Skip to main content

Advertisement

SpringerLink
Log in

A study of the CSFs of an e-cluster platform adoption for microenterprises

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

Industrial electronic-cluster (e-cluster) combines electronic business (e-business) and industrial cluster, and this combination has been shown to enhance competitiveness. However, most previous studies of e-cluster have focused on small and medium enterprises (SMEs), but few have addressed e-clustering in microenterprises (MEs). MEs have some unique disadvantages compared to SMEs, and in order to enhance their competitiveness, an understanding the factors that affect their adoption of an e-cluster is an important issue for the e-cluster platform operator. This study adopted two multi-criteria decision-making methods, Fuzzy analytical hierarchy process (FAHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), to identify objectively the critical success factors (CSFs) for MEs to consider in the adoption of e-cluster. The research findings show that strengthening the four CSFs: product quality, product development and commercialization, product exposure, and market channel strength are the most important factors the successful adoption of e-cluster by MEs. Finally, these results offer some implications that provide a useful reference for MEs in adopting e-cluster as well as for e-cluster operators to use to convince MEs to join their e-cluster platforms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Aguarón J, Moreno-Jiménez JM (2003) The geometric consistency index: approximated thresholds. Eur J Oper Res 147(1):137–145

    Article  Google Scholar 

  2. Aral S, Weill P (2007) IT assets, organizational capabilities, and firm performance: how resource allocations and organizational differences explain performance variation. Org Sci 18(5):763–780

    Article  Google Scholar 

  3. Bassir NF, Zakaria Z, Hasan HA, Alfan E (2014) Factors influencing the adoption of Islamic home financing in Malaysia. Transform Bus Econ 13(1):155–174

    Google Scholar 

  4. Brown DH, Lockett NJ (2001) Engaging SMEs in e-commerce: the role of intermediaries within e-clusters. Electron Mark 11(1):52–58

    Article  Google Scholar 

  5. Buss A, Uppal R, Vilkov G (2011) Asset prices in general equilibrium with transactions costs and recursive utility. Handwork Material

  6. Carbonara N (2005) Information and communication technology and geographical clusters: opportunities and spread. Technovation 25(3):213–222

    Article  Google Scholar 

  7. Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making: methods and applications. Springer-Verlag, New York

  8. Christidis K, Mentzas G (2013) A topic-based recommender system for electronic marketplace platforms. Expert Syst Appl 40(11):4370–4379

    Article  Google Scholar 

  9. Claire B (2015) The concentration phenomenon in e-commerce. In: Crew MA, Brennan TJ (eds) Portal and delivery innovation in the digital economy. Springer, Cham, pp 31–41

    Google Scholar 

  10. Csutora R, Buckley JJ (2001) Fuzzy hierarchical analysis: the lambda-max method. Fuzzy Sets Syst 120(2):181–195

    Article  Google Scholar 

  11. Daniel DR (1961) Management information crisis. Harvard Bus Rev 39:111–121

    Google Scholar 

  12. Davidović M (2014) Development of e-clusters. MIPRO 26–30 May, Opatija, Croatia, 1563–1568

  13. Dias A Jr, Ioannou P (1996) Company and project evaluation model for privately promoted infrastructure projects. J Constr Eng Manag 122(1):71–82

    Article  Google Scholar 

  14. Ding K (2010) The role of the specialized markets in upgrading industrial clusters in China. In: Kuchiki A, Tsuji M (eds) From agglomeration to innovation: upgrading industrial clusters in emerging economies. Palgrave McMillian, London, pp 270–289

    Chapter  Google Scholar 

  15. Dini M, Humphrey J (2001) Promoting networks of small enterprises in Latin America. In: Levitsky J, Mikkelsen LH (eds) Micro and small enterprises in Latin America. Inter American Development Group Publishing, London, pp 55–66

    Chapter  Google Scholar 

  16. Dou, H. (2006). Studying the roles of government on industrial clusters. In: Ph.D. dissertation, Shandong University, Jinan

  17. Duke JM, Aull-Hyde R (2002) Identifying public preferences for land preservation using the analytic hierarchy process. Ecol Econ 42(1–2):131–145

    Article  Google Scholar 

  18. Elliot S, Fowell S (2000) Expectations versus reality: a snapshot of consumer experiences with Internet retailing. Int J Inform Manag 20(5):323–336

    Article  Google Scholar 

  19. Fitch D (2004) Measuring convenience: Scots’ perceptions of local food and retail provision. Int J Retail Distrib Manag 32(2–3):100–108

    Article  Google Scholar 

  20. Gazé P, Vaubourg AG (2011) Electronic platforms and two-sided markets: a side-switching analysis. J High Technol Manag Res 22(2):158–165

    Article  Google Scholar 

  21. Giuliani E, Pietrobelli C, Rabellotti R (2005) Upgrading in global value chain: lesson from Latin America cluster. World Dev 33(4):549–573

    Article  Google Scholar 

  22. Gordon I, McCann P (2000) Industrial clusters: complexes, agglomeration and/or social networks? Urban Stud 37(3):513–532

    Article  Google Scholar 

  23. Gunasekaran A, Raia BK, Griffinb M (2011) Resilience and competitiveness of small and medium size enterprises: an empirical research. Int J Prod Res 49(8):33–46

    Google Scholar 

  24. Hagen M, Park S (2013) Ambiguity acceptance as a function of project management: a new critical success factor. Project Manag J 44(2):52–66

    Article  Google Scholar 

  25. Hair JF, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis, 7th edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  26. Hooley GJ, Greenley GE, Cadogan JW, Fahy J (2005) The performance impact of marketing resource. J Bus Res 58(1):18–27

    Article  Google Scholar 

  27. Iammarino S, McCann P (2006) The structure and evolution of industrial clusters: transactions, technology and knowledge spillovers. Res Policy 35(7):1018–1036

    Article  Google Scholar 

  28. Ika LA, Diallo A, Thuillier D (2012) Critical success factors for World Bank projects: an empirical investigation. Int J Project Manag 30(1):105–116

    Article  Google Scholar 

  29. Iyer KC, Jha KN (2006) Critical factor affecting schedule performance: evidence from Indian construction project. J Constr Eng Manag 132(8):871–881

    Article  Google Scholar 

  30. Johnson S (1998) Programme impact assessment in micro-finance: the need for analysis of real markets. IDS Bulletin 29(4):21–30

    Article  Google Scholar 

  31. Joines JL, Scherer CW, Scheufele DA (2003) Exploring motivations for consumer web use and their implications for e-commerce. J Consum Market 20(2):90–108

    Article  Google Scholar 

  32. Keeble D, Wilkinson F (1999) Collective learning and knowledge development in the evolution of regional clusters of high technology SMEs in Europe. Reg Stud 33(4):295–303

    Article  Google Scholar 

  33. Kelley, L. (1992). The strategy palette. Communication Art. May/June, pp 134–139

  34. Lai YL, Hsu MS, Lin FJ, Chen YM, Lin YH (2014) The effects of industry cluster knowledge management on innovation performance. J Bus Res 67(5):734–739

    Article  Google Scholar 

  35. Lam PK, Chin KS (2005) Identifying and prioritizing critical success factors for conflict management in collaborative new product development. Ind Mark Manag 34(8):761–772

    Article  Google Scholar 

  36. Lee JH, Choi MK, Lee HS (2015) Factors affecting smart learning adoption in workplaces: comparing large enterprises and SMEs. Inf Technol Manag 16(4):291–302

    Article  Google Scholar 

  37. Lee SH (2016) Factors influencing the social networking service user’s value perception and word of mouth decision of corporate post with special reference to the emotional attachment. Inf Technol Manag 17(1):15–27

    Article  Google Scholar 

  38. Lee WB, Cheung CF, Lau HCW, Choy KL (2003) Development of a web-based enterprise collaborative platform for networked enterprises. Bus Process Manag J 9(1):46–59

    Article  Google Scholar 

  39. Li H, de Zubielqui GC, O’Connor A (2015) Entrepreneurial networking capacity of cluster firms: a social network perspective on how shared resources enhance firm performance. Small Bus Econ 45(3):523–541

    Article  Google Scholar 

  40. Li W, Veliyath R, Tan J (2013) Network characteristics and firm performance: an examination of the relationships in the context of a cluster. J Small Bus Manag 51(1):1–22

    Article  Google Scholar 

  41. Liu B, Fu Z (2011) Relationship between strategic orientation and organizational performance in born global: a critical review. Int J Bus Manag 6(3):109–115

    Article  Google Scholar 

  42. Machikita T (2010) Industrial clusters and workplace training to expand innovation capability: evidence from manufacturing in Greater Bangkok, Thailand. In: Kuchiki A, Tsuji M (eds) From agglomeration to innovation: upgrading industrial clusters in emerging economies. Palgrave McMillian, London, pp 290–325

    Chapter  Google Scholar 

  43. Mason C, Castleman T, Parker C (2004) Knowledge management for SME‐based regional clusters. In: Proceedings of the 2004 collecter E‐commerce conference

  44. Matawale CR, Saurav D, Sankar MS (2014) Leanness estimation procedural hierarchy using interval-valued fuzzy sets (IVFS). Benchmarking 21(2):150–183

    Article  Google Scholar 

  45. Mawardi MK, Choi T, Perera N (2011) The factors of SME cluster developments in a developing country: the case of Indonesian clusters. In: ICSB World Conference, Stockholm, Sweden, pp 408–408

  46. McCain G, Tennyson SA, Eggert RJ, Hatten SA (2011) Enhancing rural manufacturers’ competitiveness through design automation for new product development. J Bus Econ Res 2(9):81–88

    Google Scholar 

  47. Mindlin YB, Zhukov BM, Prokhorova VV, Shutilov FV, Belova EO (2016) Main stages of the formation of an economic cluster. Int J Econ Finan Issue 6(1S):261–265

    Google Scholar 

  48. Morgan A, Colebourne D, Thomas B (2006) The development of ICT advisors for SME businesses: an innovative approach. Technovation 26(8):980–987

    Article  Google Scholar 

  49. Muller R, Turner R (2007) The influence of project managers on project success criteria and project success by type of project. Eur Manag J 25(4):298–309

    Article  Google Scholar 

  50. Muscio A (2007) The impact of absorptive capacity on SMEs’ collaboration. Econ Innov New Technol 16(8):653–668

    Article  Google Scholar 

  51. Nishimura J, Okamuro H, Nishimura J, Okamuro H (2011) R&D productivity and the organization of cluster policy: an empirical evaluation of the industrial cluster project in Japan. J Technol Transfer 36(2):117–144

    Article  Google Scholar 

  52. O’Sullivan A (2009) Urban economics, 7th edn. McGraw Hill Irvin, New York

    Google Scholar 

  53. Oh W, Pinsonneault A (2007) On the assessment of the strategic value of information technologies: conceptual and analytical approaches. MIS Q 31(2):239–265

    Article  Google Scholar 

  54. Opricovic S (1998) Multicriteria optimization of civil engineering systems, faculty of civil engineering, Belgrade

  55. Opricovic S, Tzeng GH (2007) Extended VIKOR method in comparison with outranking methods. Eur J Oper Res 178(2):514–529

    Article  Google Scholar 

  56. Paiva T, Domingues C, Andrade LP (2016) Innovation and knowledge transference in a cluster user-driven innovation perspective-the inovcluster experience. Int J Food Stud 5:54–60

    Google Scholar 

  57. Pike S, Mason R (2010) Destination competitiveness through the lens of brand positioning: the case of Australia’s sunshine coast current issues in tourism. Curr Issues Tour 14(6):169–182

    Google Scholar 

  58. Poledníková, E., Kashi, K. (2014). Using MCDM methods: evaluation of regional innovation performance in the Czech Republic. In: European conference on management, leadership & governance, pp 487–496

  59. Ravichandran T, Lertwongsatien C (2005) Effect of information systems resources and capabilities on firm performance: a resource-based perspective. J Manag Inf Syst 21(4):237–276

    Article  Google Scholar 

  60. Reynolds PD, Bygrave WD, Autio E, Cox LW, Hay M (2006) Global entrepreneurship monitor 2005, executive report. Babson College/Ewing Marion Kauffman Foundation, London Business School, London

    Google Scholar 

  61. Robbins SP (1994) Management. Prentice Hall, Englewood Cliffs

    Google Scholar 

  62. Saaty TL (1980) The analytic hierarchy process. McGraw Hill, New York

    Google Scholar 

  63. Saaty TL (1996) The analytic network process. RWS Publications Expert Choice Inc, Pittsburgh

    Google Scholar 

  64. Saaty TL, Wind Y (1980) Marketing application of the analytic hierarchy process. Manag Sci 26(7):641–658

    Article  Google Scholar 

  65. Sawhney M, Verona G, Prandelli E (2005) Collaborating to create: the internet as a platform for customer engagement in product innovation. J Interact Market 19(4):4–17

    Article  Google Scholar 

  66. Schmitz H (1995) Collective efficiency: growth path for small-scale industry. J Dev Stud 31(4):529–566

    Article  Google Scholar 

  67. Sellitto C, Wenn A, Burgress S (2003) A review of Web sites of small Australian wineries: motivations, goals and success. Inf Technol Manag 4(2):215–232

    Article  Google Scholar 

  68. Smith TR, Zeng ML, Knowledge TOA (2002) Structured models of scientific concepts for organizing, accessing, and using learning materials. In: The seventh international ISKO conference

  69. Tagliavini M, Ravarini A, Antonelli A (2001) An evaluation model for electronic commerce activities within SMEs. Inf Technol Manag 2(2):211–230

    Article  Google Scholar 

  70. Tavitiyaman P, Qu H, Zhang HQ (2011) The impact of industry force factors on resource competitive strategies and hotel performance. Int J Hosp Manag 30(3):648–658

    Article  Google Scholar 

  71. Templeton GF, Byrd TA (2003) Determinants of the relative advantage of a structured SDM during the adoption stage of implementation. Inf Technol Manag 4(4):409–428

    Article  Google Scholar 

  72. Van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11(3):199–227

    Google Scholar 

  73. Waits MJ (2000) The added value of the industrial cluster approach to economic analysis, strategy development, and service delivery. Econ Dev Q 14(1):35–51

    Article  Google Scholar 

  74. Wasim B (2012) Industrial clusters, Schumpeterian innovations and entrepreneurs’ human and social capital. Pakistan Econ Soc Rev 50(1):71–95

    Google Scholar 

  75. Wennberg K, Lindqvist G (2010) The effect of clusters on the survival and performance of new firms. Small Bus Econ 34(3):221–241

    Article  Google Scholar 

  76. Woisetschläger DM, Hanning D, Backhaus C (2016) Why frontline employees engage as idea collectors: an assessment of underlying motives and critical success factors. Ind Mark Manag 52:109–116

    Article  Google Scholar 

  77. Yoon K (1987) A reconciliation among discrete compromise solutions. J Oper Res Soc 38(3):277–286

    Article  Google Scholar 

  78. Yu CS (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29:1969–2001

    Article  Google Scholar 

  79. Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New York

  80. Zhang S (2010) On the development of science and technology service industry of Shenyang City. Int J Bus Manag 5(8):174–178

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Marketing and Distribution Management, National Kaohsiung First University of Science and Technology, 2, Jhuoyue Rd, Nanzih District, Kaohsiung City, 811, Taiwan, ROC

    Hsin-Pin Fu, Hsiaoping Yeh & Rong-Ling Ma

Authors
  1. Hsin-Pin Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsiaoping Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rong-Ling Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hsin-Pin Fu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, HP., Yeh, H. & Ma, RL. A study of the CSFs of an e-cluster platform adoption for microenterprises. Inf Technol Manag 19, 231–243 (2018). https://doi.org/10.1007/s10799-017-0284-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-017-0284-x

Keywords

Search

Navigation