Factors affecting the adoption of electronic marketplaces

Abstract

PurposeTo present a three‐layer hierarchical structure of the factors involved in adopting an electronic marketplace (EM) model and to examine the relative weightings given to various strategic factors by the securities industry (SI) and the heavy electric machinery industry in Taiwan.Design/methodology/approachA literature review and a review of nine Taiwanese industries allow the formulation of a three‐layer hierarchical structure of adoption factors. A fuzzy analytic hierarchical process (AHP) is then undertaken to ascertain the relative weightings of factors that affect entry to an EM in two of these industries which are studied in more detail.FindingsThe weights of “proactive” factors are found to be greater than those of “defensive” factors. For example, contrary to previous findings in this area, the “risk of adopting new technology” is not found to be the major factor influencing decision making. Various factors are found to have different routes of influence in determining decision making in different industries.Practical implicationsEnterprises that appreciate the weightings of factors to be considered will be able to facilitate the adoption of an EM model with lower costs and greater efficiency.Originality/valueThe study provides novel and reliable information about strategic factors that are involved in corporate decisions about entering an EM.

Factors Affecting the Development of the Electronic Marketing Capacity of Professional Sports Federations

“…It seems that the level of interest and willingness, and the skills and expertise of the managers of professional sports federations should be high to perform activities through e-commerce. The results of this study are consistent with the results of the studies of Mzee, Ogweno and Irene (2015), and Fu et al (2006) (16,17).…”

A case study exploring drivers and implications of collaborative electronic logistics marketplaces

“…Maintain control of order-to-delivery process McIvor and Humphreys (2004), Ho, Au, and Newton (2003), Lewis and Talalayersky (2000), Goldsby and Eckert (2003) Productivity and efficiency improvement via process automation, improved visibility and quick information exchange Kaplan and Sawhney (2000), McLaren et al (2002), Eng (2004), Rask and Kragh (2004) Resource utilisation and cost reduction Kumar and van Dissel (1996), McLaren et al (2002), Bengtsson and Kock (2000), Skjøtt-Larsen et al (2003), da Silveira and Cagliano (2006), Howard et al (2006) Secure market competitiveness Stockdale and Standing (2004), Fu, Ho, Chen, Chang, and Chien (2006) Pool of complementary resources Kumar and van Dissel (1996), Martinez et al (2001), Bengtsson and Kock (2000) Reach a critical size ('critical mass') Martinez et al (2001), Golicic et al (2002), McIvor and Humphreys (2004), Standing et al (2006), Howard et al (2006) Network optimisation, economy of scale and scope of logistics operations Martinez et al (2001), Standing et al (2006) Risk, infrastructure and expenses sharing Kumar and van Dissel (1996), Bengtsson and Kock (2000), Standing et al (2006) Access to superior technology Frankel and Whipple (1996) Zineldin & Bredenlow (2003) Relational Reduce uncertainties and opportunism in the supply chain Martinez et al (2001), Kumar and van Dissel (1996) Desire to coordinate and collaborate in exchanging information and conducting transactions Kumar and van Dissel (1996), …”

Analytic Hierarchy Process and Supply Chain Management: A Bibliometric Study

“…The main topic identified was the supplier selection with the AHP application. Other combined methods were also applied: Goal programming [16]; Fuzzy logic [19], [22], [25] and [29]; DEA [20] and [21]; Neural network [24] and ELECTRE III [28], as can be seen in Table 2. Table 3 shows details of the cluster II.…”