Cost-benefit factor analysis in e-services using bayesian networks
Introduction
Since the mid-1990s, businesses have spent quite a bit of time, money and effort developing web-based electronic service (e-service) systems. These systems assist businesses in building more effective customer relationships and gaining competitive advantage through providing interactive, personalized, faster e-services to customers (Chidambaram, 2001). Businesses in the earlier stages of employing web-based e-service systems had little data, knowledge, and experience for assessing and evaluating the potential impacts and benefits of e-services for organizations. Organizational efforts were largely geared toward customer service provision with little thought to identifying and measuring the costs involved in e-service development against the benefits received by adopting e-services. After several years’ experience of e-service provision, businesses now urgently need to do it for planning their further development in e-services. Importantly, businesses have obtained related e-service systems running data and knowledge, which can directly help identify which items of investments for an e-service system effectively contribute to what benefit aspects of business objectives.
With the wide development of e-services, researchers have expressed increasing interest in evaluating the success, quality, usability and benefit of e-service systems from various views and using various methods (DeLone and McLean, 2004, Wade and Nevo, 2005). A major focus in this area is the evaluation for the features, functions or usability of e-service systems. Typical approaches used are testing, inspection and inquiry (Hahn & Kauffman, 2002) through a web search or a desk survey such as the results reported in Ng et al., 1998, Smith, 2001, Lu et al., 2001. Another type of related research is the evaluation of customers’ satisfaction for e-services. Questionnaire-based survey and multi-criteria evaluation systems are widely used to conduct this kind of research such as Lin, 2003, Srinivasan et al., 2002. Moreover, some significant results are reported in the establishment of e-service evaluation models and framework, such as the results shown in Lee et al., 1999, Zhang and von Dran, 2000, Giaglis et al., 1999.
However, the research discussed above only focuses on the evaluation of an e-service system itself from the user point of view by measuring either customer satisfaction or functionality of the e-service system. Although some research addresses the view of e-service providers such as Giaglis, Paul, and Doukidis (1999) presented a case study of e-commerce investment evaluation, Drinjak et al., 2001, Piris et al., 2004 investigated the perceived business benefits of investing in e-service systems, and Amir, Awerbuch, and Borgstrom (2000) created a cost-benefit framework for online system evaluation, lack of exploration and deep analysis of possible relations to link these investment items with related business benefits.
Furthermore, businesses would like to know if their investments in e-service systems are successful by conducting cost and benefit analysis. The investments (costs) include e-service related software development, database maintenance, website establishment, staff training and other items. Similarly, the benefits obtained through e-service applications include many aspects, such as increasing the number of customers, better business image, and competitive advantages. Therefore, businesses, as e-service providers, need to know which item(s) of their investments are more important and effective than other items for achieving their business objectives, and also which item(s) of their investments can bring more obvious benefits for certain aspect(s) of the businesses. These results will directly or indirectly support better business strategy making in e-service application developments.
Our previous research reported in (Lu & Zhang, 2003) identified some inter-relationships and interactive impacts among costs and benefits via providing e-services to customers by using the linear regression and ANOVA analysis approaches. Since some inter-relationships among the above mentioned cost-benefit factors may be non-linear, as a further study, this paper reports how these cost-benefit factor relationships are verified and how uncertain relationships are identified by applying the Bayesian network techniques.
After the introduction, this paper outlines our previous work including an e-service cost-benefit factor framework, data collection process, and a cost-benefit factor-relation model in Section 2. Section 3 analyses how Bayesian network techniques are applied in finding any relationships among cost and benefit factors. The detailed process of establishing a cost-benefit Bayesian network and conducting inference among cost and benefit factors are presented in Section 4. Section 5 reports our findings on the relationships between cost and benefit factors in e-service systems. Conclusions and further study are discussed in Section 6.
Section snippets
e-Service cost-benefit factor framework
e-Service cost (C) is the expenses incurred in adopting e-services, such as expense of setting up e-service and maintaining e-service. e-Service benefit (B) is concerned with the benefits gained through employing e-services. Fig. 1 shows 16 benefit factors and 8 cost factors of e-service system developments and applications. All these factors have been well identified and described in Lu and Zhang (2003).
Data collection
This study collected data concerning e-service development costs and benefits from a sample
Bayesian network techniques
Bayesian network techniques are a kind of powerful knowledge representation and reasoning tools under conditions of uncertainty. A Bayesian network B = 〈N, A, Θ〉 is a directed acyclic graph (DAG) 〈N, A〉 with a conditional probability distribution (CPD) for each node, collectively represented byΘ, each node n ∈ N represents a variable, and each arc a ∈ A between nodes represents a probabilistic dependency (Pearl, 1988). In a practical application, the nodes of a Bayesian network represent uncertain
Bayesian networks based cost-benefit factor relationship analysis
In general, there are three main steps when applying Bayesian network techniques in analyzing a set of relationships for a practical problem: (1) creating a graphical Bayesian network structure for the problem, (2) calculating related conditional probabilities to establish a Bayesian network, and (3) using the established Bayesian network to conduct inference for finding possible relationships among these factor nodes of the Bayesian network. The following sub-sections will describe the three
Result analysis
Over all inference results obtained through running the Junction-tree algorithm, five main significant results (Result 1, Result 2, Result 3, Result 4, Result 5) are particularly discussed in the paper. These results are under the evidences that the factor node is with a ‘high’ value. For the other situations, such as under the evidence that the node value is ‘low’, the similar results have been obtained. Result 1 Assuming the cost factor C2 (maintaining e-service) = 4 (high), we have got the probabilities
Conclusions and further study
By applying Bayesian network techniques this study explored and verified a set of relationships between cost factors and benefit factors in the application of e-service systems. A cost-benefit factor-relation model proposed in our previous study was considered as domain knowledge and the data collected through a survey was as evidences to conduct the inference-based verification. Through calculating CPDs among these cost and benefit factors, we found that certain cost factors are more important
Acknowledgement
This research is partially supported by Australian Research Council (ARC) under discovery Grants DP0557154 and DP0559213.
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