Abstract
Big data means a large volume of data used and stored by different firms in their day-to-day operations. It is a field that extracts and analyzes a complex, large volume of data. This research is conducted to study the adoption of big data in Indian SMEs using the TOE framework. This research created awareness for the adoption of big data software in Indian SMEs. For this, a structured literature review was conducted. Three independent variables, technological, organizational, and environmental perspectives, are identified. Survey is carried out in the SMEs with the help of questionnaires. The target population is IT managers, plant managers, owners, and directors. For data analysis, exploratory factor analysis using SPSS 20.0 software and structural equation modeling using AMOS 20.0 software is used. The developed model using three independent variables and one dependent variable showed a good fit.
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References
Addo-Tenkorang, R., Helo, P.T.: Big data applications in operations/supply-chain management: a literature review. Comput. Ind. Eng. 101, 528–543 (2016)
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J.: How to improve firm performance using big data analytics capability and business strategy alignment? Int. J. Prod. Econ. 182, 113–131 (2016)
Choi, T.M., Wallace, S.W., Wang, Y.: Big data analytics in operations management. Prod. Oper. Manag. 27(10), 1868–1883 (2018)
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308–317 (2017)
Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., Childe, S.J.: Modelling quality dynamics, business value and firm performance in a big data analytics environment. Int. J. Prod. Res. 55(17), 5011–5026 (2017)
Lugmayr, A., Stockleben, B., Scheib, C., Mailaparampil, M.A.: Cognitive big data: survey and review on big data research and its implications. What is really “new” in big data? J. Knowl. Manage. (2017)
Prescott, M.E.: Big data and competitive advantage at Nielsen. Manage. Decis. (2014)
Wang, G., Gunasekaran, A., Ngai, E.W., Papadopoulos, T.: Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int. J. Prod. Econ. 176, 98–110 (2016)
Zhang, Y., Ren, S., Liu, Y., Si, S.: A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J. Clean. Prod. 142, 626–641 (2017)
Chen, P.T., Lin, C.L., Wu, W.N.: Big data management in healthcare: Adoption challenges and implications. Int. J. Inf. Manage. 53, 102078 (2020)
Rajabion, L.: Application and adoption of big data technologies in SMEs. In: 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1133–1135. IEEE (2018)
Pereira, J.P., Ostritsova, V.: ICT and big data adoption in SMEs from rural areas: comparison between Portugal, Spain and Russia. In: World Conference on Information Systems and Technologies, pp. 291–301. Springer, Cham (2020)
Azevedo, F., Reis, J.L.: Big data analysis in supply chain management in Portuguese SMEs “leader excellence”. J. Inf. Syst. Eng. Manage. 4(3), em0096 (2019)
Karim, S., Al-Tawara, A., Gide, E., Sandu, R.: Is big data too big for SMEs in Jordan? In: 2017 8th International Conference on Information Technology (ICIT), pp. 914–922. IEEE (2017)
Tien, E.L., Ali, N.M., Miskon, S., Ahmad, N., Abdullah, N.S.: Big data analytics adoption model for Malaysian SMEs. In: International Conference of Reliable Information and Communication Technology, pp. 45–53. Springer, Cham (2019)
Iqbal, M., Kazmi, S.H.A., Manzoor, A., Soomrani, A.R., Butt, S.H., Shaikh, K.A.: A study of big data for business growth in SMEs: opportunities & challenges. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1–7. IEEE (2018)
Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort-Martorell, X., Reis, M.S.: How can SMEs benefit from big data? Challenges and a path forward. Qual. Reliab. Eng. Int. 32(6), 2151–2164 (2016)
Shah, S., Soriano, C.B., Coutroubis, A.D.: Is big data for everyone? The challenges of big data adoption in SMEs. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 803–807. IEEE (2017)
Saleem, H., Li, Y., Ali, Z., Mehreen, A., Mansoor, M.S.: An empirical investigation on how big data analytics influence China SMEs performance: do product and process innovation matter? Asia Pac. Bus. Rev. 26(5), 537–562 (2020)
Sen, D., Ozturk, M., Vayvay, O.: An overview of big data for growth in SMEs. Procedia Soc. Behav. Sci. 235, 159–167 (2016)
Wang, S., Wang, H.: Big data for small and medium-sized enterprises (SME): a knowledge management model. J. Knowl. Manage. (2020)
Ifinedo, P.: An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. Int. J. Inf. Technol. Decis. Mak. 10(04), 731–766 (2011)
Silva, J., Hernández-Fernández, L., Cuadrado, E.T., Mercado-Caruso, N., Espinosa, C.R., Ortega, F.A., Hugo Hernández, P., Delgado, G.J.: Factors affecting the big data adoption as a marketing tool in SMEs. In: International Conference on Data Mining and Big Data, pp. 34–43. Springer, Singapore (2019)
Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M.H.N.B.M., Asadi, S., Samad, S., Awang, N.F.: The impact of big data on firm performance in hotel industry. Electron. Commer. Res. Appl. 40, 100921 (2020)
O’Connor, C., Kelly, S.: Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector. J. Knowl. Manage. (2017)
Vajjhala, N.R., Ramollari, E.: Big data using cloud computing-opportunities for small and medium-sized enterprises. Eur. J. Econ. Bus. Stud. 2(1), 129–137 (2016)
Tornatzky, L.G., Fleischer, M., Chakrabarti, A.K.: Processes of Technological Innovation. Lexington Books (1990)
Alharbi, F., Atkins, A., Stanier, C.: Understanding the determinants of cloud computing adoption in Saudi healthcare organisations. Complex Intell. Syst. 2(3), 155–171 (2016)
Ahmadi, H., Nilashi, M., Shahmoradi, L., Ibrahim, O.: Hospital information system adoption: expert perspectives on an adoption framework for Malaysian public hospitals. Comput. Hum. Behav. 67, 161–189 (2017)
Mukherjee, S., Chittipaka, V.: Analysing the adoption of intelligent agent technology in food supply chain management: an empirical evidence. FIIB Bus. Rev. (2021)
Gupta, P., Seetharaman, A., Raj, J.R.: The usage and adoption of cloud computing by small and medium businesses. Int. J. Inf. Manage. 33(5), 861–874 (2013)
Chang, I.C., Hwang, H.G., Hung, M.C., Lin, M.H., Yen, D.C.: Factors affecting the adoption of electronic signature: executives’ perspective of hospital information department. Decis. Support Syst. 44(1), 350–359 (2007)
Rogers, E.M.: Diffusion of Innovations: modifications of a model for telecommunications. In: Die diffusion von innovationen in der telekommunikation, pp. 25–38. Springer, Berlin (1995)
Gangwar, H., Date, H., Ramaswamy, R.: Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. J. Enterprise Inf. Manage. (2015)
Gide, E., Sandu, R.: A study to explore the key factors impacting on cloud-based service adoption in Indian SMEs. In: 2015 IEEE 12th International Conference on e-Business Engineering, pp. 387–392. IEEE (2015)
Kouhizadeh, M., Saberi, S., Sarkis, J.: Blockchain technology and the sustainable supply chain: theoretically exploring adoption barriers. Int. J. Prod. Econ. 231, 107831 (2021)
Kamble, S., Gunasekaran, A., Arha, H.: Understanding the blockchain technology adoption in supply chains-Indian context. Int. J. Prod. Res. 57(7), 2009–2033 (2019)
Makena, J.N.: Factors that affect cloud computing adoption by small and medium enterprises in Kenya. Int. J. Comput. Appl. Technol. Res. 2(5), 517–521 (2013)
Kuan, K.K., Chau, P.Y.: A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Inf. Manage. 38(8), 507–521 (2001)
Queiroz, M.M., Wamba, S.F.: Blockchain adoption challenges in supply chain: an empirical investigation of the main drivers in India and the USA. Int. J. Inf. Manage. 46, 70–82 (2019)
Xu, W., Ou, P., Fan, W.: Antecedents of ERP assimilation and its impact on ERP value: a TOE-based model and empirical test. Inf. Syst. Front. 19(1), 13–30 (2017)
Wong, L.W., Leong, L.Y., Hew, J.J., Tan, G.W.H., Ooi, K.B.: Time to seize the digital evolution: adoption of blockchain in operations and supply chain management among Malaysian SMEs. Int. J. Inf. Manage. 52, 101997 (2020)
Umam, B., Darmawan, A.K., Anwari, A., Santosa, I., Walid, M., Hidayanto, A.N.: Mobile-based smart regency adoption with TOE framework: an empirical inquiry from Madura Island Districts. In: 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), pp. 1–6. IEEE (2020)
Oliveira, T., Thomas, M., Espadanal, M.: Assessing the determinants of cloud computing adoption: an analysis of the manufacturing and services sectors. Inf. Manage. 51(5), 497–510 (2014)
Pateli, A., Mylonas, N., Spyrou, A.: Organizational adoption of social media in the hospitality industry: an integrated approach based on DIT and TOE frameworks. Sustainability 12(17), 7132 (2020)
Premkumar, G., Roberts, M.: Adoption of new IT in rural small business. Omega 27, 467–484 (1999)
Baral, M.M., Verma, A.: Cloud computing adoption for healthcare: an empirical study using SEM approach. FIIB Bus. Rev. 23197145211012505 (2021)
Al Hadwera, A., Tavana, M., Gillis, D., Rezania, D.: A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet of Things 100407 (2021)
Badi, S., Ochieng, E., Nasaj, M., Papadaki, M.: Technological, organisational and environmental determinants of smart contracts adoption: UK construction sector viewpoint. Constr. Manag. Econ. 39(1), 36–54 (2021)
Ergado, A.A., Desta, A., Mehta, H.: Determining the barriers contributing to ICT implementation by using technology-organization-environment framework in Ethiopian higher educational institutions. Educ. Inf. Technol. 26(3), 3115–3133 (2021)
Abed, S.S.: Social commerce adoption using TOE framework: an empirical investigation of Saudi Arabian SMEs. Int. J. Inf. Manage. 53, 102118 (2020)
Stjepić, A.M., Pejić Bach, M., Bosilj Vukšić, V.: Exploring risks in the adoption of business intelligence in SMEs using the TOE framework. J. Risk Financ. Manage. 14(2), 58 (2021)
Seshadrinathan, S., Chandra, S.: Exploring factors influencing adoption of blockchain in accounting applications using technology–organization–environment framework. J. Int. Technol. Inf. Manage. 30(1), 30–68 (2021)
Shahzad, F., Xiu, G., Khan, I., Shahbaz, M., Riaz, M.U., Abbas, A.: The moderating role of intrinsic motivation in cloud computing adoption in online education in a developing country: a structural equation model. Asia Pac. Educ. Rev. 21(1), 121–141 (2020)
Skafi, M., Yunis, M.M., Zekri, A.: Factors influencing SMEs’ adoption of cloud computing services in Lebanon: an empirical analysis using toe and contextual theory. IEEE Access 8, 79169–79181 (2020)
Singeh, F.W., Abrizah, A., Kiran, K.: Bringing the digital library success factors into the realm of the technology-organization-environment framework. Electron. Libr. (2020)
Sharma, M., Gupta, R., Acharya, P.: Prioritizing the critical factors of cloud computing adoption using multi-criteria decision-making techniques. Glob. Bus. Rev. 21(1), 142–161 (2020)
Cruz-Jesus, F., Pinheiro, A., Oliveira, T.: Understanding CRM adoption stages: empirical analysis building on the TOE framework. Comput. Ind. 109, 1–13 (2019)
Pal, S.K., Mukherjee, S., Baral, M.M., Aggarwal, S.: Problems of big data adoption in the healthcare industries. Asia Pac. J. Health Manag. (2021)
Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: Indeed, a silver bullet. J. Market. Theory Pract. 19(2), 139–152 (2011)
Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P.: Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88(5), 879 (2003)
Hair Jr, J.F., Sarstedt, M., Hopkins, L., Kuppelwieser, V.G.: Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur. Bus. Rev. (2014)
Nunnally, J.C.: Psychometric Theory 3E. Tata McGraw-Hill Education (1994)
Henseler, J., Ringle, C.M., Sinkovics, R.R.: The use of partial least squares path modeling in international marketing. Emerald Group Publishing Limited, In New challenges to international marketing (2009)
DeVellis, R. F., Lewis, M. A., & Sterba, K. R.: Interpersonal emotional processes in adjustment to chronic illness. Social psychological foundations of health and illness, 256–287 (2003).
Fornell, C., Larcker, D.F.: Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 18(1), 39–50 (1981)
Byrne, B.M.: Structural equation modeling with AMOS: basic concepts, applications, and programming (multivariate applications series). Taylor & Francis Group 396, 7384 (2010)
Kline, R. B.: Assumptions in structural equation modeling. - PsycNET. (2012).
Park, J. H., Kim, M. K., & Paik, J. H.: The factors of technology, organization and environment influencing the adoption and usage of big data in Korean firms (2015).
Maroufkhani, P., Ismail, W. K. W., & Ghobakhloo, M.: Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management (2020).
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Mukherjee, S., Chittipaka, V., Mohan Baral, M. (2022). A Structural Equation Modeling Approach for Adoption of Big Data Analytics by SMEs in India. In: Gupta, D., Goswami, R.S., Banerjee, S., Tanveer, M., Pachori, R.B. (eds) Pattern Recognition and Data Analysis with Applications. Lecture Notes in Electrical Engineering, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-19-1520-8_20
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