Stakeholder Causal Scope Analysis for Strategic Management of Big Data: Implications for the European-Mediterranean Region

Call for papers for: EuroMed Journal of Business

Rationale

Big data, as a research domain has attracted enormous attention in recent decade from researchers of different knowledge-streams. The practical need of analysing data, in contribution of different interconnected knowledge-streams, such as information science, policy and decision making, strategic management, sustainable growth (among others) to enrich a progressive management development capacity in diverse socio-economic settings is well-recognized (Ferraris et al., 2019; Hargreaves et al., 2018; Vassakis et al., 2018). However, scholars are concerned to simplify the overall management structure of data, such as data capture/exploration, data storage, data visualization, inflow and outflow of data (Chen and Zhang, 2014; Bikakis et al., 2018) to make on-time decision, based on right data for the right target audience. As Tabesh et al. (2019) underline, many companies have failed to incorporate big data analytics (BDA) into their decision making processes; our ongoing commitment and support, communication, and the development of knowledge on big data are required for an effective implementation of a strategy based on BDA. It is therefore clear that we need to underpin our knowledge to understand how BDA can create strategic value for an organization and its impact on competitive advantage (Grover et al., 2018). It gives us a strategic management problem from both research and managerial perspectives of big data management. For example, which data we should explore and store, related to our different organizational issues (e.g. challenges and opportunities), how long we should store specific datasets, what would be the sequence and duration of data inflow and outflow to visualize and analyse the right data, how big data tracking system could be exploited for pattern mining to explore stakeholder behaviour, personal data and privacy, fake news and data theft, neutralizing the prospective variance in data analytics and factual error, and ethical issues in  data analytics and management are the crucial concerns (Bello-Orgaz et al., 2016; Shams and Solima, 2019; Shams et al., 2020),

Big data and analytics are also challenging existing modes of business and well-established companies. Yet, there is limited understanding of how organizations need to change to embrace these technological innovations, and the business shifts they entail. Even more, the business value and strategic relevance of big data and analytics technologies still remain largely underexplored. (Mikalef et al., 2017, np)

As a result, for data centred strategic decision making, diverse challenges appear in big data exploration/identification, visualization, storage, pattern mining and analytics. It adversely impacts on the contemporary decision making process related to business, healthcare and other sectors as well (Hilbert, 2016). For example, indicating to the scandal of Facebook’s data mismanagement, McNamee and Parakilas, (2018) reported that “it was not just the user’s profile data that was harvested, but also that of their friends” (np). In terms of improper management in between multiple data streams and data (pattern) mining, a spokesperson from Tesco described that “we’re currently experiencing an IT issue which is affecting some online grocery shopping orders. We’re working hard to fix this problem and apologise to customers for any inconvenience this may cause” (McDonald, 2017, np), as the IT system misleads data from multiple data streams of customers’ online orders. In practice, there are more examples of mismanagement of data from multiple data streams, which fail to develop an appropriate algorithm of data inflow and outflow, in order to track, explore, visualize and analyse the right data from multiple data sources to make the right decision, at the right time and targeting the right stakeholder(s). For instance, “the problem of multiple data streams – Thomson Reuters is not alone getting burned by the management of multiple data streams. Earlier this year LexisNexis was sued when a customer found errors in a paper back code volume” (O'Grady, 2017, np). As a result, in real-life business world, the absence of a context-based scalable large data management structure often leads to wrong decision making (Barth, 2017).

Similar to many other sectors, the healthcare sector is also affected because of the absence of a proper scalable data exploration and visualization approach. For example, the key challenges in big data analytics in the healthcare sector are documented as, data inconsistency and instability, data quality (volume, variety, velocity and veracity), limitation of observational data, data validation against a particular context and data analysis perspectives (Rumsfeld et al., 2016). In the manufacturing sector, big data management also “raises challenges for organizations with regard to data storage, analysis and processing” (Dubey et al., 2016, p. 632). Indeed, in this sector, if appropriate data management skills (data acquisition, access, analytical skills, etc.) can be adopted, it can be instrumental to plan, implement and monitor a strategy based on BDA that can improve organisational efficiency, business performance and value for manufacturing firms (Popovič et al., 2018). Also, the agricultural and food business sector has received relatively less attention from BDA researchers; although the sector is undergoing a digital revolution (Bronson and Knezevic, 2016). In this field big data can take a relevant importance as they allow to reproduce the long-standing relationships among the food system actors, ensuring an effective traceability system which guarantee quality, safety, and sustainability of agri-food products and processes (Xu et al., 2020; Kamilaris et al., 2017). In line with this, Singh and El-Kassar (2019) emphasize the need to integrate the management of supply chain, with the management of human resources and big data to improve the sustainable performance of companies.

Aim and theme 
“In practice, organizations need to continuously realign work…(with) stakeholder interests in order to reap the benefits from big data” (Günther et al., 2017, p. 191). Since an organization attempts to explore, visualize and analyse data about its stakeholders, developing a strategic data mining pattern based on analysing the “cause and consequence of stakeholder relationships and interactions as a stakeholder causal scope (SCS)” (Shams et al., 2020; Shams, 2016a, p. 141) would be instrumental to establish non-complex, scalable, effectual, customized and interactive data exploration and visualization methods from multiple data-streams. For example, focusing on a particular service encounters or any other kind of business interactions between a business firm and its customers and other stakeholders (e.g. shareholders, suppliers, among others), the business firm usually gathers market data related to its diverse stakeholders’ (including customers’) behaviour/perception (Giacomarra et al., 2019; Shams and Solima, 2019). In such business encounters, focusing on specific stakeholder relationship management (RM) constructs would be useful to collect/explore, store and visualize particular dataset, where the specific RM constructs would be used to categorizing different datasets, in order to abstracting/conceptualizing insights, based on that specific RM construct to undertake stakeholder-specific management decisions (Belyaeva et al., 2020; Shams and Solima, 2019; Shams, 2016b). Different established and emergent RM constructs would include, but not limited to trust (Blenkhorn and Mackenzie, 1996; Moliner et al., 2007), satisfaction (Crosby et al., 1990; Macintosh, 2007), commitment (Dwyer et al., 1987; Patrick and Vesna, 2010), communication (Gummesson, 1994; Parasuraman et al., 2005), reciprocity and co-creation (Fontenot and Wilson, 1997), reliability (Parasuraman et al., 1985; Bennett and Barkensjo, 2005); responsiveness (Parasuraman et al., 1991; Bennett and Barkensjo, 2004), bond (Wilson and Mummalaneni, 1986; Lang and Colgate, 2003), loyalty (Berry, 1995; Dimitriadis and Stevens, 2008) and so forth.

In this context, and centring on the the key focus of the EuroMed Journal of Business, this special issue aims to enhance our understanding on how analysing stakeholder relationships based on different RM constructs in the European and Mediterranean socio-economic settings would be instrumental to establish non-complex stakeholder-specific strategic data mining patterns. And how establishing such data mining patterns could ensure a stable practice of effectual and interactive data exploration and visualization method from multiple data-streams, as a practice of evaluating large pre-existing (and embryonic) databases in order to generate original insights, which is scalable and offers novel socio-economic and/or business value of existing and new datasets. 

The guest editors welcome both conceptual and empirical (qualitative, quantitative or mixed) studies that concerns how analysing different established and emergent RM constructs would be instrumental to simplify the process in data exploration, visualization and analysis, based on diverse data sources, in order to develop insights for right management decision, at the right time, based on the right data and targeting the right stakeholders. Some examples of the prospective themes are discussed below; however, they are not the comprehensive topics: 
-    stakeholder theory and its implications on stakeholder-centred big data management for the European and Mediterranean region’s socio-economic development;
-    implications of different established and emergent RM constructs to streamline big data management system and its implications for the European and Mediterranean region;
-    SCS analysis and big data management, and its impact on the socio-economic or ecological issues in the European and Mediterranean;
-    stakeholder relationships, and comparative studies across different markets, sectors and industries to underpin the big data management process to address the European and Mediterranean region’s socio-economic challenges; 
-    the IT-enhanced business and management platforms from the European and Mediterranean region, and its implications for stakeholder relationship management and big data analytics;
-    cross-cultural stakeholder management across the European and Mediterranean region and its impact on big data analytics and management;
-    influence of different geopolitical, environmental and non-government stakeholders across the European and Mediterranean region and beyond, and its impact on big data management;
-    ethical and legal concerns in big data management and implications of RM constructs to address the online privacy policy focusing on the European and Mediterranean region;
-    insights from the diverse extents of different stakeholders’ perceptions on various management issues (e.g. corporate social responsibility, cause-related marketing, public relations etc.) for big data exploration, visualization and analysis, and the European and Mediterranean contexts of such insights;
-    the intersection between different strategic management theories and concepts (e.g. resource-based view, dynamic capabilities, organisational sustainability etc.) and stakeholder engagement for big data management, and the European and Mediterranean contexts of such studies;
-    public and private sector collaboration and stakeholder relationship management in the European and Mediterranean region, and its impact on big data analytics;
-    Big data and agro food system players: really a great investment opportunity? 
-    stakeholder engagement in smart city models in the European and Mediterranean countries, and big data management for current and future use of data;
-    knowledge management across the European and Mediterranean countries and beyond, and knowledge transfer between different stakeholders and big data analytics; 
-    the European and Mediterranean contexts of cross-disciplinary and cross-functional studies on stakeholder relationship management and big data analytics. 


Timeline

Opening for receipt of full-text manuscripts is July 15, 2021
The closing date for receipt of full-text manuscripts is October 15, 2021


Author guidelines

Papers submitted to this issue will undergo a double-blind peer review process. Manuscript should be formatted as per the Journal’s guidelines and submitted online by the Journal’s online manuscript portal. More information for manuscript preparation and online submission is available here: https://www.emeraldgrouppublishing.com/journal/emjb#author-guidelines .

To submit this special issue, authors should select this special issue title while submitting online. 

Guest editors

Dr. Riad Shams, Newcastle Business School, Northumbria University, UK, E-mail: [email protected]

Associate Professor Antonino Galati (Managing Guest Editor), University of Palermo, Italy, E-mail:  [email protected] 

Professor Darko Vukovic, Finance and credit department, Faculty of Economics, People’s Friendship University of Russia (RUDN University), Russia and Geographical Institute “Jovan Cvijic” SASA, Serbia, E-mail: [email protected] , [email protected]  

Dr. Giuseppe Festa, Department of Economics and Statistics, University of Salerno (Italy), E-mail: [email protected]


References

Barth, P. (2017). Data agility or scale: A false choice? Available at https://www.cio.com/article/3243565/data-management/data-agility-or-scale-a-false-choice.html (accessed on 02 January, 2018). 
Bello-Orgaz, G., Jung, J. J, and Camachoa, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45 – 59.
Belyaeva, Z., Shams, S. R., Santoro, G., & Grandhi, B. (2020). Unpacking stakeholder relationship management in the public and private sectors: the comparative insights. EuroMed Journal of Business, 15(3), pp. 269-281.
Benett, R. and Barkensjo, A. (2005). Relationship quality, relationship marketing, and client perceptions of the levels of service quality of charitable organisations.  International Journal of Service Industry Management, 16 (1), 81-106.
Bennett, R., and Barkensjo, A. (2004). Causes and consequences of donor perceptions of the quality of the relationship marketing activities of charitable organisations.  Journal of Targeting, Measurement and Analysis for Marketing, 13 (2), 122-129.
Berry, L. L. (1995). Relationship marketing of services – Growing interest, emerging perspectives. Journal of the Academy of Marketing Science, 23 (4), 236-245.
Bikakis, N., Papastefanatos, G. and Papaemmanouil, O. (2018). Big data exploration, visualization and analytics. Big Data Research.  Available at https://www.journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-big-data-exploration-visualization-and-anal (retrieved on 04 January, 2018). 
Blenkhorn, D. and Mackenzie, H.F. (1996). Interdependence in relationship marketing. Australasian Marketing Journal, 4 (1), 25-30.
Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 2053951716648174.
Chen, P. and Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 10, 314 – 347. 
Crosby, L. A., Kenneth, E. R. and Deborah, C. (1990). Relationship quality in services selling: An interpersonal influence perspective.  Journal of Marketing, 54, 68-81. 
Dimitriadis, S. and Stevens, E. (2008). Integrated customer relationship management for service activities: An internal/external gap model. Managing Service Quality: An International Journal, 18 (5), 496-511.
Dubey, R., Gunasekaran, A., Ghilde, S. J., Wamba, S. F. and Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. International Journal of Advanced Manufacturing Technology, 84, 631 – 645. 
Dwyer, F. R., Schurr, P. H. and Oh, S. (1987). Developing buyerseller relationships. Journal of Marketing, 51, 11-28.
Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), pp. 1923-1936
Fontenot, R. and Wilson, E. (1997). Relational exchange: A review of selected models for a prediction matrix of relationship activities. Journal of Business Research, 39 (1), 5-12.
Giacomarra, M., Crescimanno, M., Sakka, G., & Galati, A. (2019). Stakeholder engagement toward value co-creation in the F&B packaging industry. EuroMed Journal of Business, 15(3), pp. 315-331
Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423. 
Gummesson, E., (1994). Making relationship marketing operational. International Journal of Service Industry Management, 5 (5), 5-20.
Günther, W. A., Rezazade, M. H., Huysman, M. M. and Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26, 191 - 209.
Hargreaves, I., Roth, D., Karim, M. R., Nayebi, M. and Ruhe, G. (2018). Effective customer relationship management at ATB financial. A case study on industry-academia collaboration in data analytics. Studies in Big Data, 27, 45 – 59.
Hilbert, M. (2016). Big data for development: A review of promises and challenges. Development Policy Review, 34(1), 135–174.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.
Lang, B. and Colgate, M. (2003). Relationship quality, on-line banking and the information technology gap. International Journal of Bank Marketing, 21 (1), 29-37.
Macintosh, G. (2007). Customer orientation, relationship quality, and relational benefits to the firm. Journal of Services Marketing, 21 (3), 150-159.
McDonald, C. (2017). Tesco customers miss out on deliveries due to tech fault. Available at http://www.computerweekly.com/news/450421076/Tesco-customers-miss-out-on-deliveries-due-to-tech-fault (accessed on 20 December, 2017). 
McNamee, R. and Parakilas, S. (2018). The Facebook breach makes it clear: Data must be regulated. Available at https://www.theguardian.com/commentisfree/2018/mar/19/facebook-data-cambridge-analytica-privacy-breach (accessed on 12 April, 2018). 
Mikalef, P., Pappas, I. O., Pavlou, P. A. and Krogstie, J. (2017). Big data analytics and business value. Information and Management. Available at: https://www.journals.elsevier.com/information-and-management/call-for-papers (retrieved 15 November, 2017). 
Moliner, M.A., Sánchez, J., Rodriguez, R.M. and Callarisa, L. (2007). Relationship quality with a travel agency: The influence of the postpurchase perceived value of a tourism package. Tourism and Hospitality Research, 7 (3-4), 194-211.
O’Grady, J. (2017). Breaking news on bad data: Thomson Reuters discovers data error in their monitor suite litigation analytics. Available at https://www.deweybstrategic.com/2017/08/breaking-news-bad-data-thomson-reuters-discovers-data-error-monitor-suite-litigation-analytics.html (accessed 12 November, 2017). 
Parasuraman, A., Berry L. L. and Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67, 420-450.
Parasuraman, A., Zeithaml, V. A. and Berry L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49, 41-50.
Parasuraman, A., Zeithaml, V. A. and Malhotra, A. (2005). E-S-QUAL-A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7 (3), 213-233.
Patrick, V. and Vesna, Z. (2010). Relationship quality evaluation in retailers’ relationships with consumers. European Journal of Marketing, 44 (9-10), 1334-1365.
Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20(2), 209-222.
Rumsfeld, J. S., Joynt, K. E. and Maddox, T. M. (2016). Big data analytics to improve cardiovascular care: Promise and challenges. Nature Reviews Cardiology, 13, 350–359.
Shams, S. M. R. (2016b). Capacity building for sustained competitive advantage: A conceptual framework. Marketing Intelligence & Planning, 34 (5), 671 – 691.
Shams, S. M. R. and Solima, L. (2019). Big data management: Implications of dynamic capabilities and data incubator. Management Decision, 57(8), 2113-2123.
Shams S.M.R., Vrontis D., Weber Y., Tsoukatos E., Galati A. (2020). Stakeholder Engagement and Sustainability. Routledge, 214 pp.
Shams. S. M. R. (2016a). Branding destination image: A stakeholder causal scope analysis for internationalisation of destinations. Tourism Planning & Development, 13 (2), 140-153.
Singh, S. K., & El-Kassar, A. N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner production, 213, 1264-1273.
Tabesh, P., Mousavidin, E., & Hasani, S. (2019). Implementing big data strategies: A managerial perspective. Business Horizons, 62(3), 347-358.
Vassakis K., Petrakis E., Kopanakis I. (2018). Big data analytics: Applications, prospects and challenges. Data Engineering and Communications Technologies, 10, 3 – 20.
Wilson, D. T. and Mummalaneni, V. (1986). Bonding and commitment in buyer-seller relationships: A preliminary conceptualisation.  Industrial Marketing and Purchasing, 1 (3), 44-58.
Xu, J., Guo, S., Xie, D., & Yan, Y. (2020). Blockchain: A new safeguard for agri-foods. Artificial Intelligence in Agriculture, 4, 153-161.