• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.151-171
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• 2014

Firms today have sought management effectiveness and efficiency utilizing information technologies (IT). Numerous firms are outsourcing specific information systems functions to cope with their short of information resources or IT experts, or to reduce their capital cost. Recently, Software-as-a-Service (SaaS) as a new type of information system has become one of the powerful outsourcing alternatives. SaaS is software deployed as a hosted and accessed over the internet. It is regarded as the idea of on-demand, pay-per-use, and utility computing and is now being applied to support the core competencies of clients in areas ranging from the individual productivity area to the vertical industry and e-commerce area. In this study, therefore, we seek to quantify the value that SaaS has on business performance by examining the relationships among firm strategies, SaaS technology maturity, and business performance of SaaS providers. We begin by drawing from prior literature on SaaS, technology maturity and firm strategy. SaaS technology maturity is classified into three different phases such as application service providing (ASP), Web-native application, and Web-service application. Firm strategies are manipulated by the low-cost strategy and differentiation strategy. Finally, we considered customer acquisition as a business performance. In this sense, specific objectives of this study are as follows. First, we examine the relationships between customer acquisition performance and both low-cost strategy and differentiation strategy of SaaS providers. Secondly, we investigate the mediating and moderating effects of SaaS technology maturity on those relationships. For this purpose, study collects data from the SaaS providers, and their line of applications registered in the database in CNK (Commerce net Korea) in Korea using a questionnaire method by the professional research institution. The unit of analysis in this study is the SBUs (strategic business unit) in the software provider. A total of 199 SBUs is used for analyzing and testing our hypotheses. With regards to the measurement of firm strategy, we take three measurement items for differentiation strategy such as the application uniqueness (referring an application aims to differentiate within just one or a small number of target industry), supply channel diversification (regarding whether SaaS vendor had diversified supply chain) as well as the number of specialized expertise and take two items for low cost strategy like subscription fee and initial set-up fee. We employ a hierarchical regression analysis technique for testing moderation effects of SaaS technology maturity and follow the Baron and Kenny's procedure for determining if firm strategies affect customer acquisition through technology maturity. Empirical results revealed that, firstly, when differentiation strategy is applied to attain business performance like customer acquisition, the effects of the strategy is moderated by the technology maturity level of SaaS providers. In other words, securing higher level of SaaS technology maturity is essential for higher business performance. For instance, given that firms implement application uniqueness or a distribution channel diversification as a differentiation strategy, they can acquire more customers when their level of SaaS technology maturity is higher rather than lower. Secondly, results indicate that pursuing differentiation strategy or low cost strategy effectively works for SaaS providers' obtaining customer, which means that continuously differentiating their service from others or making their service fee (subscription fee or initial set-up fee) lower are helpful for their business success in terms of acquiring their customers. Lastly, results show that the level of SaaS technology maturity mediates the relationships between low cost strategy and customer acquisition. That is, based on our research design, customers usually perceive the real value of the low subscription fee or initial set-up fee only through the SaaS service provide by vender and, in turn, this will affect their decision making whether subscribe or not.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.