Building supply chain risk resilience

Benchmarking - Tập 26 Số 7 - Trang 2318-2342 - 2019
Nitya Singh1, Shubham Singh2
1Department of Management and Marketing, Franklin P. Perdue School of Business, Salisbury University, Salisbury, Maryland, USA
2Department of Operations and Supply Chain Management, Monte Ahuja College of Business, Cleveland State University, Cleveland, Ohio, USA

Tóm tắt

Purpose

The purpose of this paper is to examine how firms can develop business risk resilience from supply chain disruption events, by developing big data analytics (BDA) capabilities within their organization. The authors test whether BDA mediates the impact of institutional response to supply chain disruption events, and information technology infrastructure capabilities (ITICs), on firm’s ability to develop risk resilience from supply chain disruption events.

Design/methodology/approach

The study is based on survey data collected from 225 firms, spread across several sectors in the USA and Europe. The respondents are primarily senior and middle management professionals who have experience within the information technology (IT) and supply chain domain. Validity and reliability analyses were performed using SPSS and AMOS; and covariance-based structural equation modeling was used to test the hypothesis.

Findings

The analysis reveals two significant findings. First, the authors observe that institutional experience with managing supply chain disruption events has a negative impact on firm’s ability to develop business risk resilience. However, if the organizations adopt BDA capabilities, it enables them to effectively utilize resident firm knowledge and develop supply chain risk resilience capacity. The results further suggest that BDA positively adds to an organization’s existing IT capabilities. The analysis shows that BDA mediates the impact of ITIC on the organization’s ability to develop risk resilience to supply chain disruption events.

Originality/value

This study is one of the few works that empirically validate the important role that BDA capabilities play in enabling firms develop business risk resilience from supply chain disruption events. The study further provides a counterpoint to the existing perspective within the supply chain risk management literature that institutional experience of managing past supply chain disruption events prepares the organization to deal with future disruption events. This paper adds to our understanding of how, by adopting BDA capabilities, firms can develop supply chain risk resilience from disruption events.

Từ khóa


Tài liệu tham khảo

2016, Big data research in information systems: toward an inclusive research agenda, Journal of the Association for Information Systems, 17, 1

Acharya, A., Singh, S.K., Pereira, V. and Singh, P. (2018), “Big data, knowledge co-creation and decision making in fashion industry”, International Journal of Information Management, Vol. 42 No. 10, pp. 90-101.

2018, Supply chain resilience: a dynamic and multidimensional approach, The International Journal of Logistics Management, 29, 1451, 10.1108/IJLM-04-2017-0093

2015, Investigating the determinants of big data analytics (BDA) adoption in emerging economies, Academy of Management Proceedings, 2015, 11290, 10.5465/ambpp.2015.11290abstract

2014, Dynamic capability building in service networks: an exploratory case study, Journal of New Business Ideas & Trends, 12, 27

2016, IT infrastructure and competitive aggressiveness in explaining and predicting performance, Journal of Business Research, 69, 4667, 10.1016/j.jbusres.2016.03.056

Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J. (2016), “How to improve firm performance using big data analytics capability and business strategy alignment?”, International Journal of Production Economics, Vol. 182 No. 12, pp. 113-131.

Altay, N., Gunasekaran, A., Dubey, R. and Childe, S.J. (2018), “Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view”, Production Planning and Control, Vol. 29 No. 14, pp. 1158-1174.

2015, Firm’s resilience to supply chain disruptions: scale development and empirical examination, Journal of Operations Management, 33-34, 111, 10.1016/j.jom.2014.11.002

2016, HR and analytics: why HR is set to fail the big data challenge, Human Resource Management Journal, 26, 1, 10.1111/1748-8583.12090

1977, Estimating nonresponse bias in mail surveys, Journal of Marketing Research, 14, 396, 10.1177/002224377701400320

2017, An exploratory study on supply chain analytics applied to spare parts supply chain, Benchmarking, 24, 1571, 10.1108/BIJ-04-2016-0053

2015, Big data computing and clouds: trends and future directions, Journal of Parallel and Distributed Computing, 79-80, 3

1988, On the evaluation of structural equation models, Journal of the Academy of Marketing Science, 16, 74, 10.1007/BF02723327

1991, Firm resources and sustained competitive advantage, Journal of Management, 17, 99, 10.1177/014920639101700108

1986, The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations, Journal of Personality and Social Psychology, 51, 1173, 10.1037/0022-3514.51.6.1173

2018, Technological change, information processing and supply chain integration: a conceptual model, Benchmarking, 25, 1279, 10.1108/BIJ-03-2016-0039

2015, Introducing social media for knowledge management: determinants of employees’ intentions to adopt new tools, Computers in Human Behavior, 48, 290

2015, IT impact on talent management and operational environmental sustainability, Information Technology and Management, 16, 207, 10.1007/s10799-015-0226-4

1966, The Social Construction of Knowledge: A Treatise in the Sociology of Knowledge

2011, Understanding responses to supply chain disruptions: insights from information processing and resource dependence perspectives, Academy of Management Journal, 54, 833, 10.5465/amj.2011.64870145

2018, Understanding the value of big data in supply chain management and its business processes., International Journal of Operations & Production Management, 38, 1589, 10.1108/IJOPM-05-2017-0268

2001, Structural Equation Modeling with AMOS: Basic Concepts, Applications and Programming

2017, Select the best supply chain by risk analysis for Indian industries environment using MCDM approaches, Benchmarking, 24, 1400, 10.1108/BIJ-09-2015-0090

2014, IT capability and organizational performance: the roles of business process agility and environmental factors, European Journal of Information Systems, 22, 326

2014, Reducing the risk of supply chain disruptions, MIT Sloan Management Review, 55, 73

2019, Adoption of green practices throughout the supply chain: an empirical investigation, Benchmarking: An International Journal

1979, A paradigm for developing better measures of marketing constructs, Journal of Marketing Research, 16, 64, 10.1177/002224377901600110

2017, Assessing business value of big data analytics in European firms, Journal of Business Research, 70, 379

2007, The severity of supply chain disruptions: design characteristics and mitigation capabilities, Decision Sciences, 38, 131, 10.1111/j.1540-5915.2007.00151.x

2010, Analytics at Work: Smarter Decisions, Better Results

2009, Optimal structure, market dynamism, and the strategy of simple rules, Administrative Science Quarterly, 54, 413, 10.2189/asqu.2009.54.3.413

1983, The iron cage revisited: collective rationality and institutional isomorphism in organizational fields, American Sociological Review, 48, 147, 10.2307/2095101

Dubey, R., Gunasekaran, A., Childe, S.J., Fosso Wamba, S., Roubaud, D. and Foropon, C. (2019), “Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience”, International Journal of Production Research, available at: https://doi.org/10.1080/00207543.2019.1582820 (accessed June 6, 2019).

2019, Antecedents of resilient supply chains: an empirical study, IEEE Transactions on Engineering Management, 66, 8, 10.1109/TEM.2017.2723042

2018, Big data and predictive analytics in humanitarian supply chains., International Journal of Logistics Management, 29, 485, 10.1108/IJLM-02-2017-0039

2009, Dynamic capabilities: current debates and future directions, British Journal of Management, 20, S1

2008, Resource configuration in family firms: linking resources, strategic planning and technological opportunities to performance, Journal of Management Studies, 45, 26, 10.1111/j.1467-6486.2007.00717.x

2000, Dynamic capabilities: what are they?, Strategic Management Journal, 21, 1105

2018, Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices, Technological Forecasting and Social Change, 144, 483

2018, The impact of big data analytics and data security practices on service supply chain performance, Benchmarking, 25, 4009, 10.1108/BIJ-07-2017-0194

Fortune (2016), “Toyota, other major Japanese firms hit by quake damage, supply disruptions”, Fortune, available at: http://fortune.com/2016/04/17/toyota-earthquake-disruptions/ (accessed June 6, 2019).

2014, Factors influencing knowledge transfer between NPD teams: a taxonomic analysis based on a sociotechnical approach, R&D Management, 45, 1

2012, Social enterprises and social categories, Social Enterprises, 47

2018, Evaluating the key performance indicators for supply chain information system implementation using IPA model, Benchmarking, 25, 1844, 10.1108/BIJ-03-2017-0041

Gaskin, J. (2016), “Confirmatory factor analysis”, Gaskination’s StatWiki, available at: www.youtube.com/watch?v=Y7Le5Vb7_jg.c (accessed April 18, 2019).

2014, Business analytics: radical shift or incremental change?, Communications of the Association for Information Systems, 34, 287

Glover, J.L., Champion, D., Daniels, K.J. and Dainty, A.J.D. (2014), “An institutional theory perspective on sustainable practices across the dairy supply chain”, International Journal of Production Economics, Vol. 152 No. 6, pp. 102-111.

2015, Supply risk management and competitive advantage: a misfit model, International Journal of Logistics Management, 26, 459, 10.1108/IJLM-05-2013-0062

2017, Big data and predictive analytics for supply chain and organizational performance, Journal of Business Research, 70, 308

2016, Toward the development of a big data analytics capability, Information and Management, 53, 1049, 10.1016/j.im.2016.07.004

2010, Multivariate Data Analysis, 7th ed.

2013, Organization Theory. Modern, Symbolic, and Postmodern Perspectives

2013, Introduction to Mediation, Moderation, and Conditional Process Analysis

2018, Back in business: operations research in support of big data analytics for operations and supply chain management, Annals of Operations Research, 270, 201, 10.1007/s10479-016-2226-0

1973, Resilience and stability of ecological systems, Annual Review of Ecology and Systematics, 4, 1, 10.1146/annurev.es.04.110173.000245

2019, Integrative qualities and dimensions of social commerce: toward a unified view, Information & Management, 56, 249, 10.1016/j.im.2018.09.003

Isabel, M., Bravo, R., Javier, F., Montes, L. and Ruiz, A. (2017), “The management of operations open innovation and quality management: the moderating role of interorganisational IT infrastructure and complementary learning styles”, Production Planning & Control, Vol. 28 No. 9, pp. 744-757.

2018, Strategic framework towards measuring a circular supply chain management, Benchmarking, 25, 3238, 10.1108/BIJ-11-2017-0304

2009, An analysis of job dissatisfaction and turnover to reduce global supply chain risk: evidence from China, Journal of Operations Management, 27, 169, 10.1016/j.jom.2007.09.002

2017, Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management, International Journal of Operations & Production Management, 37, 10, 10.1108/IJOPM-02-2015-0078

2008, Framing contests: strategy making under uncertainty, Organization Science, 19, 729, 10.1287/orsc.1070.0340

2013, Extending the use of institutional theory in operations and supply chain management research, International Journal of Operations & Production Management, 33, 1318, 10.1108/IJOPM-10-2011-0364

2007, Bridging organization theory and supply chain management: the case of best value supply chains, Journal of Operations Management, 25, 573, 10.1016/j.jom.2006.05.010

2006, SME adoption of IT: the case of electronic trading systems, IEEE Transactions on Engineering Management, 53, 275, 10.1109/TEM.2006.872251

2000, The role of intuition in strategic decision making, Human Relations, 53, 57, 10.1177/0018726700531004

2012, Knowledge complementarity and knowledge exchange in supply channel relationships, International Journal of Information Management, 32, 35, 10.1016/j.ijinfomgt.2011.05.002

2011, Methodology in the Social Sciences. Principles and Practice of Structural Equation Modeling

2009, Proactive planning for catastrophic events in supply chains, Journal of Operations Management, 27, 141, 10.1016/j.jom.2008.06.002

2014, The effect of performance measurement systems on firm performance: a cross-sectional and a longitudinal study, Journal of Operations Management, 32, 313, 10.1016/j.jom.2014.06.003

Lamba, K. and Singh, S.P. (2017), “Big data in operations and supply chain management: current trends and future perspectives”, Production Planning &Control, Vol. 28 Nos 11-12, pp. 877-890.

2018, Modeling big data enablers for operations and supply chain management., International Journal of Logistics Management, 29, 629, 10.1108/IJLM-07-2017-0183

2017, Resilience in business and management research: a review of influential publications and a research agenda, International Journal of Management Reviews, 19, 4, 10.1111/ijmr.12076

2015, Managerial cognition and internationalization, Journal of International Business Studies, 46, 733, 10.1057/jibs.2015.9

2006, Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research, Management Science, 52, 1865, 10.1287/mnsc.1060.0597

1984, The new institutionalism: organizational factors in political life, The American Political Science Review, 78, 734

2017, An evidence-based review of HR Analytics, International Journal of Human Resource Management, 28, 3, 10.1080/09585192.2016.1244699

2011, Product safety and security in the global supply chain: Issues, challenges and research opportunities, Journal of Operations Management, 29, 707, 10.1016/j.jom.2011.06.007

2017, Making sense of big data – can it transform operations management?, International Journal of Operations & Production Management, 37, 37, 10.1108/IJOPM-02-2015-0084

1982, Adapting to environmental jolts, Administrative Science Quarterly, 27, 515, 10.2307/2392528

1977, Institutionalised organisations: formal structure as myth and ceremony, The American Journal of Sociology, 83, 340, 10.1086/226550

Miah, S.J., Vu, H.Q., Gammack, J. and McGrath, M. (2017), “A big data analytics method for tourist behaviour analysis”, Information and Management, Vol. 54 No. 6, pp. 771-785.

1992, A framework for integrated risk management in international business, Journal of International Business Studies, 23, 311, 10.1057/palgrave.jibs.8490270

2018, Big data and supply chain management: a review and bibliometric analysis, Annals of Operations Research, 270, 313, 10.1007/s10479-016-2236-y

2018, Comparing supply chain risks for multiple product categories with cognitive mapping and analytic hierarchy process, Technological Forecasting and Social Change, 131, 159

2012, Information technology and firm profitability: mechanisms and empirical evidence, MIS Quarterly, 36, 205, 10.2307/41410414

2016, Perspectives on past and future AIS research as the journal of information systems turns thirty, Journal of Information Systems, 30, 157, 10.2308/isys-51495

1991, Development of an instrument to measure the perceptions of adopting an information technology innovation, Information Systems Research, 2, 173

1990, Institutions, institutional change, and economic performance, Economic Perspective, 5, 97

1978, Psychometric Theory, 2nd ed.

2015, Explorative versus exploitative business model change: the cognitive antecedents of firm-level responses to disruptive innovation, Strategic Entrepreneurship Journal, 9, 58, 10.1002/sej.1192

2017, The role of big data in explaining disaster resilience in supply chains for sustainability, Journal of Cleaner Production, 142, 1108

2003, Common method biases in behavioral research: a critical review of the literature and recommended remedies, Journal of Applied Psychology, 88, 879, 10.1037/0021-9010.88.5.879

1991, The New Institutionalism in Organizational Analysis

2008, Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models, Behavior Research Methods, 40, 879, 10.3758/BRM.40.3.879

2013, Assessment of supply chain risk: scale development and validation, Benchmarking, 20, 79, 10.1108/14635771311299506

2018, Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects, International Journal of Production Research, 56, 5206, 10.1080/00207543.2018.1427900

2015, Learning from practice: how HR analytics avoids being a management fad, Organizational Dynamics, 44, 236, 10.1016/j.orgdyn.2015.05.008

2014, Should sustainability and resilience be combined or remain distinct pursuits?, Ecology and Society, 19, 37, 10.5751/ES-06390-190237

2017, The impact of risk management on the frequency of supply chain disruptions: a configurational approach, International Journal of Operations & Production Management, 37, 557, 10.1108/IJOPM-03-2016-0129

2019, A neural network approach for retailer risk assessment in the aftermarket industry, Benchmarking: An International Journal

2010, Tradeoffs in manufacturing? A meta-analysis and critique of the literature, Production and Operations Management, 19, 127, 10.1111/j.1937-5956.2009.01072.x

2018, Achieving supply chain excellence through supplier management, Benchmarking: An International Journal, 25, 4084, 10.1108/BIJ-02-2018-0042

2016, Integrated supply, production and distribution scheduling under disruption risks, Omega (United Kingdom), 62, 131

2017, A portfolio approach to supply chain disruption management, International Journal of Production Research, 55, 1970, 10.1080/00207543.2016.1249432

2018, Supply chain disruption propagation: a systemic risk and normal accident theory perspective, International Journal of Production Research, 56, 43, 10.1080/00207543.2017.1355123

1987, The adolescence of institutional theory, Administrative Science Quarterly, 32, 493, 10.2307/2392880

2008, Institutions and Organizations: Ideas and Interests, 3rd ed.

2013, How information systems help create OM capabilities: consequents and antecedents of operational absorptive capacity, Journal of Operations Management, 31, 409, 10.1016/j.jom.2013.07.013

2014, Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations, European Journal of Information Systems, 23, 433, 10.1057/ejis.2014.17

2016, Modeling information risk in supply chain using Bayesian networks, Journal of Enterprise Information Management, 29, 238, 10.1108/JEIM-03-2014-0031

2017, Market disruptions in supply chains: a review of operational models, International Transactions in Operational Research, 24, 697, 10.1111/itor.12333

2004, Information technology as a facilitator for enhancing dynamic capabilities through knowledge management, Information and Management, 41, 933, 10.1016/j.im.2003.06.004

2017, From local to global: developing a business model for Indian MNCs to achieve global competitive advantage, Journal of Asia-Pacific Business, 18, 192, 10.1080/10599231.2017.1346409

2019, Role of big data analytics in developing sustainable capabilities, Journal of Cleaner Production, 213, 1264

2007, Managing firm resources in dynamic environments to create value: looking inside the black box, Academy of Management Review, 32, 273, 10.5465/amr.2007.23466005

1994, Institutional pressures and isomorphic change: an empirical test, Organization Studies, 15, 803, 10.1177/017084069401500602

2015, Customer use of virtual channels in multichannel services: does type of activity matter?, Decision Sciences, 46, 1

1981, Threat rigidity effects in organizational behavior: a multilevel analysis, Administrative Science Quarterly, 26, 501, 10.2307/2392337

2004, Validation guidelines for IS positivist research, Communications of the Association for Information Systems, 13, 380

2015, Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph, International Journal of Production Economics, 165, 223

1997, Dynamic capabilities and strategic management, Strategic Management Journal, 18, 509, 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

1994, The dynamic capabilities of the firm, Industrial and Corporate Change, 3, 537

Thibodeau, I. and Naughton, N. (2018), “Ford halts F-150 production over parts shortage”, The Detroit News, available at: www.detroitnews.com/story/business/autos/ford/2018/05/09/ford-trucks-production-halted/34718903/ (accessed June 6, 2019).

2006, On the value of mitigation and contingency strategies for managing supply chain disruption risks, Management Science, 52, 639, 10.1287/mnsc.1060.0515

2018, Risks and performance in supply chain: the push effect, International Journal of Production Research, 56, 1369, 10.1080/00207543.2017.1363429

2016, Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities, International Journal of Production Economics, 171, 8

2017, Big data analytics and firm performance: effects of dynamic capabilities, Journal of Business Research, 70, 356

2015, How information technology influences environmental performance: empirical evidence from China, International Journal of Information Management, 35, 160, 10.1016/j.ijinfomgt.2014.11.005

2018, Big data analytics: understanding its capabilities and potential benefits for healthcare organizations, Technological Forecasting and Social Change, 126, 3

2009, Why all the changes?, International Journal of Physical Distribution & Logistics Management, 39, 595, 10.1108/09600030910996279

2014, Can e-business adoption be influenced by knowledge management? An empirical analysis of Malaysian SMEs, Journal of Knowledge Management, 18, 121, 10.1108/JKM-08-2013-0323

2009, An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management, International Journal of Production Economics, 120, 252, 10.1016/j.ijpe.2008.07.023

2002, Deliberate learning and the evolution of dynamic capabilities, Organization Science, 13, 339, 10.1287/orsc.13.3.339.2780

2016, Identifying and managing supply quality risk, International Journal of Logistics Management, 27, 908, 10.1108/IJLM-02-2015-0043