data Analytics, AI, ML, Deep Learning (DL), Visualization, and Business Intelligence (BI) is evident. This SLR has reviewed the most relevant articles and synthesized the knowledge on ‘Data Science’ strategies. The findings and synthesized components are further developed into a conceptual framework. Developing this conceptual framework is the main theoretical contribution of this article. The findings, therefore, contribute to answering the research question ‘What are the enablers and challenges that affect the strategic implementation and success of ‘Data Science’ (AI/ML/BD) in organizations?’ and fulfil a significant gap in the literature.

The existing literature on ‘Big Data Analytics’ and ‘Artificial Intelligence’ draws upon strong theoretical backgrounds (Refer Table S2). ‘Data Science’ adoption in organizations has extensive opportunity in generating novel theory, along with new management practices. This article contributes to the theories by drawing upon Stimulus-Organism-Response (S–O-R) model, Contingency model, and Institutional theory in formulation of the proposed conceptual framework.

The proposed framework sets the agenda for further research in ‘Data Science’ strategy. This SLR article could serve as reference for practitioners as well as research scholars. Through the proposed framework, literature can be further enriched by developing the constructs grouped under ‘barriers’ and ‘enablers’, and developing them into propositions and hypotheses. Research contributions towards validating these propositions and/or hypotheses using appropriate qualitative and quantitative techniques can add to the knowledge body.

5.2 Managerial implications

Managers need to note that ‘Data Science’ is a part of the wider ecosystem including ‘Big Data’, ‘Data Analytics’, ‘Machine Learning’, ‘Artificial Intelligence’, and ‘Business Intelligence’. It also provides significant benefits over traditional analytics (Business Intelligence) systems. The considerable effect of ‘Data Science’ on the workforce is quite eminent in the next few years both at the organizational and the personal levels.

The proposed framework of ‘Data Science Strategy’ can help organizations from various industries and domains in taking the right steps towards the successful implementation of ‘Data Science’ projects. We summarize the implications to managers below.

Every enterprise’s ‘Data Science’ journey should start with the right business questions. Be sure of a clear business strategy and an expected value that the ‘Data Science Strategy’ can relate to. In answering the business questions pertaining to ‘Data Science’, choose the strategic choices necessary to drive the ‘Data Science’ transformation forward with resources, such as Data, Technology, Infrastructure and Human talent (skills and knowledge). Without business-appropriate choice of these resources, the desired outcome would hardly be achieved.

For ‘Data Science’ project to successfully lead to business outcomes, managers need to be cognizant of the barriers. Though it may not be possible to completely eliminate them, they can be minimized. The proposed framework summarizes the comprehensive list of barriers that managers need to keep their focus upon. Prior analyses of these barriers much before implementation of ‘Data Science’, would help in the planning and distribution of resources. The barriers summarized in the framework are a result of our review on multiple industry sectors and domains. We believe many organizations would almost face all or few of these barriers. Depending on the specific industry and domain, specifically applicable barriers should be considered by the managers (also refer to Supplementary Table S1). In summary, good overview of barriers at the early stages of planning and implementation of ‘Data Science’ projects will help managers to overcome hindrances and realize expected outcomes.

Along with barriers to ‘Data Science’, the proposed framework also summarizes the comprehensive list of enablers which intend to minimize the barriers. Enablers and barriers of ‘Data Science’ projects are inversely related. The summary of enablers for various industry sectors and domains is presented in Supplementary Table S2. Detailed prior research on all the enablers and specific attention to the ones relevant to their industry would help them to optimize the resources and accelerate the ‘Data Science’ journey to realizing ‘business value’.

The outcomes of ‘Data Science’ projects can be either functional or symbolic. Organizations must focus upon clearly targeted outcomes which can be measured using process KPIs and business value in terms of profits and cost savings (RoI). The value in the form of competitive advantage and structural transformation must also be monitored as indicated in the framework. Managers must also recognize the importance of ‘Data Science’ adoption in the form of symbolic value add. As a symbolic business value, ‘Data Science’ adds to the organizations’ brand value, and recognition as technology-driver. ‘Data Science’ projects can also enhance and lead social, environmental and corporate friendly initiatives within the organization. Managers must realize that the business outcomes in most ‘Data Science’ project initiatives are iterative and prone to initial hiccups. Awareness and balance of barriers and enablers from the proposed framework will help to realize the desired results in a stipulated time span. Therefore, early-stage setbacks on ‘Data Science’ projects should not deter the purpose of successful utilization of ‘Data Science’. Despite the mentioned barriers, managers in organizations need to keep up their motivations and convince the stakeholders (sub-ordinates, partners, vendors and management teams), and count on the ‘Data Science’ enablers.

Knowledge acquired from the learnings of the proposed framework in the form of resources, barriers, enablers, and strategic business value would help organizations in adopting ‘Data Science’ and exploit new business opportunities.

6 Limitations and future research

We acknowledge the limitations of our study and the readers should interpret the content of this SLR article in the context of these limitations. Primarily, the SLR has been reliant on accessible research articles from four databases, namely, EBSCO Business Source Ultimate, Scopus, Science Direct, and Pro-Quest databases, in English language. Though we have conducted SLR by identifying all possible articles relevant, we could have missed some from other leading databases (Web of Science). In addition, the interpretation of findings and synthesis are subjective, a fact which we (authors) have attempted to overcome by examining the articles independently. Secondly, amongst the mentioned databases used, the articles are identified based on ‘keyword search’ in the Title, Abstract, and Article keywords sections. We could have failed to consider the relevant articles where the subject area of interest is embedded in the main text. Thirdly, the proposed conceptual framework needs to be developed into an hypotheses and validated with empirical studies.

In addressing the first and second limitations mentioned, it is recommended further to conduct literature reviews from other databases including keyword search throughout the article (not limiting only to Title, Abstract, and Article keywords). To address the third limitation mentioned above, the proposed framework could help future researchers push significant agenda for ‘Data Science Strategy’ research.

We have identified the following future research opportunities:

Developing constructs for each of the constituents in the framework, formulating and empirically validating hypotheses.

As highlighted in motivation for this SLR, the literature articles have focused highly on certain individual components of ‘Data Science Strategy’ in discrete (viz. Big Data Strategy, Big Data Analytics Strategy, Artificial Intelligence Strategy, and BI&A Strategy). Considerably less research attention is paid to other individual components of ‘Data Science strategies’ including data visualization strategies, Machine Learning and Deep Learning strategies in enterprises, and ‘Data Science’ strategies with ‘Small Data’. Data visualization is an important component in persuading the top management stakeholders. Future studies could focus upon examining the ‘Data Science’ strategies in these neglected areas.

A large number of ‘Data Science Strategy’ studies have been dedicated to ‘Big Data Analytics’. Apart from ‘Big Data Analytics’, there also exists relevance for ‘Small Data’ analytics. For many small and medium businesses, ‘Small Data’ also provides great business insights. There is dearth of studies on enablers, barriers and impact of ‘Small Data’ implementation and strategic constituents. Future researchers can focus upon this direction.

Extant literature predominantly examines the enablers and barriers of ‘Data Science’ adoption in certain industry sectors and business domains (viz. Healthcare, Manufacturing, Construction, E-commerce, Telecommunications, Banking and Finance, Automotive, Aerospace, and Entertainment) (Ahmed et al. 2018 ; Dremel et al. 2017 ; Holland et al. 2020 ; Lee et al. 2017 ; Saldžiūnas and Skyrius 2017 ; Yang et al. 2015 ; Wang and Hajli 2017 ; Wamba et al. 2017 ). However, many sectors are yet to be explored (viz. Offline and Online Education, Disaster management) in this context. Researchers could pay attention to these unexplored areas.

Sector-wise comparative analysis between enablers and barriers of ‘Data Science’ Strategy between enterprises has also not been attempted in extant literature. Studies in such direction can bring in key insights which can be used for peer-to-peer learning among industries. Owing to the current Covid-19 pandemic situation, organizations are speeding up their digital transformation journeys. As a result, they are capturing large amounts of data which is unprecedented and new AI use cases are also evolving. The impact of Covid-19 pandemic on the overall ‘Data Science’ ecosystem including strategic dimensions needs investigation.

‘Data Science Strategy’ studies have largely focused on industry sectors in countries such as the USA, India, China and Europe (Refer to Fig. S4). Except India and China, the industries in other Asia–Pacific regions have not been examined extensively. Future studies can be devoted to this aspect. Additionally, market-specific condition combination (developed & developing economies) along with organizational culture (explorative and exploitative, flexibility) can further be explored. The adoption of ‘Data Science’ in internationalization is still an emerging research interest. The organizational variables affecting the relationship between ‘Data Science’ and internationalization could be of interest to future researchers.

With many start-up companies mushrooming, studies exploring the factors associated with ‘Data Science’ adoption and success, and contrasting them with that of established firms is an area can be researched. Further research should be conducted to understand the role of ‘Data Science’ in digital transformation.

There may be scenarios, wherein the Human Resource Management (HRM) dimension has led to a failure in the ‘Data Science’ implementation. This dimension has not been studied extensively. Studies can be conducted specifically on the HRM dimension of the ‘Data Science’ implementation.

Consideration towards growing diversity of consumer preferences ethically is strategically relevant to deploy the ‘Data Science’ business models (Wiener et al. 2020 ). On the contrary, Biazzin and Castro-Carvalho ( 2019 ) studied the impact of buyers’ current behaviour and found it does not significantly impact their deployment. Societal impacts of different ‘Data Science’ strategies should involve the proper implementation of rules and policies to ensure ethical use. This could also be direction for future research.

Studies are also limited on the implementation of ‘Data Science Strategy’ in government sector organizations. As an example, the citizens’ data collected by Government of India through ‘Aadhar’ unique identification number, ‘Aarogya Sethu’, and ‘CoWIN’ can be used for building social welfare and health infrastructure development programs. The benefits and the intertwined challenges with ‘Data Science Strategy’ in the government sector offer further opportunities for research.

7 Conclusion

The SLR aimed at synthesizing the enablers and barriers of 'Data Science Strategy' adoption in different industries, sectors, and business domains. The review conducted an in-depth study of 113 published articles (1998- 2021) sourced from four different databases, namely, EBSCO Business Source Ultimate, Scopus, Science Direct, and Pro-Quest databases, respectively. The findings show that the enablers of ‘Data Science’ and barriers for adoption of ‘Data Science Strategy’ are synthesized. The SLR identified four themes namely ‘Content’ (Data Quality & Governance), ‘Context’ (Technology and Environmental), ‘Intent’ (Culture, Alignment, Agility), and ‘Outcomes’ (Functional and Symbolic business value) that influence the development of ‘Data Science Strategy’ in organizations.

The key lessons learnt are formulated in to a conceptual framework linking ‘Data Science’ resources to business outcomes, influenced by ‘barriers’ and ‘enablers’. In summary, the barriers and enablers are segregated under individual, organizational and institutional contexts. Organizations may not completely be able to eliminate the ‘barriers’, but capitalizing on ‘enablers’ would mitigate the risks of ‘Data Science’ adoption and success of projects leading to desired business outcomes. The study also summarizes the ‘barriers’ and ‘enablers’ of ‘Data Science Strategy’ in organizations depending on industry sectors and domains. Although most of the barriers and enablers might be common across businesses, organizations should prioritize those relevant to their areas of expertise.

The study also emphasizes on the significant role of 'Data Science Strategy' in organizations augmenting 'Data Strategy', 'digitalization' and, eventually 'digital transformation' leading to business success. Research gaps are identified for future attention. The academic and practitioner community should focus their efforts on promoting the development and implementation of a well-crafted, outcome-based, holistic 'Data Science Strategy' so as to reap the benefits of the digital revolution that’s happening globally. Additionally, the onset of Covid-19 pandemic has accentuated the challenges faced by enterprises across various domains which further pushes for a need for a ‘Data Science Strategy’ to aid in the development of a ‘Digital Strategy’ for an organization.

Adrian C, Abdullah R, Atan R, Jusoh YY (2016) Towards developing strategic assessment model for big data implementation: a systematic literature review. Int J Adv Soft Comput Appl 8(3):173–192

Google Scholar

Adrian C, Abdullah R, Atan R, Jusoh YY (2017) Factors influencing to the implementation success of big data analytics: a systematic literature review. In: 2017 International conference on research and innovation in information systems (ICRIIS). IEEE, pp 1–6

Ahmed V, Aziz Z, Tezel A, Riaz Z (2018) Challenges and drivers for data mining in the AEC sector. Eng Constr Archit Manag 25(11):1436–1453. https://doi.org/10.1108/ECAM-01-2018-0035

Article Google Scholar

Akter S, Wamba SF, Gunasekaran A, Dubey R, Childe SJ (2016) How to improve firm performance using big data analytics capability and business strategy alignment? Int J Prod Econ 182:113–131. https://doi.org/10.1016/j.ijpe.2016.08.018

Alaskar TH, Mezghani K, Alsadi AK (2020) Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia. J Decis Syst. https://doi.org/10.1080/12460125.2020.1859714

Ali Q, Salman A, Yaacob H, Zaini Z, Abdullah R (2020) Does big data analytics enhance sustainability and financial performance. The case of ASEAN banks. J Asian Finance Econ Bus 7(7):1–13. https://doi.org/10.13106/jafeb.2020.vol7.no7.001

Al-Sai ZA, Abdullah R, Husin MH (2020) Critical success factors for big data: a systematic literature review. IEEE Access 8:118940–118956. https://doi.org/10.1109/access.2020.3005461

Alsheiabni S, Messom C, Cheung Y, Alhosni M (2020) Winning AI strategy: six-steps to create value from artificial intelligence. AMCIS 2020 Proceedings. 1. https://aisel.aisnet.org/amcis2020/adv_info_systems_research/adv_info_systems_research/1

Arunachalam D, Kumar N, Kawalek JP (2018) Understanding big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice. Trans Res E Log Trans Rev 114:416–436. https://doi.org/10.1016/j.tre.2017.04.001

Becker W, Schmid O (2020) The right digital strategy for your business: an empirical analysis of the design and implementation of digital strategies in SMEs and LSEs. Bus Res. https://doi.org/10.1007/s40685-020-00124-y

Behl A, Dutta P, Lessmann S, Dwivedi YK, Kar S (2019) A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach. IseB 17(2):285–318. https://doi.org/10.1007/s10257-019-00452-5

Bendre MR, Thool VR (2016) Analytics, challenges and applications in big data environment: a survey. J Manage Anal 3(3):206–239. https://doi.org/10.1080/23270012.2016.1186578

Bertello A, Ferraris A, Bresciani S, De Bernardi P (2020) Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities. J Manage Governance. https://doi.org/10.1007/s10997-020-09542-w

Bertot JC, Gorham U, Jaeger PT, Sarin LC, Choi H (2014) Big data, open government and e-government: issues, policies and recommendations. Inform Polity 19(1,2):5–16. https://doi.org/10.3233/IP-140328

Bharati P, Chaudhury A (2019) Assimilation of big data innovation: investigating the roles of IT, social media, and relational capital. Inf Syst Front 21(6):1357–1368

Biazzin C, Castro-Carvalho L (2019) Big data in procurement: the role of people behavior and organization alignment. Dimensión Empresarial 17(4):10–28

Bogdan M, Lungescu DC (2018) Is strategic management ready for big data? A review of the big data analytics literature in management research. Manage Chall Contemp Soc Proc 11(2):65

Boldosova V (2019) Deliberate storytelling in big data analytics adoption. Inf Syst J 29(6):1126–1152. https://doi.org/10.1111/isj.12244

Borges AF, Laurindo FJ, Spínola MM, Gonçalves RF, Mattos CA (2021) The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. Int J Inf Manage 57(2021):102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225

Božič K, Dimovski V (2020) The relationship between business intelligence and analytics use and organizational absorptive capacity: applying the DeLone & mclean information systems success model. Econ Bus Rev Central South-Eastern Eur 22(2):191–232. https://doi.org/10.15458/ebr99

Braganza A, Brooks L, Nepelski D, Ali M, Moro R (2017) Resource management in big data initiatives: processes and dynamic capabilities. J Bus Res 70:328–337. https://doi.org/10.1016/j.jbusres.2016.08.006

Brinch M, Stentoft J, Jensen JK, Rajkumar C (2018) Practitioners understanding of big data and its applications in supply chain management. Int J Log Manage 29(2):555–574. https://doi.org/10.1108/IJLM-05-2017-0115

Bughin J (2018) Wait and see could be a costly AI Strategy. MIT Sloan Management Review. https://www.kungfu.ai/wp-content/uploads/2019/01/Wait-and-See-Could-Be-a-Costly-AI-Strategy.pdf

Cabrera-Sánchez JP, Villarejo-Ramos AF (2019) Factors affecting the adoption of big data analytics in companies. Revista De Administração De Empresas 59(6):415–429

Calvard TS (2016) Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity. Manag Learn 47(1):65–82. https://doi.org/10.1177/1350507615592113

Cao G, Duan Y (2017) How do top-and bottom-performing companies differ in using business analytics? J Enterp Inf Manag 30(6):874–892. https://doi.org/10.1108/JEIM-04-2016-0080

Chakravorty R (2020) Common challenges of data governance. J Securit Oper Custody 13(1):23–43

Chatterjee S (2020) AI strategy of India: policy framework, adoption challenges and actions for government. Transf Govern People Process Policy 14(5):757–775. https://doi.org/10.1108/TG-05-2019-0031

Chaurasia SS, Verma S (2020) Strategic determinants of big data analytics in the AEC sector: a multi-perspective framework. Constr Econ Build 20(4):63–81

Chen Y, Banerjee A (2020) Improving the digital health of the workforce in the COVID-19 context: an opportunity to future-proof medical training. Future Healthcare J 7(3):189. https://doi.org/10.7861/fhj.2020-0162

Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188. https://doi.org/10.2307/41703503

Choi TM, Wallace SW, Wang Y (2018) Big data analytics in operations management. Prod Oper Manag 27(10):1868–1883. https://doi.org/10.1111/poms.12838

Ciampi F, Marzi G, Demi S, Faraoni M (2020) The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives. J Knowl Manag 24(5):1157–1176. https://doi.org/10.1108/JKM-02-2020-0156

Colvin G (2018) What the Hell Happened at GE? Fortune Magazine. 177/6, http://fortune.com/longform/ge-decline-what-the-hell-happened/

Comuzzi M, Patel A (2016) How organisations leverage big data: a maturity model. Ind Manag Data Syst 116(8):1468–1492. https://doi.org/10.1108/IMDS-12-2015-0495

Coombs C, Hislop D, Taneva SK, Barnard S (2020) The strategic impacts of Intelligent automation for knowledge and service work: an interdisciplinary review. J Strateg Inf Syst 29(4):101600. https://doi.org/10.1016/j.jsis.2020.101600

Correani A, De Massis A, Frattini F, Petruzzelli AM, Natalicchio A (2020) Implementing a digital strategy: learning from the experience of three digital transformation projects. Calif Manage Rev 62(4):37–56. https://doi.org/10.1177/0008125620934864

Crossan MM, Lane HW, White RE (1999) An organizational learning framework: from intuition to institution. Acad Manag Rev 24(3):522–537. https://doi.org/10.5465/amr.1999.2202135

Dam NAK, Le Dinh T, Menvielle W (2019) A systematic literature review of big data adoption in internationalization. Journal of Marketing Analytics 7(3):182–195. https://doi.org/10.1057/s41270-019-00054-7

Davenport TH (2013) Analytics 3.0. Harvard Bus Rev 91(12):64–72, available at: https://hbr.org/2013/12/analytics-30# . Accessed 28 April 2021

De Luca LM, Herhausen D, Troilo G, Rossi A (2020) How and when do big data investments pay off? The role of marketing affordances and service innovation. J Acad Mark Sci 2020:1–21. https://doi.org/10.1007/s11747-020-00739-x

de Medeiros MM, Hoppen N, Maçada ACG (2020) Data science for business: benefits, challenges and opportunities. The Bottom Line 33(2):149–163. https://doi.org/10.1108/BL-12-2019-0132

Devasia R (2018) Data strategy to excel through disruptions in CADS. Sansmaran Res J, pp 1–2

de Swaan Arons M, van den Driest F, Weed K (2014) The ultimate marketing machine. Harvard Business Review.

Downes L, Mui C (1998) The end of strategy. Strat Lead 26(5):4

Dremel C, Wulf J, Herterich MM, Waizmann JC, Brenner W (2017) How AUDI AG established big data analytics in its digital transformation. MIS Q Exec 16(2):81–100

Dubey R, Gunasekaran A, Childe SJ (2019a) Big data analytics capability in supply chain agility. Manag Decis 57(8):2092–2112. https://doi.org/10.1108/MD-01-2018-0119

Dubey R, Gunasekaran A, Childe SJ, Blome C, Papadopoulos T (2019b) Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource-based view and big data culture. Br J Manag 30(2):341–361. https://doi.org/10.1111/1467-8551.12355

Eggert M, Alberts J (2020) Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda. Bus Res 13(2):685–739. https://doi.org/10.1007/s40685-020-00108-y

Erceg V, Zoranović T (2020) Required competencies for successful digital transformation. Ekonomika 66(3):47–54. https://doi.org/10.5937/ekonomika2003047E

Fakhri AB, Mohammed SL, Khan I, Sadiq AS, Alkazemi B, Pillai P, Choi BJ (2020) Industry 4.0: architecture and equipment revolution. Comput Mater Continua 66(2):1175–1194. https://doi.org/10.32604/cmc.2020.012587

Ferraris A, Mazzoleni A, Devalle A, Couturier J (2019) Big data analytics capabilities and knowledge management: impact on firm performance. Manag Decis 57(8):1923–1936. https://doi.org/10.1108/MD-07-2018-0825

Fiedler FE (1964) A contingency model of leadership effectiveness. In: Advances in experimental social psychology. Academic Press, vol 1, pp 149–190. doi: https://doi.org/10.1016/S0065-2601(08)60051-9

Foshay N, Yeoh W, Boo YL, Ong KL, Mattie D (2015) A comprehensive diagnostic framework for evaluating business intelligence and analytics effectiveness. Australas J Inf Syst 19:S37–S54. https://doi.org/10.3127/ajis.v19i0.1178

Fox S, Do T (2013) Getting real about big data: applying critical realism to analyse big data hype. Int J Manag Proj Bus 6(4):739–760. https://doi.org/10.1108/IJMPB-08-2012-0049

GalbRaith JR (2014) Organizational design challenges resulting from big data. J Org Des 3(1):2–13. https://ssrn.com/abstract=2458899

George G, Lakhani KR, Puranam P (2020) What has changed? The impact of Covid pandemic on the technology and innovation management research agenda. J Manage Stud 57(8):1754–1758. https://doi.org/10.1111/joms.12634

Ghasemaghaei M, Ebrahimi S, Hassanein K (2018) Data analytics competency for improving firm decision making performance. J Strateg Inf Syst 27(1):101–113. https://doi.org/10.1016/j.jsis.2017.10.001

Gong Y, Janssen M (2021) Roles and capabilities of enterprise architecture in big data analytics technology adoption and implementation. J Theor Appl Electron Commer Res 16(1):37–51. https://doi.org/10.4067/S0718-18762021000100104

Gregory A (2011) Data governance—protecting and unleashing the value of your customer data assets. J Direct Data Digit Mark Pract 12(3):230–248. https://doi.org/10.1057/dddmp.2010.41

Grossman RL (2018) A framework for evaluating the analytic maturity of an organization. Int J Inf Manage 38(1):45–51. https://doi.org/10.1016/j.ijinfomgt.2017.08.005

Grover V, Chiang RH, Liang TP, Zhang D (2018) Creating strategic business value from big data analytics: a research framework. J Manag Inf Syst 35(2):388–423. https://doi.org/10.1080/07421222.2018.1451951

Günther WA, Mehrizi MHR, Huysman M, Feldberg F (2017) Debating big data: a literature review on realizing value from big data. J Strateg Inf Syst 26(3):191–209. https://doi.org/10.1016/j.jsis.2017.07.003

Gupta M, George JF (2016) Toward the development of a big data analytics capability. Inf Manage 53(8):1049–1064. https://doi.org/10.1016/j.im.2016.07.004

Halaweh M, Massry AE (2015) Conceptual model for successful implementation of big data in organizations. J Int Technol Inf Manage 24(2):2

Hallikainen H, Savimäki E, Laukkanen T (2020) Fostering B2B sales with customer big data analytics. Ind Mark Manage 86:90–98. https://doi.org/10.1016/j.indmarman.2019.12.005

Hantrais L, Allin P, Kritikos M, Sogomonjan M, Anand PB, Livingstone S (2020) Covid-19 and the digital revolution. Contemp Soc Sci 16(2):256–270. https://doi.org/10.1080/21582041.2020.1833234

Harlow HD (2018) Developing a knowledge management strategy for data analytics and intellectual capital. Meditari Account Res 26(3):400–419. https://doi.org/10.1108/MEDAR-09-2017-0217

Holland CP, Thornton SC, Naudé P (2020) B2B analytics in the airline market: harnessing the power of consumer big data. Ind Mark Manage 86:52–64. https://doi.org/10.1016/j.indmarman.2019.11.002

Huberty M (2015) Awaiting the second big data revolution: from digital noise to value creation. J Ind Compet Trade 15(1):35–47

Inamdar Z, Raut R, Narwane VS, Gardas B, Narkhede B, Sagnak M (2020) A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018. J Enterp Inf Manag 34(1):101–139. https://doi.org/10.1108/JEIM-09-2019-0267

Jägare U (2019) Data science strategy for dummies. John Wiley and Sons, Incorporated

Jha AK, Agi MA, Ngai EW (2020) A note on big data analytics capability development in supply chain. Decis Support Syst 138:113382. https://doi.org/10.1016/j.dss.2020.113382

Ji-fan Ren S, Fosso Wamba S, Akter S, Dubey R, Childe SJ (2017) Modelling quality dynamics, business value and firm performance in a big data analytics environment. Int J Prod Res 55(17):5011–5026. https://doi.org/10.1080/00207543.2016.1154209

Johnson JS, Friend SB, Lee HS (2017) Big data facilitation, utilization, and monetization: exploring the 3Vs in a new product development process. J Prod Innov Manag 34(5):640–658. https://doi.org/10.1111/jpim.12397

Kache F, Seuring S (2017) Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. Int J Oper Prod Manag 37(1):10–36. https://doi.org/10.1108/IJOPM-02-2015-0078

Kamble SS, Gunasekaran A, Goswami M, Manda J (2019) A systematic perspective on the applications of big data analytics in healthcare management. Int J Healthcare Manage 12(3):226–240. https://doi.org/10.1080/20479700.2018.1531606

Keding C (2020) Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Manage Rev Quart 71:91–134. https://doi.org/10.1007/s11301-020-00181-x

Kemppainen L, Pikkarainen M, Hurmelinna-Laukkanen P, Reponen J (2019) Connected health innovation: data access challenges in the interface of AI companies and hospitals. Technol Innov Manag Rev 9(12):43–55

Khan M (2019) Challenges with big data analytics in service supply chains in the UAE. Manag Decis 57(8):2124–2147. https://doi.org/10.1108/MD-06-2018-0669

Khosrowpour M (1990) The changing role of information technology services. Rev Bus 11(4):11

Kitsios F, Kamariotou M (2021) Artificial intelligence and business strategy towards digital transformation: a research Agenda. Sustainability 13(4):2025. https://doi.org/10.3390/su13042025

Knabke T, Olbrich S (2018) Building novel capabilities to enable business intelligence agility: results from a quantitative study. Inf Syst E-Bus Manage 16(3):493–546. https://doi.org/10.1007/s10257-017-0361-z

Lacam JS (2020) Data: a collaborative? J High Technol Managem Res 31(1):100370. https://doi.org/10.1016/j.hitech.2020.100370

Lai Y, Sun H, Ren J (2018) Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management. Int J Logist Manage 29(2):676–703. https://doi.org/10.1108/IJLM-06-2017-0153

Lamba K, Singh SP (2018) Modeling big data enablers for operations and supply chain management. Int J Logist Manage 29(2):629–658. https://doi.org/10.1108/IJLM-07-2017-0183

Latif Z, Lei W, Latif S, Pathan ZH, Ullah R, Jianqiu Z (2019) Big data challenges: prioritizing by decision-making process using Analytic Network Process technique. Multimed Tools Appl 78(19):27127–27153. https://doi.org/10.1007/s11042-017-5161-4

Lautenbach P, Johnston K, Adeniran-Ogundipe T (2017) Factors influencing business intelligence and analytics usage extent in South African organisations. South Afric J Bus Manage 48(3):23–33. https://hdl.handle.net/10520/EJC-97b9b0316

Lee H, Kweon E, Kim M, Chai S (2017) Does implementation of big data analytics improve firms’ market value? Investors’ Reaction in Stock Market. Sustainability 9(6):978. https://doi.org/10.3390/su9060978

Li S, Yu H (2020) Big data and financial information analytics ecosystem: strengthening personal information under legal regulation. Inf Syst E-Bus Manage 18(4):891–909. https://doi.org/10.1007/s10257-019-00404-z

Li X, Zhang Y, Li Y, Yu K, Du Y (2021) Study of E-business applications based on big data analysis in modern hospital health management. Inf Syst E-Bus Manage 19:621–640. https://doi.org/10.1007/s10257-021-00520-9

Libert B, Beck M, Wind Y (2016) Questions to ask before your next digital transformation. Harv Bus Rev 60(12):11–13

Llave MR (2017) Business intelligence and analytics in small and medium-sized enterprises: a systematic literature review. Proc Comput Sci 121:194–205. https://doi.org/10.1016/j.procs.2017.11.027

Maalsen S, Dowling R (2020) Covid-19 and the accelerating smart home. Big Data Soc 7(2):2053951720938073. https://doi.org/10.1177/2053951720938073

Macada ACG, Brinkhues RA, Freitas JCDS (2019) Information management capability and big data strategy implementation. Revista De Administração De Empresas 59(6):379–388. https://doi.org/10.1590/S0034-759020190604

Mandal S (2019) The influence of big data analytics management on capabilities on supply chain preparedness, alertness and agility. Inf Technol People 32(2):297–318. https://doi.org/10.1108/ITP-11-2017-0386

Mandlik MA, Kadirov D (2018) Big data approaches and outcome of information asymmetry: opportunities for future research. Int J Bus Contin Risk Manage 8(4):303–318. https://doi.org/10.1504/IJBCRM.2018.095287

Markus ML (2017) Datification, organizational strategy, and IS research: what’s the score? J Strateg Inf Syst 26(3):233–241. https://doi.org/10.1016/j.jsis.2017.08.003

Maroufkhani P, Wagner R, Wan Ismail WK, Baroto MB, Nourani M (2019) Big data analytics and firm performance: a systematic review. Information 10(7):226. https://doi.org/10.3390/info10070226

Mazzei MJ, Noble D (2017) Big data dreams: a framework for corporate strategy. Bus Horiz 60(3):405–414. https://doi.org/10.1016/j.bushor.2017.01.010

Mehrabian A, Russell JA (1974) An approach to environmental psychology. The MIT Press.

Meyer JW, Rowan B (1977) Institutionalized organizations: formal structure as myth and ceremony. Am J Soc 83(2):340–363

Mikalef P, Gupta M (2021) Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Inf Manage 58(3):103434. https://doi.org/10.1016/j.im.2021.103434

Mikalef P, Pappas IO, Krogstie J, Giannakos M (2018) Big data analytics capabilities: a systematic literature review and research agenda. Inf Syst E-Bus Manage 16(3):547–578. https://doi.org/10.1007/s10257-017-0362-y

Mikalef P, Boura M, Lekakos G, Krogstie J (2019a) Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. Br J Manag 30(2):272–298. https://doi.org/10.1111/1467-8551.12343

Mikalef P, Boura M, Lekakos G, Krogstie J (2019b) Big data analytics and firm performance: findings from a mixed-method approach. J Bus Res 98:261–276. https://doi.org/10.1016/j.jbusres.2019.01.044

Mikalef P, Boura M, Lekakos G, Krogstie J (2020a) The role of information governance in big data analytics driven innovation. Inf Manage 57(7):103361. https://doi.org/10.1016/j.im.2020.103361

Mikalef P, Krogstie J, Pappas IO, Pavlou P (2020b) Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf Manage 57(2):103169. https://doi.org/10.1016/j.im.2019.05.004

Mikalef P, van de Wetering R, Krogstie J (2020c) Building dynamic capabilities by leveraging big data analytics: The role of organizational inertia. Inf Manage. https://doi.org/10.1016/j.im.2020.103412

Mikalef P, Pappas IO, Giannakos MN, Krogstie J, Lekakos G (2016) Big data and strategy: a research framework. In: MCIS

Mir UB, Sharma S, Kar AK, Gupta MP (2020) Critical success factors for integrating artificial intelligence and robotics. Digit Policy Regul Govern 22(4):307–331. https://doi.org/10.1016/j.jjimei.2021.100017

Mithas S, Tafti A, Mitchell W (2013) How a firm's competitive environment and digital strategic posture influence digital business strategy. MIS Quart, pp 511–536. http://www.jstor.org/stable/43825921

Moreno V, da Silva FELV, Ferreira R, Filardi F (2019) Complementarity as a driver of value in business intelligence and analytics adoption processes. Revista Ibero-Americana De Estrategia 18(1):57. https://doi.org/10.5585/ijsm.v18i1.2678

Müller O, Fay M, Vom Brocke J (2018) The effect of big data and analytics on firm performance: an econometric analysis considering industry characteristics. J Manag Inf Syst 35(2):488–509. https://doi.org/10.1080/07421222.2018.1451955

Najjar MS, Kettinger WJ (2013) Data monetization: lessons from a Retailer’s Journey. MIS Q Exec 12(4):213–225

Nelson B, Olovsson T (2016) Security and privacy for big data: a systematic literature review. In: 2016 IEEE international conference on big data (big data). IEEE, pp 3693–3702. doi: https://doi.org/10.1109/BigData.2016.7841037 .

Newlands G, Lutz C, Tamò-Larrieux A, Villaronga EF, Harasgama R, Scheitlin G (2020) Innovation under pressure: Implications for data privacy during the Covid-19 pandemic. Big Data Soc 7(2):2053951720976680. https://doi.org/10.1177/2053951720976680

Nguyen T, Li ZHOU, Spiegler V, Ieromonachou P, Lin Y (2018) Big data analytics in supply chain management: a state-of-the-art literature review. Comput Oper Res 98:254–264. https://doi.org/10.1016/j.cor.2017.07.004

Okwechime E, Duncan P, Edgar D (2018) Big data and smart cities: a public sector organizational learning perspective. Inf Syst E-Bus Manage 16(3):601–625. https://doi.org/10.1007/s10257-017-0344-0

Olszak CM, Mach-Król M (2018) A conceptual framework for assessing an organization’s readiness to adopt big data. Sustainability 10(10):3734. https://doi.org/10.3390/su10103734

Pappas IO, Mikalef P, Giannakos MN, Krogstie J, Lekakos G (2018) Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Inf Syst E-Bus Manage 16:479–491. https://doi.org/10.1007/s10257-018-0377-z

Poeppelbuss J, Niehaves B, Simons A, Becker J (2011) Maturity models in information systems research: literature search and analysis. Commun Assoc Inf Syst 29(27):505–532. https://doi.org/10.17705/1CAIS.02927

Popovič A, Hackney R, Tassabehji R, Castelli M (2018) The impact of big data analytics on firms’ high value business performance. Inf Syst Front 20(2):209–222. https://doi.org/10.1007/s10796-016-9720-4

Raguseo E, Vitari C (2018) Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects. Int J Prod Res 56(15):5206–5221. https://doi.org/10.1080/00207543.2018.1427900

Ram J, Afridi NK, Khan KA (2019) Adoption of Big Data analytics in construction: development of a conceptual model. Built Environ Project Asset Manage 9(4):564–579. https://doi.org/10.1108/BEPAM-05-2018-0077

Ramnath VR, Kairaitis K, Malhotra A (2020) The challenge of COVID-19 has accelerated the use of new data-sharing technologies. Respirology 25(8):800. https://doi.org/10.1111/resp.13894

Ranjan J (2019) The 10 Vs of Big Data framework in the Context of 5 Industry Verticals. Productivity 59(4):324–342

Ransbotham S, Kiron D, Gerbert P, Reeves M (2017) Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Manag Rev 59(1):1–17

Rialti R, Marzi G, Silic M, Ciappei C (2018) Ambidextrous organization and agility in big data era. Bus Process Manag J 24(5):1091–1109. https://doi.org/10.1108/BPMJ-07-2017-0210

Rialti R, Marzi G, Ciappei C, Busso D (2019) Big data and dynamic capabilities: a bibliometric analysis and systematic literature review. Manag Decis 57(8):2052–2068. https://doi.org/10.1108/MD-07-2018-0821

Rialti R, Marzi G, Caputo A, Mayah KA (2020) Achieving strategic flexibility in the era of big data. Manag Decis 58(8):1585–1600. https://doi.org/10.1108/MD-09-2019-1237

Ross JW, Beath CM, Sebastian IM (2017) How to develop a great digital strategy. MIT Sloan Manag Rev 58(2):7

Saldžiūnas K, Skyrius R (2017) The challenges of big data analytics in the mobile communications sector. Ekonomika 96(2):110–121

Sang L, Yu M, Lin H, Zhang Z, Jin R (2020) Big data, technology capability and construction project quality: a cross-level investigation. Eng Constr Archit Manag 28(3):706–727. https://doi.org/10.1108/ECAM-02-2020-0135

Sarker KU, Deraman AB, Hasan R, Abbas A (2019) Ontological practice for big data management. International Journal of Computing and Digital Systems 8(03):265–273. https://doi.org/10.12785/ijcds/080306

Schroeder R (2016) Big data business models: Challenges and opportunities. Cogent Soc Sci 2(1):1166924. https://doi.org/10.1080/23311886.2016.1166924

Sebastian IM, Ross JW, Beath C, Mocker M, Moloney KG, Fonstad NO (2017) How big old companies navigate digital transformation. MIS Q Exec 16(3):197–213

Shahbaz M, Gao C, Zhai L, Shahzad F, Hu Y (2019) Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. J Big Data 6(1):1–20. https://doi.org/10.1186/s40537-019-0170-y

Shim JP, French AM, Guo C, Jablonski J (2015) Big data and analytics: Issues, solutions, and ROI. Commun Assoc Inf Syst 37(1):39. https://doi.org/10.17705/1CAIS.03739

Shokouhyar S, Seddigh MR, Panahifar F (2020) Impact of big data analytics capabilities on supply chain sustainability. World J Sci Technol Sustain Dev 17(1):33–57. https://doi.org/10.1108/WJSTSD-06-2019-0031

Singh RK, Agrawal S, Sahu A, Kazancoglu Y (2021) Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda. TQM J Ahead of Print. doi: https://doi.org/10.1108/TQM-02-2021-0051

Singh NP, Singh S (2019) Building supply chain risk resilience. Benchmark Int J 26(7):2318–2342. https://doi.org/10.1108/BIJ-10-2018-0346

Sivarajah U, Kamal MM, Irani Z, Weerakkody V (2017) Critical analysis of Big Data challenges and analytical methods. J Bus Res 70:263–286. https://doi.org/10.1016/j.jbusres.2016.08.001

Song M, Zhang H, Heng J (2020) Creating sustainable innovativeness through big data and big data analytics capability: from the perspective of the information processing theory. Sustainability 12(5):1984. https://doi.org/10.3390/su12051984

Soto-Acosta P (2020) COVID-19 pandemic: Shifting digital transformation to a high-speed gear. Inf Syst Manag 37(4):260–266. https://doi.org/10.1080/10580530.2020.1814461

Sun S, Hall DJ, Cegielski CG (2020) Organizational intention to adopt big data in the B2B context: an integrated view. Ind Mark Manage 86:109–121. https://doi.org/10.1016/j.indmarman.2019.09.003

Tabesh P, Mousavidin E, Hasani S (2019) Implementing big data strategies: a managerial perspective. Bus Horiz 62(3):347–358. https://doi.org/10.1016/j.bushor.2019.02.001

Trompenaars F, Woolliams P (2016) Going digital internationally. Organ Dev J 34(1):11–35

Ukko J, Nasiri M, Saunila M, Rantala T (2019) Sustainability strategy as a moderator in the relationship between digital business strategy and financial performance. J Clean Prod 236:117626. https://doi.org/10.1016/j.jclepro.2019.117626

Van Tonder C, Schachtebeck C, Nieuwenhuizen C, Bossink B (2020) A framework for digital transformation and business model innovation. Manage J Contemp Manage 25(2):111–132. https://doi.org/10.30924/mjcmi.25.2.6

Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Dong JQ, Fabian N, Haenlein M (2021) Digital transformation: a multidisciplinary reflection and research agenda. J Bus Res 122:889–901. https://doi.org/10.1016/j.jbusres.2019.09.022

Verma S (2017) The adoption of big data services by manufacturing firms: an empirical investigation in India. JISTEM-J Inf Syst Technol Manage 14(1):39–68. https://doi.org/10.4301/S1807-17752017000100003

Verma S, Bhattacharyya SS (2017) Perceived strategic value-based adoption of big data analytics in emerging economy. J Enterp Inf Manag 30(3):354–382. https://doi.org/10.1108/JEIM-10-2015-0099

Vydra S, Klievink B (2019) Techno-optimism and policy-pessimism in the public sector big data debate. Gov Inf Q 36(4):101383. https://doi.org/10.1016/j.giq.2019.05.010

Walker RS, Brown I (2019) Big data analytics adoption: a case study in a large South African telecommunications organisation. South Afr J Inf Manage 21(1):1–10. https://doi.org/10.4102/sajim.v21i1.1079

Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246. https://doi.org/10.1016/j.ijpe.2014.12.031

Wamba SF, Gunasekaran A, Akter S, Ren SJF, Dubey R, Childe SJ (2017) Big data analytics and firm performance: effects of dynamic capabilities. J Bus Res 70:356–365. https://doi.org/10.1016/j.jbusres.2016.08.009

Wamba SF, Akter S, Trinchera L, De Bourmont M (2019) Turning information quality into firm performance in the big data economy. Manag Decis 57(8):1756–1783. https://doi.org/10.1108/MD-04-2018-0394

Wang Y, Hajli N (2017) Exploring the path to big data analytics success in healthcare. J Bus Res 70:287–299. https://doi.org/10.1016/j.jbusres.2016.08.002

Wang Y, Kung L, Byrd TA (2018a) Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Chang 126:3–13. https://doi.org/10.1016/j.techfore.2015.12.019

Wang Y, Kung L, Wang WYC, Cegielski CG (2018b) An integrated big data analytics-enabled transformation model: Application to health care. Inf Manage 55(1):64–79. https://doi.org/10.1016/j.im.2017.04.001

Wang L, Yang M, Pathan ZH, Salam S, Shahzad K, Zeng J (2018c) Analysis of influencing factors of big data adoption in Chinese enterprises using DANP technique. Sustainability 10(11):3956. https://doi.org/10.3390/su10113956

Wang Y, Kung L, Gupta S, Ozdemir S (2019) Leveraging big data analytics to improve quality of care in healthcare organizations: A configurational perspective. Br J Manag 30(2):362–388. https://doi.org/10.1111/1467-8551.12332

Westerman G (2018) Your company doesn’t need a digital strategy. MIT Sloan Manag Rev 59(3):1–5

Wiener M, Saunders C, Marabelli M (2020) Big-data business models: A critical literature review and multiperspective research framework. J Inf Technol 35(1):66–91. https://doi.org/10.1177/0268396219896811

Wright LT, Robin R, Stone M, Aravopoulou DE (2019) Adoption of big data technology for innovation in B2B marketing. J Bus Bus Mark 26(3–4):281–293. https://doi.org/10.1080/1051712X.2019.1611082

Wu J, Li H, Lin Z, Goh KY (2017) How big data and analytics reshape the wearable device market–the context of e-health. Int J Prod Res 55(17):5168–5182. https://doi.org/10.1080/00207543.2015.1059521

Yadav J (2017) Confronts of big data and its tools. Int J Adv Res Comput Sci 8(1):215–218

Yang H, Kundakcioglu OE, Zeng D (2015) Healthcare data analytics. Inf Syst E-Bus Manage 13:595–597. https://doi.org/10.1007/s10257-015-0297-0

Zaki M (2019) Digital transformation: harnessing digital technologies for the next generation of services. J Serv Mark 33(4):429–435. https://doi.org/10.1108/JSM-01-2019-0034

Zhang F (2021) Construction of internal management system of business strategic planning based on Artificial Intelligence. Inf Syst E-Bus Manage. https://doi.org/10.1007/s10257-021-00510-x

Zhang H, Song M, He H (2020) Achieving the success of sustainability development projects through big data analytics and artificial intelligence capability. Sustainability 12(3):949. https://doi.org/10.3390/su12030949

Zhao-hong Y, Hui-yu W, Bin Z, Zhi-he H, Wan-lin L (2018) A literature review on the key technologies of processing big data. In: 2018 IEEE 3rd International conference on cloud computing and big data analysis (ICCCBDA). IEEE, pp 202–208. doi: https://doi.org/10.1109/ICCCBDA.2018.8386512 .

Download references

Author information

Authors and affiliations.

Indian Institute of Management Shillong, Shillong, India

Rajesh Chidananda Reddy

Information Systems and Analytics Area, Indian Institute of Management Shillong, Umsawli, Shillong, 793018, India

Biplab Bhattacharjee

Strategic Management Area, Indian Institute of Management Shillong, Shillong, India

Debasisha Mishra

Department of Finance, University of Birmingham, Birmingham, UK

Anandadeep Mandal

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biplab Bhattacharjee .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 6504 kb)

Supplementary file2 (docx 51 kb), rights and permissions.

Reprints and permissions

About this article

Reddy, R.C., Bhattacharjee, B., Mishra, D. et al. A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy. Inf Syst E-Bus Manage 20 , 223–255 (2022). https://doi.org/10.1007/s10257-022-00550-x

Download citation

Received : 13 October 2021

Revised : 16 December 2021

Accepted : 05 January 2022

Published : 25 January 2022

Issue Date : March 2022

DOI : https://doi.org/10.1007/s10257-022-00550-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Data science
Digitalization
Find a journal
Publish with us
Track your research

Strategic Planning and Management in Transit Agencies (2005)

Chapter: chapter two - literature review.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

4OVERVIEW The literature review revealed that strategic planning is a popular and widespread practice in both the private and pub- lic sectors. It is a leadership and management tool that has been successfully used for decades to help organizations bet- ter prepare for the future and improve long-term performance. There is increased recognition that strategic planning is not enough by itself and that too many good strategic plans have been left to gather dust. Effective leadership is required to both develop and implement strategic plans. This has led to the concept of âstrategic management,â which includes the implementation and ongoing management of strategic plans, not just their development. Another trend has been to make the strategic planning process briefer and more flexible. Part of the problem in the past has been that formal and elaborate planning processes have often led to the development of comprehensive and thor- ough plans that are out of date by the time they are produced. Todayâs more turbulent and uncertain times require that less emphasis be given to the form of the plan and more to the substance and timeliness of the results. Henry Mintzberg has suggested that strategic planning is quite different than âstrategic thinking,â the creative synthe- sis of visionary new business strategies. He goes further by suggesting that strategic planning, essentially analytical in nature, may even be antithetical to strategic thinking. He believes that strategic planning is more appropriately thought of as âstrategic programming.â As such, it may be quite suit- able for relatively stable public transportation systems. Although it began in the military, the early nonmilitary use of strategic planning was primarily in the private sector. How- ever, its use in the public and nonprofit sectors has increased substantially and there are now several good sources of infor- mation on performing strategic planning in these sectors. Sev- eral reports and articles that pertain specifically to public- sector transportation are also reviewed. Effective strategic planning and management requires not only deciding what an organization should do, but also its having the knowledge and skills required to do it. With this in mind, the tools of organization development are discussed as a factor that could increase an organizationâs capability to develop and implement strategic plans. This includes devel- oping and retaining a workforce with the requisite knowledge and skills. Finally, a recent TCRP study about âemerging new para- digmsâ in transit has suggested that fundamental changes will be required if transit is to succeed in a world of increasing social, economic, political, and technological change. If so, it could well be that strategic planning and management will be a crucial tool toward this end. INTRODUCTION As would be expected, there is a significant amount of liter- ature on the subject of strategic planning in general, particu- larly in the private sector. There is also a fair amount written on strategic planning in the public and nonprofit sectors. How- ever, much less has been written specifically about strategic planning in public transportation. This literature review will be selective rather than exhaus- tive. It will cover strategic planning in the private sector; how- ever, it will primarily focus on strategic planning in the public sector and in public transportation. HISTORY Strategic planning has a long history in organizations. Its ori- gins are often thought to have been in the military, where there was a need to develop comprehensive, long-term strate- gies to win a war as opposed to the shorter-term, more limited tactical planning needed to win battles or skirmishes. An early form of strategic planning, the SWOT analysis (strengths, weaknesses, opportunities, and threats), came out of the Har- vard Business School in the 1950s as part of their efforts in regard to business policy development and the analysis of business strategies in relation to an organizationâs internal and external environments. The practice of strategic planning achieved considerable popularity in private-sector organizations in the 1960s and 1970s. In the 1980s, it became more and more prevalent in the public and nonprofit sectors. Unlike other management fads that have quickly disappeared, strategic planning is a management technique or practice that has demonstrated staying power and broad usage across a variety of organiza- tions in both the private and public sectors. STRATEGIC PLANNING IN GENERAL Early on, strategic planning was a relatively formal, elabo- rate, and often lengthy process. In addition, because times CHAPTER TWO LITERATURE REVIEW

5were more stable, a strategic plan was intended to last for several years. More recently, largely in response to these more rapidly changing and turbulent times, there has been a clear trend in the private sector to â¢ Shorten the strategic planning cycle so that the plan is not out of date by the time it is finished. (However, it should also be noted that there has been a countervail- ing trend to open up the process and involve as many participants as feasible, rather than having it be a âtop- downâ plan developed largely by top management and imposed on the organization. The effect of this has been to lengthen the process.) â¢ Make the planning process less formal and more flexible. â¢ Make the process relatively continuous rather than some- thing undertaken only every 2 to 5 years. â¢ Link it to other important business processes such as budgeting and performance measurement. An example of this is found in Strategic Readiness: The Making of the Learning Organization (1) in which the authors suggest a shift from strategic planning to strategic learning through a regular process of â¢ Continuous planningâwherein the organization focuses on questioning and quickly adjusting existing plans; â¢ Improvised implementationâthat is, both experimental and evolving, and that provides good information for learning; and â¢ Deep reflection about original assumptionsâwhat has changed and what has been learned. The authors maintain that âmost organizational change results not from formal plans and fixed programs for change but from a process of learningâand not just from the learning of indi- viduals but, more importantly, from the collective learning of entire organizationsâ (1, p. xi). The 1994 publication of The Rise and Fall of Strategic Planning by Mintzberg (2) presented a very comprehensive, complex, and contrarian view of the practice of strategic plan- ning. His central argument is that strategic planning by its very nature is the antithesis of âstrategic thinking.â He views strategic thinking as a creative act of synthesis, and strategic planning as a process that is essentially one of analysis. (An analogy might be right brain versus left brain thinking, in which the right brain is considered to be more creative and holistic and the left brain is considered to be more linear and logical.) Mintzberg believes that âstrategic planning often spoils strategic thinking, causing managers to confuse real vision with the manipulation of numbersâ (3, p. 107). Mintzberg goes on to argue that âStrategic planning, as it has been practiced, has really been strategic programming, the articulation and elaboration of strategies, or visions, that already existâ (3, p. 107). He asserts that strategic planning does not fail because of such commonly mentioned pitfalls as a lack of commitment from top management or resistance to change, but instead because of its fundamental nature of being too analytical, too formalized, and too detached from the functions and interests of the businessâs line managers. In spite of his belief that strategic planning is not effective at creating business strategies (strategic thinking), he believes that it can play important roles including: â¢ Strategic planning as strategic programmingâInstead of trying to use strategic planning to generate strategies or visions, use it to program them. This can be done through a process of âcodificationâ (expressing the strate- gies in ways that are clear and operational), by âelab- orationâ (breaking strategies down into substrategies and action plans), and âconversionâ (identifying the impacts of the strategies on operations; e.g., on budgets and performance controls). â¢ Strategic plans as tools to communicate and controlâ Plans can be an important way of communicating the organizationâs intentions both internally (helping to ensure that everyone in the organization is pulling in the same direction) and externally. â¢ Strategic planners as analystsâPlanners can use their analytical skills and orientation by performing special studies and by offering new perspectives. â¢ Strategic planners as catalystsâIn this role, planners do not try to enter the âblack boxâ of strategy making, but instead encourage and assist line managers to think in creative ways about the future (3, pp. 112â114). In Mintzbergâs words, âPlanners shouldnât create strategies, but they can supply data, help managers think strategically, and program the visionâ (3, p. 107). Although insightful, Mintzbergâs distinction between strategic thinking and strategic planning may be more rele- vant to the private sector, where the environment tends to be more volatile and where businesses must constantly be on the lookout for new business strategies and opportunities. Gen- erally, in the public sector, and in public transportation more specifically, the situation is usually more constrained. For example, the purpose and geographic jurisdiction of public transportation agencies is usually prescribed by state legisla- tion. For the most part, public transportation agencies are not going to be pursuing business opportunities outside of the area of public transportation. In addition, their financial struc- ture is often defined or controlled in large part by their enabling legislation or funding bodies. It might therefore be argued that strategic planning, when thought of as strategic programming as described by Mintzberg, may be the primary role it should play in public transportation. Mintzberg implies this when he states that âOnly when an organization is sure of the relative stability of

its environment and is in need of the tight coordination of a myriad of intricate operations (as is typically the case of air- lines with their needs for complicated scheduling), does such strategic programming make senseâ (3, p. 112). The more creative strategic thinking described by Mintzberg might be used only infrequently, when there is a need for more trans- formative organizational or institutional change. Another valuable perspective on strategic planning, that appeared in the mid-1990s, was the concept of the âbalanced scorecard,â as developed by Kaplan and Norton in response to their belief that strategic planning and management as practiced in the private sector gave too much weight to financial matters (4). To remedy this, they developed the balanced scorecard idea, an approach that provides a frame- work for helping an organization achieve its long-term strate- gic goals by viewing the organization from four key per- spectives: (1) the customer, (2) internal business processes, (3) learning and growth, and (4) financial performance. The balanced scorecard system involves the development, utilization, and analysis of performance metrics that are devel- oped for each of these perspectives. STRATEGIC PLANNING IN THE PUBLIC SECTOR One of the âbiblesâ of strategic planning in the public (and nonprofit) sector has been John Brysonâs Strategic Planning for Public and Nonprofit Organizations: A Guide to Strength- ening and Sustaining Organizational Achievement (5). This book was first published in 1988, and then substantially revised in 1995. Bryson defines strategic planning as âa disciplined effort to produce fundamental decisions and actions that shape and guide what an organization is, what it does, and why it does itâ (5, pp. 4â5). He offers a generic strategic planning model that has been successfully used by many public and nonprofit agencies. This model consists of 10 steps that he refers to as the Strategy Change Cycle (5, p. 23): 1. Initiate and agree on a strategic planning process (including who should be involved, and their roles and responsibilities). 2. Identify organizational mandates (e.g., enabling legislation). 3. Clarify organizational mission and values (this step should include an analysis of key âstakeholdersâ). 4. Assess the organizationâs external and internal envi- ronments to identify strengths, weaknesses, opportu- nities, and threats (SWOT analysis). 5. Identify the strategic issues facing the organization. 6. Formulate strategies to manage these issues. 7. Review and adopt the strategic plan or plans. 8. Establish an effective organizational vision. 9. Develop an effective implementation process. 10. Reassess strategies and the strategic planning process. 6 Note that Steps 1â8 focus primarily on the planning process, whereas Steps 9 and 10 shift the emphasis from strategic planning to strategic management. There are some lessons to be learned in the revisions the author made to the book in 1995. For example, Steps 7, 9, and 10 were added, reflecting the need to not just produce a plan but to effectively implement it, and also to reassess the planning process as a prelude to another round of planning. The emphasis is on strategic management, not just strategic planning. In addition, the author places more emphasis on the recognition that strategic planning is different than strategic thought and action (a point strongly made by Mintzberg and noted previously). In summary, the 1995 edition makes a more explicit linkage between leadership, strategic planning, and management. Bryson is clearly a strong believer in the benefits of strate- gic planning, including the promotion of strategic thought and action, improved decision making, and enhanced orga- nizational responsiveness and performance (5, p. 7). However, he also recognizes that strategic planning has limitations and should not be considered a panacea for all organizational problems or situations. For example, strategic planning is most likely not an appropriate response to extreme adverse circumstances; if the organization does not have the necessary people, skills, or management commitment to pro- duce a good plan; or if the likelihood of plan implementation is low. Bryson believes that strategic planning is here to stay because it effectively deals with substantive issues and con- cerns as well as the reality of political decision making. In 1993, the U.S. Congress passed the Government Per- formance and Results Act (GPRA). This act requires that federal agencies take a number of steps to operate in a more businesslike manner and that they become more accountable to taxpayers. These steps require agencies to â¢ Define their missions and desired outcomes, use strate- gic planning, involve their stakeholders, assess their environments, and align their activities, core processes, and resources in support of mission-related outcomes; â¢ Measure their performance to ensure that goals are being met; and â¢ Use the performance data to improve organizational processes, identify performance gaps, and set improve- ment goals. Under the GPRA, agencies were instructed to develop mul- tiyear strategic plans, annual performance plans, and annual performance reports. As a result, in 1996, the FTA published its first strategic plan, covering the period from 1997 to 2001. An executive guide for implementing the GPRA, pub- lished by the Government Accounting Office, pointed out that

7the success of the GPRA âdepends on strong leadership prac- tices that devolve decision-making authority with account- ability, create incentives, build expertise, and integrate man- agement reformsâ (6). STRATEGIC PLANNING IN PUBLIC-SECTOR TRANSPORTATION In the mid-1980s, the University Research and Training Program of the Urban Mass Transportation Administration (UMTA) (currently the FTA) funded a project that examined strategic planning in small- and medium-size transit agen- cies (7). The study report included a discussion of strategic planning and its benefits, a survey of 104 transit agencies, and a case study of a small transit system in Missouri (South- east Missouri Transportation Services, Inc.). The author observed that the crux of strategic planning is to create a process that answers three basic questions (7, p. 5): 1. Where are we? 2. Where do we want to go? 3. How can we get there? In the survey of transit properties, the following percent- ages of respondents answered that they conduct a formal strategic planning process: â¢ Small systems (fewer than 50 vehicles) 42% â¢ Medium systems (50â99 vehicles) 68% â¢ Big systems (100â500 vehicles) 62% â¢ Large systems (more than 500 vehicles) 93% The average for the four sizes was 59%. However, when responses were analyzed more closely, it was noted that more than half (57%) were actually referring to long-range service and capital plans as required by the fed- eral UMTA, short-range service plans, or to the annual bud- geting process. In short, there appeared to be a great deal of confusion as to what actually constitutes strategic planning. The study identified the following managerial tools used by transit agencies that might become part of a strategic plan- ning process: â¢ Strategic-type special studies (e.g., performance audits), â¢ Performance indicators, â¢ Management by objectives, and â¢ Strategy retreats. It found that there were a number of obstacles to con- ducting strategic planning in agencies, and that these obsta- cles differed according to the size of the agency. For exam- ple, inadequate staff resources was regarded as a significant obstacle by small agencies, but much less so by large ones. Conversely, a lack of good information was regarded as a serious problem by large agencies, but not so serious by small agencies. Large agencies cited the âfish-bowlâ envi- ronment of public agencies as a problem and also changing state policies. Other obstacles cited by respondents included: â¢ Operating pressures, â¢ A short-term orientation inside and outside the agency (âfire-fightingâ), â¢ The annual budget process, and â¢ An inability to enter new markets or alter the agencyâs external environment. The study also found that small agencies in particular have difficulty in regard to strategic planning. In general, these dif- ficulties had to do with inadequate resources, including staff, expertise, data, and the money needed to hire outside exper- tise. Three key conclusions emerged from the study: 1. The need to reinterpret the concept of strategic planning. 2. The importance of organizational readiness. 3. The need to mold strategic planning to the specific context of the organization. In regard to the need to reinterpret the concept of strate- gic planning, the study noted that conducting a comprehen- sive and formal strategic planning process (the âclassicalâ strategic planning model) may be beyond the capability of most transit agencies. Moreover, it may be more than is nec- essary. Five strategies are suggested in regard to this issue: 1. The formality of the process needs to be de-emphasized. 2. The level of data and analysis should be kept in rea- sonable relationship to the capabilities of the agency. 3. The cost of the process should be kept in some reason- able relationship with the likely benefits. 4. There needs to be a better understanding that long- range planning is not the same as strategic planning. Long-range planning may be necessary and useful; how- ever, it does not fulfill the same purposes. 5. Strategic planning might best be performed by seg- menting it. Rather than implementing a single, rigid process, it might be more effective if it is kept more flex- ible and if it uses various tools for specific purposes (e.g., special strategic studies). The second conclusion was the importance of organiza- tional readiness; some organizations may just not be ready to undertake strategic planning. Key factors in whether an orga- nization is ready include organizational sophistication and stamina, the flexibility and commitment of management, and the skills and expertise of the participants. Finally, in regard to adapting strategic planning to the orga- nizational context, management is encouraged to be realistic about the capabilities of the agency, set priorities and clearly define objectives for the process, design a process that reflects

these considerations, and build on what already exists in the agency. In 1985, an article was published about a case study of the development of a strategic management process in the Penn- sylvania Department of Transportation (8). This study made clear the importance of not just developing a strategic plan but of also creating a management process to implement and man- age the plan. There were several factors that distinguished this effort from typical strategic planning processes at the time. For example, several key structural changes were made. A strategic management committee made up of the agencyâs six top managers was formed to direct and manage the change process. Seven substantive subcommittees were also formed to help manage the effort. Organization-wide planning was centralized and program and project planning were decentral- ized. A concept of âbusiness groupsâ was used to create a new way of thinking about the primary products and services pro- vided by the agency (as opposed to the more traditional way of thinking about the functions performed). Four-year plans were developed for each of the agencyâs operating districts. Finally, the strategic planning process was tightly linked to the budget process to ensure that the agencyâs resources were being aligned with the plans. All of these actions served to institutionalize the strategic planning process throughout the agency. In 1988, the same year that Bryson published his book on strategic planning in the public sector, the UMTA published A Guide to Strategic Planning for Transit Properties (9). Considered by some in the transit industry to be a classic text, this report provided an early guide or framework for strate- gic planning in transit. The authors define strategic planning as âthe analysis of environmental change, the formulation of organizational objectives, and the establishment of priorities for resource allocationâ (9, p. 1). The report goes on to describe why it is important to do strategic planning, and presents several case studies as examples of how to do it effectively. (The case studies were AlamedaâContra Costa Transit, NJ Transit, Port Authority of Allegheny County Transit, Seattle Metro, and the Utah Transit Authority.) In addition, the report discusses the following key differences between planning in the public and private sectors (9, pp. 3â4): â¢ The private sector is primarily driven by the financial bottom line, whereas public agencies generally have multiple and often ambiguous goals and a variety of key stakeholders. â¢ Public agencies are more often subject to public scrutiny and political pressures. â¢ Mandates in the public sector are often legislated and are not up to management prerogative. Key conclusions of the study were: 8 â¢ Upper management must be seriously committed to and participate in the strategic planning process. â¢ The development of a mission, goals, and objectives should be based on a careful analysis of the environ- ment (both internal and external) and should emphasize a marketing perspective. Objectives should be stated in ways that are measurable. â¢ There should be linkages between strategic planning, program planning, and budgeting, and between achieve- ment of strategic planning objectives and personnel appraisals and compensation. â¢ Indicators should be developed that measure the effi- ciency and effectiveness of transit services. â¢ Good communication is a crucial ingredient of an effec- tive strategic planning and implementation process. â¢ âStrategy championsâ with appropriate responsibilities and incentives are needed for successful implementation. â¢ Environmental change should be regularly monitored. â¢ The process should be flexible, iterative, and continuous. â¢ Based on the case studies, there are clearly different ways that strategic planning can be conducted in terms of levels of detail and formality, how the process is organized, who participates, and the degree of analysis and documentation. The report ends with a recommended strategic planning framework that includes the following seven steps: 1. Organize management team and planning staff. 2. Undertake an environmental or situation audit. 3. Establish mission, goals, and objectives. 4. Develop broad strategies. 5. Establish programs and budgets. 6. Monitor program resultsâmeasure. 7. Monitor the environment. In 1990, the NCHRP published Strategic Planning and Management Guidelines for Transportation Agencies (10). This study looked at the current environment for strategic planning and management in a variety of publicly funded transportation agencies, provided a definition of strategic man- agement and its components, and recommended guidelines for successfully institutionalizing strategic management. The report distinguished strategic management from other traditional management practices by noting that the tradi- tional practices ask âHow do we keep doing what we are doing, only do it better?â Strategic management focuses instead on an overall vision of where the organization should be heading, i.e., what it plans to accomplish and how it can get it accomplished. It provides for the involvement of the entire organization in managing its people, processes, and products toward successful accomplishment of its goals and objec- tivesâ (10, p. 1). The report went on to delineate the following minimum components of a strategic management process (10, p. 2):

9â¢ Mission statement (including goals and objectives), â¢ Environmental scan, â¢ Strategy development, â¢ Action plan development, â¢ Resource allocation, and â¢ Performance measurement. An environmental scan generally involves an analysis or assessment of both the external and internal issues that are likely to affect an organization. It is sometimes referred to as a âsituation audit.â It concluded that in the various transportation agencies stud- ied, a âstrategic management process and its benefits are pres- ent and understood in less than a dozen state departments of transportationâ and that âmany of the remainderâplus many transit, airport, port authorities, and other publicly funded transportation agenciesâseem to have insufficient interest in, or understanding of, strategic managementâ (10, p. 1). The report included a set of guidelines for instituting strate- gic management comprised of four primary stages: Stage I. Identifying the Need for Strategic Management â¢ Determine the current status of strategic management in the agency. Stage II. Establishment or Enhancement of Key Strategic Management Elements â¢ Define the agencyâs business. â¢ Develop plans for implementing strategic management initiatives. Stage III. Integration of the Key Elements into a Functioning System â¢ Ensure that the agency mission statement and goal structure are in place. â¢ Obtain chief administrative officer and senior management commitment to the strategic management process. â¢ Establish a clearly understood division of responsibility for strategic management implementation, including the selec- tion of implementation managers or facilitators. â¢ Develop an accurate information base and maintain its timeliness. Stage IV. Ongoing Use and Refinement of the Strategic Management System â¢ Monitor the strategic management system. â¢ Develop a reward and recognition program. In a 2003 article, âHow to Develop a Strategic Plan That Wonât Gather Dustâ (11), Richard Simonetta (CEO of Val- ley Metro Rail, Inc., in Phoenix, Arizona) recognized that there is often a significant gap or breakdown between com- pleting a strategic plan and implementing its recommenda- tions. Too often the focus is on the production of a plan doc- ument and not enough emphasis is placed on its execution. He outlined several principles to ensure that the strategic planning effort is successful (11, p. 80): â¢ The process needs to be ongoing, with broad input for development and specific responsibility for effective implementation. â¢ The plan needs to have milestones and performance metrics to measure progress and celebrate achievements. â¢ The plan needs to involve stakeholders in the develop- ment, deployment, and refinement stages. â¢ Finally, a strategic plan must be a living document that evolves over time as the public transit system becomes more successful at achieving its short- and long-range goals. A similar case, that is, that strategic plans often fail to achieve full-scale implementation and significant service improvements, is made in âSegmented Strategic Planning: An Incremental Approach to Transit Planning and Implemen- tationâ (12). The authors propose a segmented approachâ rather than attempting a comprehensive organization-wide strategic planning effort, they recommend a âseries of orches- trated, low-level strategic plansâ (12, p. 1). The report describes such an approach in Knoxville, Tennessee, where only the downtown and university campus trolley service was the focus of a recent strategic planning effort. This segmentation made it much more manageable and helped to avoid many of the obstacles that often arise from a larger-scale planning effort. In December 2001, recognizing the potential of strategic planning to improve the effectiveness of transit systems, APTA produced a Strategic Planning Resource Kit (avail- able on CD) (13). Basically, the kit contains copies of the strategic plans of 28 transit systems. STRATEGIC PLANNING AND PERFORMANCE MEASUREMENT Almost all of the literature on strategic planning and man- agement emphasizes the need for linking the process to per- formance measurement. This is necessary to be able to mon- itor whether plans are being achieved and whether corrective action needs to be taken. There is a great deal of literature on the general subject of performance measurement. An excellent and very compre- hensive study of the issue that pertains directly to transit is the 2003 TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System (14). This report provides detailed guidance for developing a system using tra- ditional and nontraditional measures that address both cus- tomer and community issues.

STRATEGIC PLANNING AND ORGANIZATION DEVELOPMENT One of the strengths of strategic management is that it recog- nizes that strategic planning by itself often does little to change an organizationâs direction, because it does not deal with the significant structural, political, and behavioral forces that tend to resist change. This point is made in two articles that argue for the blending or âmarriageâ of strategic planning and management and âOrganization Developmentâ (15, 16). Organization development (OD) seeks to develop and implement planned change strategies aimed at improving an organizationâs effectiveness. It uses many tools or techniques to ensure that an organization has the internal capability to effectively achieve its goals and objectives. These include such activities as goal setting, leadership development, team building, employee feedback surveys, education and train- ing, organizational design, process consultation, coaching and counseling, and development of appropriate reward and recog- nition programs. In âFor Successful Strategic Change: Blend OD Practices with Strategic Managementâ (15, p. 43), Buller suggests that OD tools can be helpful in the following phases of a typical strategic planning process: â¢ Assessing and developing the organizationâs readiness for change, â¢ Facilitating the strategic planning process, â¢ Helping with strategy implementation, and â¢ Developing leadership skills. A similar point is made by Eadie and Steinbacher in âStrategic Agenda Management: A Marriage of Organiza- tional Development and Strategic Planningâ; that is, that tra- ditional strategic planning too often results in âunimple- mentable strategiesâ (16, p. 424). In this article, the authors describe a case study of a large and complex public-sector organization in Ohio. They argue that strategic management can be one of the tools in the larger strategy of OD. More specifically they describe a concept of âstrategic agenda management.â Strategic agenda management basically involves the âcol- lective management of a strategic agenda, which changes as an organizationâs problems and opportunities change.â The authors state that âeffective strategic agenda management requires intensive, continuous, and collective involvement of senior management,â and that a strategic agenda must be highly selective if it is not to overtax the organizationâs abil- ity to manage strategic issues (16, p. 425). The important point is that strategic planning and man- agement must pay close attention to not only the development of strategic initiatives but also to the organizationâs capabil- ity to implement the initiatives. OD tools can be an important resource for developing or enhancing this capability. 10 NEW PARADIGMS IN PUBLIC TRANSPORTATION In the late 1990s, a group of transit industry leaders gathered under the aegis of the TCRP. The reason for this gathering was a shared concern that unless fundamental changes were made, the future of the transit industry was in jeopardy, owing to the significant changes taking place in the world and the inability of the transit industry to adapt to them (e.g., fundamental social and economic changes in this country, an emerging global economy, and the increasing significance of rapid advances in information technology). In 1997, a âfuture search conferenceâ was organized by TCRP and the Eno Foundation to explore this issue. The con- ference included approximately 80 individuals representing transit managers, riders, drivers, labor leaders, political lead- ers, community residents and other important public trans- portation stakeholders. The effort led to a TCRP project that in 2003 culminated in a report that described emerging ânew paradigmsâ that might affect the industry and that outlined a number of suggested responses (17). Many of the responses are such that strategic planning and management would be a key tool for developing and implementing them. The report points out that the transit industry has not changed its basic business model for more than 40 years. It also notes that although in recent years transit ridership has reached post-World War II highs, its market share of trips remains quite small. Moreover, increasing levels of automo- bile traffic are far outpacing the capacity of the street and highway system, thus leading to severe congestion and grid- lock in many areas. The project studied transportation in three key arenas else- where in transportation industries in search of lessons, or new models, for transit. 1. The âlogistics revolutionâ in intermodal freight. 2. The rise of airline âalliances.â 3. New models for organizing and managing transit in Europe. In the freight arena, companies are finding ways to inte- grate their operations to better serve the door-to-door needs of their customers, encourage customer loyalty, and increase efficiency. This involves use of a logistics manager, who coordinates the movement of items from start to finish, and the use of powerful information technologies that allow uni- fied dispatching and tracking. Airlines have followed a very similar strategy, forming alliances with other carriers so that passengers can book trips through a single airline and so that the quality of service can be better tracked. Information tech- nology again plays a key role. Much the same is occurring in European transit. For exam- ple, even though there are a variety of operators that provide service in metropolitan London, London Transport has been empowered to set broad policy on fares, service coverage, and

11 service quality. It tracks efficiency and quality from the cus- tomerâs viewpoint. More recently, organizational changes are being made that give multimodal responsibility (buses, taxis, rail systems, ferries, and traffic control) to a new agencyâ Transport for London. The report goes on to identify four key elements of âthe emerging paradigmâ: 1. Emphasis on monitoring service quality and customer satisfaction across the entire network is replacing the previous emphasis on the operation of a particular agencyâs vehicles and facilities. 2. New collaborative relationships or alliances are replac- ing independent agency operations and head-to-head competition. 3. Traditional assets such as vehicles, passenger terminals, and maintenance facilities are being jointly managed. 4. State-of-the-art information technology is being used to better manage operations, evaluate service quality, and provide customer information. A key theme of the emerging paradigm is that of âmobil- ity management,â the function of meeting the door-to-door travel needs of the customer no matter whose transportation assets are being used. This concept should be a central focus of the strategic planning efforts of transportation agencies. One of the new paradigm project reports includes as an appendix a guide that organizations can use for managing the change process (18). This includes strategies for â¢ Recognizing the need for change, â¢ Leading and planning change, â¢ Making change happen, and â¢ Institutionalizing new approaches. Most of the strategies described would be excellent com- ponents of an effective strategic planning and management process. The final report points out that transit agencies cannot do this alone, and that public policy decisions and public invest- ment must be coordinated in other key areas such as land use strategies, other public infrastructure investments (water, sew- ers, parking facilities, etc.), environmental measures (clean air and energy conservation), and other public policies such as tax incentives and economic development strategies. Because transit agencies have little or no control over these areas, they must work indirectly to encourage such coordination.

TRB’s Transit Cooperative Research Program (TCRP) Synthesis 59: Strategic Planning and Management in Transit Agencies examines the value and benefits of strategic planning and management in transit agencies. The report also provides case studies from five transit agencies based on the comprehensiveness of process or presence of innovative or noteworthy practices.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

DOI: 10.1108/tqm-03-2024-0098
Corpus ID: 270585038

Business excellence assessments – learning from award-winning organizations

Atif Baig , Robin Mann , +1 author Wayne Macpherson
Published in The TQM Journal 17 June 2024

53 References

The development of an organizational excellence architecture model to support the implementation of business excellence, an investigation of the global awareness of business excellence and best practices in promoting the use of business excellence, implementation of business excellence models in healthcare for quality assessment: a systematic review, malcolm baldrige national quality award winners: a quantitative analysis between strategic planning and human resource variables, the business excellence models and business strategy, tracing evolution of efqm and its relationship with industry 4.0, analysis of the interrelationships between enablers and results in king abdullah ii award for excellence model, the efqm 2020 model. a theoretical and critical review, towards a further step in understanding business excellence models: a comparative approach, what makes excellence models excellent: a comparison of the american, european and japanese models, related papers.

Showing 1 through 3 of 0 Related Papers

COMMENTS

(PDF) Making Strategy Work: A Literature Review on the Factors
Institute of Corporate Communication, University of Lugano (USI), Via Buff i 13, 6900 Lugano, Switzerland. 2. Making Strate gy Work: A Literature Review on the Factors. Influencing S trategy ...
Organizational strategy and its implications for strategic studies: A
In this review essay, we want to capitalise on this opportunity by (1) providing a review of organisational strategy literature and (2) bringing it to bear on strategic and security studies. ... A Review of Strategy and Tactics in Business and Management: Recovering the Divide', International Journal of Management Reviews 19/2 (2017), 175 ...
Strategy implementation: A review and an introductory framework
Abstract. Effective strategy implementation is a critical component of organizational success and a potential source of competitive advantage. However, despite many calls for increased attention, research on the subject remains a disparate constellation of recommendations, case studies, and empirical work that provides insight but lacks a ...
A Business Model View of Strategy
Concerns have recently been raised in the broader strategic management literature that the business model literature is not enriching to it. For example, Teece (2010, p. 192) complained that: 'Like other interdisciplinary topics, business models are frequently mentioned but rarely analyzed; therefore, they are often poorly understood'.
Full article: Small business strategic management practices and
2.1. Strategic planning. Since 1990, studies exploring the relationship between strategic planning and performance have produced mixed results (Falshaw, Glaister, & Tatoglu, Citation 2006; Harris, Gibson, & McDowell, Citation 2014; Valčić & Bagarić, Citation 2017).Consequently, researchers have recently focused on what factors may affect the relationship between strategic planning and ...
Sustainable development goals and the strategic role of business: A
Business Strategy and the Environment is a sustainable business journal advancing green business strategy through eco-innovation, green ... Relying on Scopus database consultation, the current research adopts an interdisciplinary systematic literature review to investigate, analyze, and present state-of-the-art academic literature on the role ...
Literature review as a research methodology: An ...
Although rare, examples of this type of review can be identified in the business literature (e.g., Covington, 2000; ... There are many examples of articles that have been successfully published in higher-ranked business journals using a literature review strategy as a basis. Not accounting for the quality of the review itself, there seems to be ...
A Systematic Literature Review on Digital Business Strategy
Abstract. For decades, business-IT alignment has been considered the prevailing framework for aligning IT strategy with business strategy. With the ubiquity of digital technologies, the new ...
The Management of a Business Strategic Plan: A Systematic Literature Review
JEFMS, Volume 5 Issue 06 June 2022 www.ijefm.co.in Page 1776 The Management of a Business Strategic Plan: A Systematic Literature Review The strategic business plan is an important document for every organisation aiming at remaining focused and profitable in its area of operation and it further acts as a guide for the period specified therein.
Six tips for your (systematic) literature review in business and
A literature review is an essential component of almost any research project. It serves as the foundation for advancing knowledge, facilitates theory development, closes mature research areas, and uncovers novel research areas (Webster and Watson 2002).Frank and Hatak refer to a literature review as a "knowledge map", which analyzes and synthesizes prior literature.
(PDF) How to Make an Effective Literature Review in Business
The proposed framework aims to help researchers to plan and make an effective literature review in Business Development, Strategic Management and Technology as it was used successfully in earlier research in this areas (Basias et al., 2018, 2017, 2016a, 2016b, 2015a, 2015b, 2014, 2013, 2012). 5.
Literature Review on Business Strategy and Competitive Analysis
Upon completion of the course, we should be able to understand and have a broader perspective about business strategy. 3 LITERATURE REVIEW 3.1 Articles review Rumelt (2003), stated that strategy discipline for many years has been lacking a clear definition of competitive advantage and a deep understanding of the influence of this construct on ...
A Review of IS Strategy Literature: Current Trends and Future
In this paper, we examine the IS strategy literature since 2008. Using Chen, Mocker, Preston, and Teubner's (2010) review as a starting point, we identified 31 IS strategy studies published since 2008. Further, we evaluated previous frameworks for evaluating IS strategy research.
What Is Strategy, Again?
What Is Strategy, Again? by. Andrea Ovans. May 12, 2015. Post. Buy Copies. If you read what Peter Drucker had to say about competition back in the late '50s and early '60s, he really only ...
Strategic Management System For Competitive Business Performance: A
A literature review was conducted to generate the conceptual framework that equates the independent variables and dependent variables to visualize and confirm the importance of strategic ...
Innovation and international business: A systematic literature review
The query used in this systematic literature review is as it follows: TS = ("Innovation" AND "International Business"). Consequently, TS = Topic; and the search terms "innovation" and "international business" were combined with the Boolean Operator "AND". Therefore, literature search was based on two simultaneous topics ...
PDF Review of Business Growth Models: Research Strategies and Empirical
This meta-analysis functions as a gateway to the research strategies of a broad collection of literature. The analysis reveals the great diversity within the configuration perspective and provides guidelines for future development of the field. Keywords: business growth, growth configurations, stages of growth, research strategies, meta ...
The importance of literature reviews in small business and
The article "A Systematic Literature Review on SME Financing: Trends and Future Directions" by Kumar et al. (Citation 2021) presents a state-of-the-art investigation of the literature on SME financing using the systematic literature review methodology. Their review examines 280 papers from five top journals in the area of small business ...
A systematic literature review towards a conceptual ...
While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted 'Data Science Strategy' (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of 'Data Science' in discrete for various industry sectors and domains ...
Sustainability and financial performance of small and medium sized
Business Strategy and the Environment is a sustainable business journal advancing green business strategy through eco ... A bibliometric analysis and a systematic literature review method were employed by analyzing articles published between 1999 and 2018, using the VOSViewer software. The study provides an overview of articles, authors, the ...
IT-Business Alignment: A Systematic Literature Review
Literature reported IT-business alignment as one of the key enablers for the success of IT in an organization. Given its importance, this study aimed at exploring the reported benefits of IT-business alignment, the proposed methodologies and the challenges in implementing these models. To do so, a systematic literature review was conducted to ...
Chapter Two
4OVERVIEW The literature review revealed that strategic planning is a popular and widespread practice in both the private and pub- lic sectors. It is a leadership and management tool that has been successfully used for decades to help organizations bet- ter prepare for the future and improve long-term performance. ... strategic planning is ...
Sustainable development goals and the strategic role of business: A
This research work carries out a systematic review of the previous literature related to the adoption of SDGs as a corporate strategy in companies, that shows an exponential increase in the papers ...
Business excellence assessments
It offers a unique perspective on how award-winning organizations tailor their strategies to meet diverse business needs and strategic objectives. Skip to search form Skip to main ... (BECs) within a country or region, promote BE. A review of the literature revealed a gap in the understanding of … Expand. 2. Save. Implementation of Business ...
The role of data science and data analytics for innovation: a
Our literature review identified four mutually exclusive categories: "Innovation as a result of DS/DA", "DS/DA as a Result of Innovation", "Diffusion of DS/DA as Technology", and "Miscellaneous". We can draw several implications from these categories' contents and their sizes.
Asia Pacific Management Review
select article Predicting the significance of consumer environmental values, beliefs, and norms for sustainable fashion behaviors: The case of second-hand clothing