تحقیقات تجربی از عوامل مؤثر بر استفاده از ابزارهای داده کاوی

Previous studies explored the adoption of various information technologies. However, there is little empirical research on factors influencing the adoption of data mining tools (DMTs), particularly at an individual level. This study investigates how users perceive and adopt DMTs to broaden practical knowledge for the business intelligence community. First, this study develops a theoretical model based on the Technology Acceptance Model 3, and then examines its perceived usefulness, perceived ease of use, and its ability to explain users’ intentions to use DMTs. The model's determinants include 4 categories: the task-oriented dimension (job relevance, output quality, result demonstrability, response time, and format), control beliefs (computer self-efficacy and perceptions of external control), emotion (computer anxiety), and intrinsic motivation (computer playfulness). This study also surveys the moderating effect of experience and output quality on the determinants of DMT adoption and use. An empirical study involving 206 DMT users was conducted to evaluate the model using structural equation modeling. Results demonstrate that the proposed model explains 58% of the variance. The findings of this study have interesting implications with respect to DMT adoption, both for researchers and practitioners.

With the issue of globalization becoming increasingly widespread, global competition among enterprises to profit is fiercer now than it has been in the past. To face the challenges arising globally, more managers are using information technology (IT) and information systems (IS) in business. This allows them to be more efficient and accurate when acquiring information or making decisions. According to a Gartner report (Gartner, 2011a), worldwide enterprise IT spending is projected to total $2.7 trillion dollars in 2012, a 3.9% increase from 2011. Data warehousing technology is one of the paramount investments in establishing IT infrastructure (Gartner, 2011b), enabling enterprises to collect and store vast amounts of data. These data can be extracted and analyzed by data analytics, helping managers find better ways to generate value and compete in the marketplace (Goeke and Faley, 2007 and LaValle et al., 2011). A report by Gartner (2011c) lists data (next-generation) analytics as one of the top 10 strategic technologies. Data analytics has been successfully used in many fields, such as insurance (Hopkins & Brokaw, 2011), e-commerce (Kohavi, Rothleder, & Simoudis, 2002), health care fraud (LaValle, Hopkins, Lesser, Shockley, & Kruschwitz, 2010), and e-retailing (LaValle et al., 2010). Data analytics has had significant effects on both operational and strategic dimensions in enterprises (Davenport & Harris, 2007). For enterprises, data mining (DM) is a data analysis technology that is widely applicable to a variety of businesses, including sales, marketing, and customer relations (Davenport and Harris, 2007, Jackson, 2002 and Kohavi et al., 2002). Software companies have integrated DM functions and launched data mining tools (DMTs) in markets to assist users (consumers of DMT outputs) perform data analysis. DMT can be used to predict future trends and behaviors from historical data, allowing businesses to make proactive, knowledge-driven decisions (Sharma, Goyal, & Mittal, 2008). Managers can use DMTs to spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty (Rygielski et al., 2002 and Sumathi and Sivanandam, 2006). Therefore, DMT achieves the goals of improving customer service, building long-term customer relationships, reducing marketing costs, and increasing sales (Hui and Jha, 2000, Nemati and Barko, 2002, Rygielski et al., 2002 and Shaw et al., 2001). In previous studies on DMT, computer science and information engineering scholars have developed various algorithms to improve the efficiency of DMT (Han & Kamber, 2006). Davenport and Harris (2007) and LaValle et al. (2010) argued that the critical determinant of successfully using data analytics is to reduce the gap between human beings and technologies, and not just to enhance the latter functions. Hence, users like to use DMTs because it offers a variety of functions and good algorithmic performances, improves task performances, and decreases managerial costs, i.e., increases effectiveness. The same idea appears in research on the task-technology fit (Goodhue & Thompson, 1995). Because enterprises have invested a substantial amount of funds into DMT applications, examining the factors that affect DMT users is a beneficial research topic. Dahlan, Ramayah, and Mei (2002) and Dahlan, Ramayah, and Koay (2002) addressed the readiness of telecommunication employees and the banking industry in adopting DM technologies. Chang, Chang, Lin, and Kao (2003) studied the adoption of DM techniques in the financial service industry using five characteristics: organizational size, organizational culture, attitude of data resource, style of decision-making, and competitiveness of the outside environment. Huang and Chou (2004) proposed an analytical model to explore the relationships influencing the stage of web mining adoption. Although prior studies investigate the adoption of DMT, they do so from the perspective of the firm (Chang et al., 2003, Dahlan et al., 2002, Dahlan et al., 2002a and Huang and Chou, 2004). No studies investigate this issue at the individual level completely, as stated by Goodhue and Thompson (1995). Researchers in the IT/IS domain have discussed the problems of individual adoption and acceptance using different theoretical formulations and constructs. The goal of these studies is to understand and explain the important factors affecting acceptance behavior and subsequent IT/IS usage. Fishbein and Ajzen (1975) developed a well-supported behavioral theory, called the theory of reasoned action (TRA), which describes the psychological determinants of behavior. In 1989, Davis and his colleagues proposed an extension of the TRA (Davis, 1989 and Davis et al., 1989), called the technology acceptance model (TAM). This model examines the mediating role of perceived usefulness and perceived ease of use in the relationships between system or individual characteristics (external variables) and probability of system use (an indicator of system success). To make the TAM more complete, Venkatesh and Bala (2008) integrated the models proposed by Venkatesh (2000) and Venkatesh and Davis (2000) and developed a comprehensive nomological network of IT adoption and use, called TAM3. TAM3 investigates the determinants of perceived usefulness and perceived ease of use, and discusses various interventions that can influence the known determinants of IT adoption and use. The findings and research agenda of Venkatesh and Bala (2008) provide important implications for IT implementation. Response time and format remain the major issues in the DM research community (Chen et al., 1996 and Chung and Gray, 1999), and have been studied by computer science and information engineering scholars. According to previous studies on DMTs, two significant determinants primarily influence behavioral intention to use DMTs. As mentioned previously, previous research fails to address the problem of behavioral intention to use DMTs. Because DMT is a type of decision tool for users, research on individual-level IT adoption is a particularly important path to understanding DMT usage. Therefore, this study proposes a comprehensive theoretical model based on TAM3 to address this issue. The findings of this study have significant implications for DMT implementation for researchers and practitioners. The remainder of this paper is organized as follows. Section 2 reviews the literature on DM and TAM3. Section 3 discusses the research model and hypotheses, and Section 4 describes the research methodology. Section 5 presents the finding of analyzing the empirical data. Section 6 discusses the results and concludes the paper with contributions and implications, discussions of the limitations, and directions for future research.

This study takes a step in the direction of investigating user intentions to use DMTs. We draw the following conclusions. First, based on the results of this study, perceived usefulness and perceived ease of use are the factors most affecting an individual’s intention to use DMTs. Furthermore, the task-oriented dimension and control beliefs are two important antecedents that have significant effects on perceived usefulness and perceived ease of userespectively. Emotional and intrinsic motivations are not major factors in this study. Second, the task-oriented dimension and control beliefs are the major predictors in this study. In fact, both categories are the keys to explaining the adoption and acceptance of DMTs. Third, experience does not have a significant moderating effect in this study, except for the effect of perceived ease of use on behavioral intentions. Consequently, experience is not a critical factor in this research model. Finally, people use DMTs to gain useful information or interesting knowledge. Therefore, most DMT users would like to improve their task efficiency or achieve workrelated goals rather than just use the DMT for fun in their leisure time.