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British Journal of Management Special Issue 2019 CALL FOR PAPERS BIG DATA AND PERFORMANCE

29th August 2017

British Journal of Management Special Issue 2019
CALL FOR PAPERS
BIG DATA AND PERFORMANCE

Guest Editors:

Paper submission deadline: 1st December 2017

The amount of data that businesses collect and produce daily has dramatically exploded over the last five years. According to industry experts, even the average SME holds around 1000 terabytes of data at any given time. The data that businesses collect come in different shapes and formats but they all share a common characteristic: they can offer businesses invaluable insights about their customers and processes which can ultimately help them to gain some competitive advantage over their competitors if correctly used and interpreted (Kubina et al., 2015; McKinsey Global Institute, 2011). Unsurprisingly, then, Big Data have been heralded as the new frontier for growth and innovation as practitioners and researchers agree that Big Data allow businesses to leverage data-driven strategies to innovate, compete and ultimately create value (McKinsey Global Institute, 2011). Academic research has shown that businesses that adopt data-driven strategies tend to be more productive and profitable than their competitors (Brynjolfsson et al., 2011).

While it is clear that Big Data can offer tangible benefits to businesses across all industries, our understanding of the internal mechanisms through which Big Data-driven strategies can enhance business growth is very limited. Academic literature has suggested that businesses which have incorporated Big Data in their decision making processes share some specific characteristics (McAfee and Brynjolfsson, 2012. See also Berner et al., 2014 and their discussion on how Big Data require businesses to move away from the command and control hierarchies) but no attempt has been made to develop a unified body of knowledge in this area. Still this is an important subject to research as it will offer important insights on how the internal processes of a business can mediate the causal relationship between the adoption of data-driven strategies and business growth.

The British Journal of Management (BJM) will publish a special issue on “Big Data and Performance” in 2019 with a short collection of research papers that complement and enrich the existing body of knowledge around the impact that Big Data have on business growth by examining not only outcomes such as profitability and productivity but also internal factors (such as core competencies, internal processes etc.) that drive these outcomes. We hope that the collection of papers in this special issue will help the ongoing dialogue among scholars from a diverse set of disciplines that share an interest in the Big Data phenomenon. We also hope that the special issue will help develop a unified body of knowledge around the impact that Big Data have on businesses. In addition, we hope this special issue will offer executive and managers some guidance to assess the conditions under which exploitation of Big Data can add business value to organizations.

The Call for papers welcomes theoretical, comparative and empirically based submission around the theme of “Big Data and Performance”. We encourage contributions that address the potential issues outlined below but the list is not exhaustive. Illustrative topics include:

Theorizing the impact of Big Data on businesses. Existing theoretical research on Big Data has been commonly rooted in the area of IT management. In turn this has used the Dynamic Capabilities approach to assess the impact of Big Data on organizational performance. Rooted in the Knowledge Based View, proponents argue that the dynamic capabilities enable firms to modify their resource to adapt rapidly to changing conditions, helping them to sustain their competitive advantage over time (Teece et al., 1997). Is the dynamic capability view the only theoretical lens that allows to map the impact of Big Data on an organization? What are the limitations of this theory? Are there alternative theories that can provide a robust understanding of how Big Data can affect organizations?

Big Data and Core Competencies. What are the core competencies for growth that in a world of Big Data? McAfee and Brynjolfsson (2012) put forward personnel management, technology infrastructure, and corporate decision making as critical capabilities across organizations that want to exploit Big Data. Similarly, Davenport et al. (2012) highlight that the management, people and technology dimensions can enhance broader firm performance that aims at extracting useful insights from their Big Data. There are though some obvious omissions in this area. For instance, organizational knowledge such as operational routines, skills, and know-how constitutes a key source of competitiveness but very little is known about its role in the context of Big Data exploitation. How important is organizational knowledge in a world of Big Data? Are there other core competencies organizations need to acquire before they can benefit from Big Data strategies? How do businesses reconfigure core competencies to incorporate insights from Big Data-enabled strategies? Given the fact that each stage of growth is characterized by its own strategic goals, organizational and management systems, how does this reconfiguration process vary along the life-cycle of a business given the fact that its strategic goals may change over time? Do we observe differences among SMEs and large firms?

Big Data and High-growth. Over the last few years, researchers have increasingly become interested in high-growth and its link to employment growth in developed countries (Schreyer, 2000; Davidsson and Henrekson, 2002; Delmar et al., 2003). High-growth firms have ten or more employees and typically experience above average growth over three years (i.e., the OECD definition of a HGF). Are firms that adopt data-driven strategies more likely to become high-growth firms and why? Are there internal factors that mediate this relationship? Are there significant differences among old and young firms in this respect?

Big Data and Innovation. It is commonly believed that Big Data can generate innovation along the Big Data value chain. What is the nature of innovation in a world of Big Data? Innovation is not developed in isolation but it is relies on the business’ interactions with lead users, suppliers, competitors and with a range of institutions (including Universities). What are the processes and the resources associated to the management of the links with this variety of actors and institutions along the Big Data value chain? What is the best collaborative framework (i.e. joint venture etc.) to support data-driven innovation in these networks? How do firms protect their intellectual property in this new environment?

Organizational Structure, Skills and Leadership. Academic literature has suggested that businesses which have incorporated data-driven strategies in their decision making processes share a common structure which is different from the standard command and control hierarchies. What are the best internal structures that facilitate the development of Big Data-based strategies? What is the role of the senior management team in facilitating the development of these strategies? What are the characteristics of the senior leadership in businesses that have adopted data-driven strategies? Do SMEs and large businesses differ in this respect? What skills need the senior management to be equipped with so that it can benefit from the introduction of data-driven strategies?

Big Data and International Business. Big Data tend to be associated to operations of large and digital companies that operate in more than one country even if this is not necessarily the case. However, very little is known about how multinationals use Big Data and whether the way they engage with Big Data differs from smaller companies. What are theoretical and methodological advances Big Data that might have extra implications for multinational enterprises and their activities in global markets? What impacts big data may have on internationalization patterns of young, agile, entrepreneurial firms compared to mature multinationals? What are the theoretical implications of big data on internationalization theories and market entry modes?

SUBMISSION PROCESS
BJM is published by the British Academy of Management and provides an outlet for research and scholarship on management- orientated themes and topics. It publishes articles of a multi-disciplinary and interdisciplinary nature as well as empirical research from within traditional disciplines and managerial functions. With contributions from around the globe, the Journal includes articles across the full range of business and management disciplines. High quality papers that do not make the final set of papers for the special issue may be considered for publication in a regular issue of the journal.

Contact Professor Vania Sena (vsena@essex.ac.uk) or Professor Mehmet Demirbag (mdemirc@essex.ac.uk) for additional information.

  • Deadline for paper submissions: 1st December 2017

  • Notification of acceptances: October 2018

  • Special issue published: January 2019

Authors should ensure they adhere to the journal guidelines which are available at: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467- 8551/homepage/ForAuthors.html

Submissions should be uploaded to the BJM ScholarOne Manuscripts site at http://mc.manuscriptcentral.com/bjm (by midnight UK time). Authors should select ‘special issue paper’ as the paper type, ensure they answer ‘yes’ to the question ‘Is this submission for a special issue’ and enter the title of the special issue in the box provided.

References
Berner, M., Graupner, E. and Maedche, A., (2014). ‘The information panopticon in the Big Data era’, Journal of Organisation Design, 3 (1), 14-19.
Brynjiolfsson, E., Hill, Lorin and Kim, H.H., (2011). ‘Strength in numbers: How does data-driven decision-making affect firm performance’, MIT Sloan Working Paper, Cambridge, Mass.
Davenport, T. H., Barth, P., & Bean, R. (2012). ‘How ‘Big Data’ is different’, MIT Sloan Management Review, 54, 43–46.
Davidsson, P., Henrekson, M. (2002). ‘Determinants of the prevalence of start-ups and high-growth firms’, Small Business Economics, 19, 81-104.
Delmar, F., Davidsson, P., Gartner, W.B. (2003). Arriving at the high-growth firm, Journal of Business Venturing, 18, 189-216.
Kubina, M., Varmus, M. and Kubinova, I. (2015). ‘Use of Big Data for competitive advantage of company’, Procedia Economics and Finance, 26, 561-565.
McAfee, A. and Brynjolfsson, (2012). ‘Big Data: the management revolution’, Harvard Business Review, October, Cambridge, Mass.
McKinsey Global Institute, (2011). ‘Big Data: The next frontier for innovation’, Competition and Productivity.
Schreyer, P. (2000). ‘High-growth firms and employment, OECD Science’, Technology and Industry Working Paper No. 2000/03.
Teece, D. J., Pisano, G., & Shuen, A. (1997). ‘Dynamic capabilities and strategic management’. Strategic Management Journal, 18(7), 509–533.
 

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