Hierarchical Clustering for Functionalities E-Commerce Adoption

Web functionality is one driver for e-commerce adoption. It has appeared the level of technological capabilities as well as the accentuation of the strategy put on ecommerce by the organization. Web functionality is related to the level of e-commerce relocation. A website with more functionality will give way better benefits for shoppers and trade partners. Functionalities of the web are components that support the achievement of adoption benefits. Hierarchical clustering and ranking availability of e-commerce functionality is a challenging task. Ward Linkage algorithm was used to measure distance. This study proposed to get a grouping of e-commerce functionalities that influence e-commerce adoption and to get the ranking of the groups that most influence the achievement of these benefits. The result shows that functionalities that support the achievement of every benefit of e-commerce have been clustered into two or three clusters, where each cluster also has been ranked to facilitate the achievement of these benefits. The contribution of this research is to produce recommendations on the existence of some e-commerce functionalities that must be actively accessed by users so that the benefits of using e-commerce to manage the user’s business can be achieved. The novelty of this study is a grouping of ecommerce functionality that must be available and actively accessed by users. At present, there are no studies that examining this study

[1]  Arif Djunaidy,et al.  A Maturity Model for E-Commerce Adoption By Small And Medium Enterprises In Indonesia , 2017, J. Electron. Commer. Organ..

[2]  E. Santarelli,et al.  The Diffusion of E-commerce among SMEs: Theoretical Implications and Empirical Evidence , 2003 .

[3]  S. Kurnia,et al.  E-Commerce Technology Adoption: A Malaysian Grocery SME Retail Sector Study , 2015 .

[4]  Feng Ying,et al.  Empirical research on interaction between electronic commerce adoption and effect evaluation in China SMEs , 2012, 2012 International Conference on Information Management, Innovation Management and Industrial Engineering.

[5]  Mohd Rizaimy Shaharudin,et al.  Determinants of electronic commerce adoption in Malaysian SMEs' furniture industry , 2012 .

[6]  Arif Djunaidy,et al.  Mapping requirements into e-commerce adoption level: A case study Indonesia SMEs , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[7]  Jay Ick Lim,et al.  Factors affecting e-commerce adoption among SMEs in Ghana , 2016 .

[8]  Hart O. Awa,et al.  Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs , 2015 .

[9]  Sandy Chong,et al.  Journal of Enterprise Information Management Success in electronic commerce implementation: A cross-country study of small and medium-sized , 2022 .

[10]  Kevin Zhu,et al.  Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level , 2006, Inf. Manag..

[11]  S. Subba Rao,et al.  Electronic commerce development in small and medium sized enterprises: A stage model and its implications , 2003, Bus. Process. Manag. J..

[12]  Arif Djunaidy,et al.  Development of a conceptual model of E-commerce adoption for SMEs in Indonesia , 2013, 2013 International Conference on Information Technology and Electrical Engineering (ICITEE).

[13]  Jinlong Bao,et al.  A Conceptual Model of Factors Affecting e-Commerce Adoption by SMEs in China , 2010, 2010 International Conference on Management of e-Commerce and e-Government.

[14]  S. S. Alam.  Adoption of internet in Malaysian SMEs , 2009 .

[15]  Syed Abbas Ali,et al.  Analyzing undergraduate students' performance using educational data mining , 2017, Comput. Educ..

[16]  Budi Santosa,et al.  DATA MINING : Teknik Pemanfaatan Data untuk Keperluan Bisnis , 2011 .

[17]  Nabeel Al,et al.  Electronic commerce in small to medium-sized enterprises: frameworks, issues and implications , 2003 .

[18]  Kai Wang,et al.  E-commerce personalized recommendation analysis by deeply-learned clustering , 2020, J. Vis. Commun. Image Represent..

[19]  Rajesri Govindaraju,et al.  E-commerce adoption by Indonesian small, medium, and micro enterprises (SMMEs): Analysis of goals and barriers , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[20]  Cheng-Hsien Tang,et al.  An efficient distributed hierarchical-clustering algorithm for large scale data , 2010, 2010 International Computer Symposium (ICS2010).

[21]  Chang-Hua Liu,et al.  Communication Base Station Log Analysis Based on Hierarchical Clustering , 2017 .

[22]  Athanasios V. Vasilakos,et al.  Data Mining for the Internet of Things: Literature Review and Challenges , 2015, Int. J. Distributed Sens. Networks.

[23]  E. Ramsey,et al.  A Profile of Adopters and Non-Adopters of Ecommerce in SME Professional Service Firms , 2005 .