“The Early Bird Catches the Worm - First Mover Advantage through IoT Adoption for Indian Public Sector Retail Oil Outlets”

References

• Abrahamson, E., & Rosenkopf, L. (1993). Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. The Academy of Management Review, 18(3), 487–517. doi:10.5465/amr.1993.9309035148
   Web of Science ®Google Scholar
• Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247. doi:10.2307/249577
   Web of Science ®Google Scholar
• Adamson, I., & Shine, J. (2003). Extending the new technology acceptance model to measure the end user information systems satisfaction in a mandatory environment: A bank’s treasury. Technology Analysis and Strategic Management, 15(4), 441–455. doi:10.1080/095373203000136033
   Web of Science ®Google Scholar
• Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. doi:10.2307/3250951
   Web of Science ®Google Scholar
• Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582. doi:10.1111/deci.1997.28.issue-3
   Web of Science ®Google Scholar
• Agarwal, R., Tanniru, M., & Wilemon, D. (1997). Assimilating information technology innovations: Strategies and moderating influences. IEEE Transactions on Engineering Management, 44(4), 347–358. doi:10.1109/17.649864
   Web of Science ®Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Akca, Y., & Ozer, G. (2014). Diffusion of innovation theory and an implementation on enterprise resource planning. International Journal of Business and Management, 9(4), 92–113. doi:10.5539/ijbm.v9n4p92
   Google Scholar
• Albarracin, D., Johnson, B., Fishbein, M., & Muellerleile, P. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127, 142–161. doi:10.1037/0033-2909.127.1.142
   PubMed Web of Science ®Google Scholar
• Applin, S. A., & Fischer, M. D. (2013). Thing theory: Connecting humans to location-aware smart environments (pp. 1). United Kingdom: University of Kent Canterbury.
   Google Scholar
• Applin, S. A., & Fischer, M. D. (2017). Thing theory: Connecting humans to smart healthcare (pp. 249–262). IGI Global.
   Google Scholar
• Armitage, C., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40, 471–499. doi:10.1348/014466601164939
   PubMed Web of Science ®Google Scholar
• Arslan, F., Bagchi, K. K., & Kris, P. (2019). Factors implicated with firm-level ICT use in developing economies. Journal of Global Information Technology Management, 22(3), 179–207. doi:10.1080/1097198X.2019.1642022
   Web of Science ®Google Scholar
• Babar, S., Mahalle, P., Stango, A., Prasad, N., & Prasad, R. (2010). Proposed security model and threat taxonomy for the Internet of Things (IoT). Recent Trends in Network Security and Applications, 420–429. Berlin: Springer.
   Google Scholar
• Balaji, M. S., & Roy, S. K. (2016). Value co-creation with Internet of things technology in the retail industry. Journal of Marketing Management, 1, 4.
   Google Scholar
• Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. doi:10.1177/014920639101700108
   Web of Science ®Google Scholar
• Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882–893. doi:10.1080/10447318.2017.1403220
   Web of Science ®Google Scholar
• Benbasat, I., & Dexter, A. S. (1986). An Investigation of the effectiveness of color and graphical information presentation under varying time constraints. MIS Quarterly, 10(1), 59–83. doi:10.2307/248881
   Web of Science ®Google Scholar
• Benjamin, R. I., Rockart, J. F., & Scott-Morton, M. C. (1984). Information technology: A strategic opportunity. Sloan Management Review, 25(3), 3–10.
   Google Scholar
• Bennett, P., & Bozionelos, G. (2000). The theory of planned behaviour as predictor of condom use: A narrative review. Psychology Health and Medicine, 5, 307–326. doi:10.1080/713690195
   Google Scholar
• Blue, C. (1995). The predictive capacity of the theory of reasoned action and the theory of planned behavior in exercise research: An integrated literature review. Research in Nursing and Health, 18, 105–121. doi:10.1002/(ISSN)1098-240X
   PubMed Web of Science ®Google Scholar
• BP. (2016). BP statistical review of world energy June 2016 (pp. 2). London, UK: Author.
   Google Scholar
• Brancheau, J. C., & Wetherbe, J. C. (1990). The adoption of spreadsheet software: Testing innovation diffusion theory in the context of end-user computing. Information Systems Research, 1(2), 115–143. doi:10.1287/isre.1.2.115
   Web of Science ®Google Scholar
• Brown, B. (2001). Thing theory. Critical Inquiry, 28(1), 1–22. doi:10.1086/449030
   Web of Science ®Google Scholar
• Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. In MIT sloan management review (pp. 23–29). Cambridge, Massachusetts.
   Google Scholar
• Buck, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces and organizational climate. MIS Quarterly, 29(1), 87–111. doi:10.2307/25148669
   Web of Science ®Google Scholar
• Carillo, K. D. (2010). Social cognitive theory in is research – Literature review, criticism, and research agenda. International Conference on Information Systems, Technology and Management (pp. 20–31). Berlin: Springer. doi: 10.1016/j.jphotobiol.2009.10.003
   Google Scholar
• Casimir, G., Keith Ng, Y. N., & Paul Cheng, C. L. (2012). Using IT to share knowledge and the TRA. Journal of Knowledge Management, 16(3), 461–479. doi:10.1108/13673271211238779
   Web of Science ®Google Scholar
• Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems, 13(2), 185–204. doi:10.1080/07421222.1996.11518128
   Google Scholar
• Chau, P. Y., & Hu, P. J.-H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699–719. doi:10.1111/j.1540-5915.2001.tb00978.x
   Web of Science ®Google Scholar
• Checkland, P. (1999). Systems thinking, systems practice. Chichester, UK: John Wiley & Sons.
   Google Scholar
• Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information and Management, 39(8), 705–719. doi:10.1016/S0378-7206(01)00127-6
   Web of Science ®Google Scholar
• Choong, K. K. (2014). Has this large number of performance measurement publications contributed to its better understanding? A systematic review for research and applications. International Journal of Production Research, 52(14), 4174–4197. doi:10.1080/00207543.2013.866285
   Web of Science ®Google Scholar
• Churchman, C. W. (1971). The design of inquiring systems: Basic concepts of systems and organizations. New York, USA: Basic Books Inc.
   Google Scholar
• Clemons, E. K., & Row, M. C. (1991). Sustaining IT advantage: The role of structural differences. MIS Quarterly, 15(3), 275–292. doi:10.2307/249639
   Web of Science ®Google Scholar
• Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computer technology: A longitudinal study. MIS Quarterly, 23(2), 145–158. doi:10.2307/249749
   Web of Science ®Google Scholar
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. doi:10.2307/249688
   Web of Science ®Google Scholar
• Conner, M., & Armitage, C. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1429–1464. doi:10.1111/jasp.1998.28.issue-15
   Web of Science ®Google Scholar
• Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123–139. doi:10.1287/mnsc.36.2.123
   Web of Science ®Google Scholar
• Cragg, P. B., & King, M. (1993). Small-firm computing: Motivators and inhibitors. MIS Quarterly, 17(1), 47–60. doi:10.2307/249509
   Web of Science ®Google Scholar
• Cristea, M., Paran, F., & Delhomme, P. (2013). Extending the theory of planned behavior: The role of behavioral options and additional factors in predicting speed behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 122–132. doi:10.1016/j.trf.2013.09.009
   Web of Science ®Google Scholar
• D’Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of E-books by academics. Journal of the American Society for Information Science and Technology, 64(1), 48–64. doi:10.1002/asi.22757
   Web of Science ®Google Scholar
• Daniel, E., Wilson, H., & Myers, A. (2002). Adoption of e-commerce by SMEs in the UK. International Small Business Journal, 20(3), 253–270. doi:10.1177/0266242602203002
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two models. Management Science, 35(8), 982–1001. doi:10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. doi:10.1111/jasp.1992.22.issue-14
   Web of Science ®Google Scholar
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. doi:10.1287/isre.3.1.60
   Web of Science ®Google Scholar
• Dholakia, R. R., & Kshetri, N. (2004). Factors impacting the adoption of the internet among SMEs. Small Business Economics, 23(4), 311–322. doi:10.1023/B:SBEJ.0000032036.90353.1f
   Web of Science ®Google Scholar
• Dibra, M. (2015). Rogers theory on diffusion of innovation - The most appropriate theoretical model in the study of factors influencing the integration of sustainability in tourism business. Procedia - Social and Behaviroal Sciences, 195(3), 1453–1462. doi:10.1016/j.sbspro.2015.06.443
   Google Scholar
• Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–Technology fit constructs. Information and Management, 36(1), 9–21. doi:10.1016/S0378-7206(98)00101-3
   Web of Science ®Google Scholar
• Earl, M. (1989). Implementation: Management strategies for information technology. New York, USA: Prentice-Hall.
   Google Scholar
• Economic Times. (2016, October 20). Rosneft, BP entry may fuel retail competition in India. Retrieved from Economic Times: http://economictimes.indiatimes.com/industry/energy/oil-gas/rosneft-bp-entry-may-fuel-retail-competition-in-india/articleshow/54945878.cms
   Google Scholar
• Edelman, F. (1981). Managers, computer systems and productivity. MIS Quarterly, 5(3), 1–19. doi:10.2307/249287
   Web of Science ®Google Scholar
• Edmondson, A., Bohmer, R., & Pisano, G. (2001). Disrupted routines: Team learning and new technology implementation in hospitals. Administrative Science Quarterly, 46, 685–716. doi:10.2307/3094828
   Web of Science ®Google Scholar
• Eisenhardt, K., & Martin, J. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21, 1105–1121. doi:10.1002/(ISSN)1097-0266
   Web of Science ®Google Scholar
• Eslami, A. A., Norozi, E., Hajihosseini, M., Ramazani, A. A., & Miri, M. R. (2018). Social cognitive theory as a theoretical framework to predict sustained abstinence 6 months after substance use treatment. Journal of Substance Use, 23(3), 300–306. doi:10.1080/14659891.2017.1394382
   Web of Science ®Google Scholar
• Feeny, D. F., & Ives, B. (1990). In search of sustainability: Reaping long-term advantage from investments in information technology. Journal of Management Information Systems, 7(1), 27–46. doi:10.1080/07421222.1990.11517879
   Google Scholar
• Fichman, R. G. (1992). Information technology diffusion: A review of empirical research. Proceedings of the Thirteenth international conference on information systems (pp. 195–206). Dallax, TX.
   Google Scholar
• Fishbein, M. A., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley.
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. doi:10.1177/002224378101800104
   Web of Science ®Google Scholar
• Franz, C. R., & Robey, D. (1986). Organizational context, user involvement, and the usefulness of information systems. Decision Sciences, 17(3), 329–356. doi:10.1111/deci.1986.17.issue-3
   Web of Science ®Google Scholar
• Gallivan, M. J., Spitler, V. K., & Koufaris, M. (2005). Does information technology training really matter? A social information processing analysis of coworkers’ influence on IT usage in the workplace. Journal of Management Information Systems, 22(1), 153–192. doi:10.1080/07421222.2003.11045830
   Web of Science ®Google Scholar
• Goodhue, D. L. (1998). Development and measurement validity of a task-technology fit instrument for user evaluations of information systems. Decision Sciences, 29(1), 105–138. doi:10.1111/deci.1998.29.issue-1
   Web of Science ®Google Scholar
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. doi:10.2307/249689
   Web of Science ®Google Scholar
• Grover, V. (1993). An empirically derived model for the adoption of customer-based interorganizational systems. Decision Sciences, 24(3), 603–640. doi:10.1111/deci.1993.24.issue-3
   Web of Science ®Google Scholar
• Grover, V., Fiedler, K., & Teng, J. (1997). Empirical evidence on Swanson’s tri-core model of information systems innovation. Information Systems Research, 8(3), 273–287. doi:10.1287/isre.8.3.273
   Web of Science ®Google Scholar
• Grover, V., & Goslar, M. D. (1993). The initiation, adoption and implementation of telecommunications technologies in U.S. organizations. Journal of Management Information Systems, 10(1), 141–163. doi:10.1080/07421222.1993.11517994
   Google Scholar
• Grover, V., & Teng, J. T. (1992). An examination of DBMS adoption and success in American Organizations. Information and Management, 23(5), 239–248. doi:10.1016/0378-7206(92)90055-K
   Web of Science ®Google Scholar
• Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability (pp. 59–82). Field Methods.
   Google Scholar
• Guo, B., Zhang, D., Wang, Z., Zhiwen, Y., & Xingshe, Z. (2013). Opportunistic IoT: Exploring the harmonious interaction between human and the Internet of things. Journal of Network and Computer Applications, 36, 1531. doi:10.1016/j.jnca.2012.12.028
   Web of Science ®Google Scholar
• Hage, J., & Aiken, M. (1967). Program change and organizational properties a comparative analysis. American Journal of Sociology, 72(5), 503–519. doi:10.1086/224380
   PubMed Web of Science ®Google Scholar
• Hair, J., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (Seventh ed., pp. 115, 117). Essex, England: Pearson Education Limited.
   Google Scholar
• Halal, W. E. (2013). Forecasting the technology revolution: Results and learnings from the techcast project. In Technological forecasting & social change (pp. 1636). Elsevier.
   Google Scholar
• Hamel, G. (1998). The challenge today: Changing the rules of the game. Business Strategy Review, 9(2), 19–26. doi:10.1111/busr.1998.9.issue-2
   Google Scholar
• Hamilton, S., & Chervany, N. L. (1981). Evaluating information system effectiveness. Part 1. Comparing evaluation approaches. MIS Quarterly, 5(3), 55–69. doi:10.2307/249291
   Web of Science ®Google Scholar
• Hendrickson, A., Massey, P., & Cronan, T. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17(2), 227–230. doi:10.2307/249803
   Web of Science ®Google Scholar
• Hsu, C.-L., & Lin, J.-C.-C. (2016). Exploring factors affecting the adoption of Internet of Things services. Journal of Computer Information Systems, 58(1), 49–57. doi:10.1080/08874417.2016.1186524
   Web of Science ®Google Scholar
• Huber, G. P. (1984). The nature and design of post-industrial organizations. Management Science, 30(8), 928–951. doi:10.1287/mnsc.30.8.928
   Web of Science ®Google Scholar
• Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465–485. doi:10.2307/249629
   Web of Science ®Google Scholar
• Igbaria, M. (1993). User acceptance of microcomputer technology: An empirical test. Omega, 21(1), 73–90. doi:10.1016/0305-0483(93)90040-R
   Web of Science ®Google Scholar
• Igbaria, M., & Livari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605. doi:10.1016/0305-0483(95)00035-6
   Web of Science ®Google Scholar
• Igbaria, M., Parasuraman, S., & Baroudi, J. (1996). A motivational model of computer usage. Journal of Management Information Systems, 13(1), 127–143. doi:10.1080/07421222.1996.11518115
   Google Scholar
• Igbaria, M., & Tan, M. (1997). The consequences of information technology acceptance on subsequent individual performance. Information and Management, 32(3), 113–121. doi:10.1016/S0378-7206(97)00006-2
   Web of Science ®Google Scholar
• Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21, 1–23. doi:10.1057/palgrave.jit.2000056
   Web of Science ®Google Scholar
• Jeyaraj, A., & Sabherwal, R. (2008). Adoption of information systems innovations by individuals: A study of processes involving contextual, adopter, and influencer actions. Information and Organization, 18(3), 205–234. doi:10.1016/j.infoandorg.2008.04.001
   Web of Science ®Google Scholar
• Jungwoo, L. (2004). Discriminant analysis of technology adoption: A case of internet technologies in small businesses. Journal of Computer Information Systems, 44(4), 57–66.
   Web of Science ®Google Scholar
• Kaiser, H., & Rice, J. (1974). Little Jiffy Mark IV. Journal of Educational and Psychological Measurement, 34, 111–117. doi:10.1177/001316447403400115
   Web of Science ®Google Scholar
• Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213. doi:10.2307/249751
   Web of Science ®Google Scholar
• Kast, F. E., & Rosenzweig, J. E. (1972). General systems theory: Applications for organization and management. Academy Management Journal, 15(4), 447–465.
   Web of Science ®Google Scholar
• Katz, D., & Kahn, R. L. (1978). The social psychology of organizations. New York, USA: John Wiley.
   Google Scholar
• Kim, T. (2015). Diffusion of changes in organizations. Journal of Organizational Change Management, 28(1), 134–152. doi:10.1108/JOCM-04-2014-0081
   Web of Science ®Google Scholar
• Kimberly, J. R., & Evanisko, M. J. (1981). Organizational innovation: The influence of individual, organizational, and contextual factors on hospital adoption of technological and administrative innovations. The Academy of Management Journal, 24(4), 689–713.
   PubMed Web of Science ®Google Scholar
• Koul, S., & Eydgahi, A. (2018). Utilizing Technology Acceptance Model (TAM) for driverless car technology. Journal of Technology Management and Innovation, 13(4), 37–46. doi:10.4067/S0718-27242018000400037
   Google Scholar
• Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. Pp. 227–251, In R. J. Boland & R. A. Hirschheim (Eds.), Critical issues in information systems research. New York, USA: John Wiley & Sons.
   Google Scholar
• Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, 14(1), 21–38. doi:10.4301/S1807-17752017000100002
   Google Scholar
• Lai, V. S., & Guynes, J. L. (1994). A model of ISDN (Integrated Services Digital Network) adoption in U.S. Corporations. Information and Management, 26(1), 75–84. doi:10.1016/0378-7206(94)90055-8
   Web of Science ®Google Scholar
• Lansiti, M., & Lakhani, K. R. (2014). Digital ubiquity: How connections, sensors, and data are revolutionizing business. Harvard Business Review, 92(11), 91–99.
   Google Scholar
• Lawrence, P. R., & Lorsch, J. W. (1967). Organization and environment. Boston, Massachusetts: Harvard Business School Press.
   Google Scholar
• Lee, K., & Joshi, K. (2018). New information technologies and organizational adoption: A regulatory focus theory perspective for cross-cultural comparisons. Journal of Global Information Technology Management, 21, 1–4. doi:10.1080/1097198X.2018.1435005
   Web of Science ®Google Scholar
• Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27(4), 657–678. doi:10.2307/30036552
   Web of Science ®Google Scholar
• Li, X., Troutt, M. D., Brandyberry, A., & Wang, T. (2011). Decision factors for the adoption and continued use of online direct sales channels among SMEs. Journal of the Association for Information Systems, 12(1), 1–31. doi:10.17705/1jais
   Web of Science ®Google Scholar
• Lo, F.-Y., & Campos, N. (2018). Blending Internet-of-Things (IoT) solutions into relationship marketing strategies. Technological Forecasting & Social Change, 10(11), 12.
   Google Scholar
• Loh, L., & Venkatraman, N. (1992). Diffusion of information technology outsourcing: Influence sources and the Kodiak effect. Information Systems Research, 4, 334–358. doi:10.1287/isre.3.4.334
   Web of Science ®Google Scholar
• Lu, Y., Papagiannidis, S., & Alamanos, E. (2018). Internet of Things: A systematic review of the business literature from the user and organisational perspectives. Technological Forecasting & Social Change, 1(2), 5, 10.
   Google Scholar
• Luthra, S., Garg, D., Mangla, S. K., & Berwal, Y. P. (2018). Analyzing challenges to Internet of Things (IoT) adoption and diffusion: An Indian context. 6th International Conference on Smart Computing and Communications (pp. 733–739). Kurukshetra: Procedia Computer Science.
   Google Scholar
• Malhotra, N. K., & Dash, S. (2010). Marketing research: An applied orientation (6th ed.). New Delhi, India: Pearson Education.
   Google Scholar
• Mani, S., & Dhingra, T. (2013). Offshore wind energy policy for India - Key factors to be considered. Energy Policy, 56, 677.
   Web of Science ®Google Scholar
• Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995). Information technology and sustained competitive advantage: A resource-based analysis. MIS Quarterly, 19(4), 487–505. doi:10.2307/249630
   Web of Science ®Google Scholar
• Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. doi:10.1287/isre.2.3.173
   Web of Science ®Google Scholar
• Mathieson, K., & Keil, M. (1998). Beyond the interface: Ease of use and task/technology fit. Information and Management, 34(4), 221–230. doi:10.1016/S0378-7206(98)00058-5
   Web of Science ®Google Scholar
• Mendhurwar, S., & Mishra, R. (2018). Emerging synergies between Internet of Things and social technologies. Journal of Global Information Technology Management, 21(2), 1–6.
   Web of Science ®Google Scholar
• Ministry of Petroleum and Natural Gas. (2016). Indian Petroleum and Natural Gas Statistics 2015–16. New Delhi, India: Author, pp. 54.
   Google Scholar
• Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. doi:10.1287/isre.2.3.192
   Web of Science ®Google Scholar
• Moriarty, R., O’Connell, K., Smit, N., Noronha, A., & Barbier, J. (2015). A New Reality for Oil and Gas. Cisco, pp. 19, 3.
   Google Scholar
• Nanterme, P., & Daugherty, P. (2016). Accenture Technology Vision 2016. Accenture, pp. 6.
   Google Scholar
• Nevo, S., & Wade, M. R. (2010). The formation and value of IT-enabled resources: Antecedents and consequences of synergistic relationships. MIS Quarterly, 34(1), 163–183. doi:10.2307/20721419
   Web of Science ®Google Scholar
• Ng, C. K., Wu, C. H., Yung, K. L., Ip, W. H., & Cheung, T. (2018). A semantic similarity analysis of Internet of Things. Enterprise Information Systems, 12(7), 1–36.
   Web of Science ®Google Scholar
• Nguyen, T.-M., Nham, P. T., & Hoang, V.-N. (2019). The theory of planned behavior and knowledge sharing: A systematic review and meta-analytic structural equation modelling. VINE Journal of Information and Knowledge Management Systems, 49(1), 76–94. doi:10.1108/VJIKMS-10-2018-0086
   Web of Science ®Google Scholar
• Nilakanta, S., & Scamell, R. W. (1990). The effect of information sources and communication channels on the diffusion of innovation in a data base development environment. Management Science, 36(1), 24–40. doi:10.1287/mnsc.36.1.24
   Web of Science ®Google Scholar
• Nunnally, J., & Bernstein, L. (1994). Psychometric theory (3rd ed.). New York, USA: McGrawHill.
   Google Scholar
• Otieno, O. C., Liyala, S., Odongo, B. C., & Abeka, S. (2016). Theory of reasoned action as an underpinning to technological innovation adoption studies. World Journal of Computer Application and Technology, 4(1), 1–7.
   Google Scholar
• Petter, S., DeLone, W., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of Management Information Systems, 29(4), 7–62. doi:10.2753/MIS0742-1222290401
   Web of Science ®Google Scholar
• Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. London, UK: Blackwell Publishing.
   Google Scholar
• Plouffe, C. R., Hulland, J. H., & Vandenbosch, M. (2001). Research Report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208–222. doi:10.1287/isre.12.2.208.9697
   Web of Science ®Google Scholar
• Porter, M., & Millar, V. E. (1985). How information gives you competitive advantage. Harvard Business Review, 63(4), 149–160.
   Web of Science ®Google Scholar
• Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994). Implementation of electronic data interchange: An innovation diffusion perspective. Journal of Management Information Systems, 11(2), 157–186. doi:10.1080/07421222.1994.11518044
   Google Scholar
• PwC. (2015). Let’s energise - Meeting India’s growing fuel demand (pp. 6). Author, India.
   Google Scholar
• Quaddus, M., & Hofmeyer, G. (2007). An investigation into the factors influencing the adoption of B2B trading exchanges in small businesses. European Journal of Information Systems, 16(3), 202–215. doi:10.1057/palgrave.ejis.3000671
   Web of Science ®Google Scholar
• Rai, A., & Howard, G. S. (1993). An organizational context for CASE innovation. Information Resources Management Journal, 6(3), 21–34. doi:10.4018/IRMJ
   Google Scholar
• Rai, A., & Howard, G. S. (1994). Propagating CASE usage for software development: An empirical investigation of key organizational correlates. OMEGA, 22(2), 133–147. doi:10.1016/0305-0483(94)90074-4
   Web of Science ®Google Scholar
• Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50–69. doi:10.1287/isre.13.1.50.96
   Web of Science ®Google Scholar
• Ramamurthy, K., & Premkumar, G. (1995). Determinants and outcomes of electronic data interchange diffusion. IEEE Transactions on Engineering Management, 42(4), 332–351. doi:10.1109/17.482083
   Web of Science ®Google Scholar
• Riemenschneider, C. K., Harrison, D. A., & Mykytyn, P. P. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information and Management, 40(4), 269–287. doi:10.1016/S0378-7206(02)00010-1
   Web of Science ®Google Scholar
• Rizzoni, A. (1991). Technological innovation and small firms: A taxonomy. International Small Business Journal: Researching Entrepreneurship, 9(3), 31–42. doi:10.1177/026624269100900302
   Google Scholar
• Robey, D., & Farrow, D. (1982). User involvement in information system development: A conflict model and empirical test. Management Science, 28(1), 73–85. doi:10.1287/mnsc.28.1.73
   Web of Science ®Google Scholar
• Rogers, E. M. (1995). Diffusion of innovations. New York, USA: The Free Press.
   Google Scholar
• Romano, N. C., Jr, Pick, J. B., & Roztocki, N. (2010). A motivational model for technology-supported cross-organizational and cross-border collaboration. European Journal of Information Systems, 19(2), 117–133. doi:10.1057/ejis.2010.17
   Web of Science ®Google Scholar
• Rose, G., & Lyytinen, K. (2001). The quad-core model of information systems innovation: Identifying and confirming the role of novel technological frames as a supra-innovation core - The case of internet induced IT innovation. International Conference on Information Systems (ICIS) (pp. 419–424). New Orleans: AIS Electronic Library (AISeL).
   Google Scholar
• Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: Individual and organizational determinants. Management Science, 52(12), 1849–1864. doi:10.1287/mnsc.1060.0583
   Web of Science ®Google Scholar
• Sandy, C., & Graham, P. (2007). Factors influencing the extent of deployment of electronic commerce for small and medium sized enterprises. Journal of Electronic Commerce in Organizations, 5(1), 1–29. doi:10.4018/jeco.2007010101
   Google Scholar
• Savitskie, K., Royne, M. B., Persinger, E. S., Grunhagen, M., & Witte, C. L. (2014). Norwegian internet shopping sites: An application & extension of the technology acceptance model. Journal of Global Information Technology Management, 10(4), 54–73.
   Google Scholar
• Scheepers, H., & Scheepers, R. (2004). The implementation of mobile technology in organizations: Expanding individual use contexts. International Conference on Information Systems (ICIS) (pp. 171–182). Washington DC: Association for Information Systems Electronic Library (AISeL).
   Google Scholar
• Seddon, P. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–254. doi:10.1287/isre.8.3.240
   Web of Science ®Google Scholar
• Sharda, R., Barr, S. H., & McDonnel, J. C. (n.d.). Decision support system effectiveness: A review and empirical test. Management Science, 34(2), 139–159. doi:10.1287/mnsc.34.2.139
   Web of Science ®Google Scholar
• Shukla, S., & Sornalakshmi, K. (2013). Internet of Things: Rule based event management. International Journal of Science and Research (IJSR), 4(4), 1214.
   Google Scholar
• Straub, D. W., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information and Management, 33(1), 1–11. doi:10.1016/S0378-7206(97)00026-8
   Web of Science ®Google Scholar
• Swanson, E. B. (1994). Information systems innovation among organizations. Management Science, 9, 1069–1092. doi:10.1287/mnsc.40.9.1069
   Web of Science ®Google Scholar
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. doi:10.1287/isre.6.2.144
   Web of Science ®Google Scholar
• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. doi:10.1002/(ISSN)1097-0266
   Web of Science ®Google Scholar
• Teng, J. T., Grover, V., & Guttler, W. (2002). Information technology innovations: General diffusion patterns and its relationships to innovation characteristics. IEEE Transactions on Engineering Management, 49(1), 13–27. doi:10.1109/17.985744
   Web of Science ®Google Scholar
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. doi:10.2307/249443
   Web of Science ®Google Scholar
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11(1), 167–187. doi:10.1080/07421222.1994.11518035
   Google Scholar
• Thong, J., & Yap, C. (1995). CEO characteristics, organizational characteristics and information technology adoption in small businesses. Omega, 23(4), 429–442. doi:10.1016/0305-0483(95)00017-I
   Web of Science ®Google Scholar
• Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29, 28–45. doi:10.1109/TEM.1982.6447463
   Web of Science ®Google Scholar
• Tu, M. (2018). An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management. International Journal of Logistics Management, 29(1), 131–151. doi:10.1108/IJLM-11-2016-0274
   Web of Science ®Google Scholar
• Vagias, & Wade, M. (2006). Likert-type scale response anchors, pp.1–2. South Carolina, USA: Clemson University.
   Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. doi:10.2307/30036540
   Web of Science ®Google Scholar
• Volberda, H. W. (1996). Toward the flexible firm: How to remain vital in hypercompetitive environments. Organization Science, 7(4), 359–374. doi:10.1287/orsc.7.4.359
   Web of Science ®Google Scholar
• Wade, M., & Hulland, J. (2004). Review: The resource-based view and information systems research: Review, extension, and suggestions for future research. MIS Quarterly, 28(1), 107–142. doi:10.2307/25148626
   Web of Science ®Google Scholar
• Wang, Y.-H., & Hsieh, -C.-C.  (2018). Explore technology innovation and intelligence for IOT (Internet of Things) based eyewear technology. Technological Forecasting & Social Change, 127, 281–290 .
   Google Scholar
• Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly, 16(2), 201–226. doi:10.2307/249576
   Web of Science ®Google Scholar
• Weichhart, G. (2014). Learning for sustainable organisational interoperability. Proceedings of the 19th World Congress (pp. 4280–4285). Cape Town, South Africa: The International Federation of Automatic Control.
   Google Scholar
• Williams, M. D., Rana, N., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. doi:10.1108/JEIM-09-2014-0088
   Web of Science ®Google Scholar
• Willmott, P. (2013). The digital enterprise. Mckinsey (pp. 2).
   Google Scholar
• Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. doi:10.1287/isre.1050.0042
   Web of Science ®Google Scholar
• Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729. doi:10.1016/j.im.2004.07.001
   Web of Science ®Google Scholar
• Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). New York, USA: Harper and Row.
   Google Scholar
• Yang, L., Yang, S. H., & Plotnick, L. (2012). How the internet of things technology enhances emergency response operations. Technological Forecasting & Social Change, 2, 5.
   Google Scholar
• Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information and Management, 43(3), 350–363. doi:10.1016/j.im.2005.08.006
   Web of Science ®Google Scholar
• Zhu, K., Kraemer, K., Xu, S., & Dedrick, J. (2004). Information technology payoff in e-Business environments: An international perspective on value creation of e-Business in financial services industry. Journal of Management Information Systems, 21(1), 17–54. doi:10.1080/07421222.2004.11045797
   Web of Science ®Google Scholar