دانلود مقاله ISI انگلیسی شماره 22297
عنوان فارسی مقاله

بررسی استفاده مستمر از ابزارهای داده کاوی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
22297 2013 11 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Investigating use continuance of data mining tools
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Information Management, Volume 33, Issue 5, October 2013, Pages 791–801

کلمات کلیدی
قصد تداوم - رضایت - تناسب کار فن آوری - عادت - ابزار داده کاوی
پیش نمایش مقاله
پیش نمایش مقاله بررسی استفاده مستمر از ابزارهای داده کاوی

چکیده انگلیسی

While improving decisional quality is important to businesses, continued use of DMTs is a critical issue for managerial personnel. This problem mainly concerns the willingness of an individual to participate in the behavior. It can be further defined in a routine-based working behavior. This problem essentially involves three key issues, task fit, technology use, and habit. This study therefore integrates task-technology fit (TTF) model, expectation–confirmation mode (ECM), and habit, to examine the determinants of continued use of DMTs. Prior studies have focused on intention to use DMTs in the first time and only considered part of the three issues for identifying the determinants. 285 respondents from managerial personnel were collected to empirically evaluate this research model. The three issues are all important in influencing continuance use intention of DMTs. In particular, the task-technology fit indicates a direct effect on two factors of the technology use issue, user satisfaction and perceived usefulness, and an indirect effect on continuance use intention. User satisfaction and perceived usefulness, and habit are the key predictors of continuance use intention.

مقدمه انگلیسی

Data mining (DM) is a type of data analysis technology which is widely applied in a variety of business functions, including sales, marketing, and customer relations (Davenport and Harris, 2007 and Kohavi et al., 2002). Software providers have integrated related DM functions, such as data warehousing and data analytics, and launched data mining tools (DMTs) in market to aid users in fulfilling the work of data analysis. DMTs can be used to predict future trends and behaviors based on the historical data, allowing businesses to make proactive, knowledge driven decisions (Sharma, Goyal, & Mittal, 2008). In particular, company's employees can employ DMTs to spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty in a building of long-term customer relationships (Strong, Dishaw, & Bandy, 2006). Researchers on DMTs have focused on the development of various algorithms to improve efficiency (Han & Kamber, 2006). However, the critical determinant of successfully using DMT is to reduce the gap between human beings and technologies, not just to enhance the efficiency of DM functions (Davenport and Harris, 2007 and LaValle et al., 2010). An understanding of individual's user behaviors toward DMTs is the key to successfully use DMTs (Huang, Liu, & Chang, 2012). However, existing studies, as in a very few manner, are mostly focused on the first-time use of DMTs. For example, Huang and Chou (2004) examined the adoption of web-based DMTs in B2C virtual firms, as DMTs considered an organizational innovation, in terms of the effect of internal and external factors. However, the long-term viability and eventual success of using an information system depend on its continued use rather than its first-time use (Bhattacherjee, 2001a, Venkatesh and Goyal, 2010 and Venkatesh et al., 2011). Based on the innovation diffusion theory (IDT), users reappraise their earlier acceptance status during the confirmation stage so that they can decide whether to continue or discontinue use of an innovation (Rogers, 2003). Heavy users of information technology (IT) are those who continue to use them in their routine context, making the driving force of long-term productivity for an organization (Kim and Malhotra, 2005 and Venkatesh et al., 2011). More specifically, DMTs are a special type of decision tool for company's employees in terms of providing the decision support for various business activities. It is mainly used to create/accumulate valuable knowledge from a huge amount of raw data for a decision's purpose and then is able to help employees to predict future economic behaviors (Strong et al., 2006). This implies that the users of DMTs are mostly focused on a high level of managerial personnel for a decision purpose. Next, as business decisions are always associated with an unstructured form, decision makers are responsible to make a high quality decision as they may have high degree of freedom to resort to the support of various IT tools. Accordingly, we consider users of DMTs in this study that are those who are managerial personnel and intend to use DMTs in a voluntary mode. Further, decision making, in essence, is a knowledge-based problem solving process and needs to be constantly refined in terms of the new knowledge input until a better form of solutions. The continued use of DMTs in a routine context is particularly important to drive the final benefits in terms of a high quality decisions (Huang et al., 2012). As many studies have discussed the continued use in various information systems (IS) settings, such as Internet based technology and traditional IS, however, they fail to address the issue of continued use of DMTs. As previously stated, DMTs are a decision-supported technology tool used for management personnel in a voluntary manner. Two major concerns thus arise for individual motivation of the continued use of DMTs: task-driven behavior (Chen et al., 2012 and Huang et al., 2012) and technology-driven behavior (Foshay, Mukherjee, & Taylor, 2007). Task-driven behavior may refer to two types of tasks: equivocality and inter-dependence, the former including ill-defined, ad hoc, non-routine, or multi-answer business problems and the latter including the business problems with more than on business function, unit, or company. Technology-driven behavior is defined as the DMT technology use by users, including information usefulness, efficiency performance, and satisfaction. Further, as the use DMTs is in a voluntary mode, a major concern in the literature to explain the behavior of continuance use is self-driven behavior (Barnes, 2011, Chiu et al., 2012, Limayem and Cheung, 2008 and Xu et al., 2012). Self-driven behavior or habit may refer to a special kind of mind-set, cognition, and belief, enhancing the perceptual readiness for habit-related activation (Verplanken & Aarts, 1999). Many researchers have applied task-technology fit (TTF) concept to examine user's intention of IS continuance use in their workplaces in terms of an IS assisting an individual to perform his/her portfolio of tasks (Larsen et al., 2009, Lin, 2012 and Zhou et al., 2010). For the technology-driven behavior, a post-acceptance model, expectation confirmation model (ECM), seeks to explain user's intention toward IS continued use from a technology-use perspective (Premkumar and Bhattacherjee, 2008 and Venkatesh et al., 2011). For the self-driven behavior, habit is considered as an important precursor of user's continuance intention of IS as it is associated with other technology-based factors (Barnes, 2011 and Kim et al., 2005). The findings of these studies all showed a positively significant effect on continuance intention of IS use in terms of their different aspects. Empirically, this implies that a combination of these perspectives may increase the explanatory power for continuance intention to use. As discussed above, we can realize that the major concern of TTF is to investigate user behavior by the macro-level perspective and that of ECM is by the micro-level one. The former explores what external factors drive users to adopt IS in their organizations, and the latter and habit are about internal beliefs of an individual for gaining the motivations. Neither is dispensable for completely indicating the prerequisites of continuance intention to use. From both of the empirical and theoretical arguments, an integration of TTF, ECM, and habit concepts tends to well improve the explanatory power of continuance intention to use. This study thus proposes a comprehensive research model to address the problem of continuance intention to use DMTs. We further examine key drivers from the three theories. Previous studies have discussed the issue of IS continuance use by either a single perspective, such as ECM or technology acceptance model (TAM) (Barnes, 2011, Lee, 2010 and Premkumar and Bhattacherjee, 2008), or an integrative perspective, such as TTF with TAM (Chang, 2008, Chang, 2010 and Yen et al., 2010), and habit associated with some factors from TAM, in terms of Internet based technology or traditional IS (Barnes, 2011, Chiu et al., 2012 and Kang and Lee, 2010). There are few studies to investigate the problem of continued use of DMTs in a robust manner to consider three perspectives. We will elaborate on the relevant theories later. The remainder of this paper is organized as follows: Section 2 for literature review, Section 3 for the research model and hypothesis development, and Section 4 for research design, Section 5 for data analysis, Section 6 for findings and discussions, and Section 7 for conclusions and suggestions.

نتیجه گیری انگلیسی

While the individual impact of each key issue, TTF, ECM, or habit, on the continuance use intention of DMTs was found to be similar or different with the previous studies in various IS settings. This means that these key issues, although all are important for continuance use intention, are not well consistent across different IS settings. In particular, DMTs is a special type of decision-supported tool for managerial personnel and is closely related the routine tasks in a voluntary manner. In order to maximize the predictive power of continuance use intention toward DMTs, the better way to approach this problem should be in a more comprehensive manner to integrate the three key issues for the present research. The empirical findings for the three issues further confirm our arguments, as discussed previously, for developing this research model. As a result, this research model would be more powerful to predict the continued use of DMTs than other research models in the literature, such as TTF mode, ECM, or a combination of TTF model and TAM. There are several contributions for enterprises and software developers. To increase the probability of continued usage, managers need to pay more attention to user satisfaction. When attempting to introduce a new DMT to enterprises, managers can conduct a survey to investigate what users care about when they come to the functions of DMTs. This kind of survey could help managers to understand the task characteristics and users’ expectations. After that, they can buy more suitable DMTs with technology attributes corresponding to task characteristics that are useful for users. Moreover, software developers also need to carefully look at the data from the survey and try to understand the requirements of users. When users are willing to continually adopt existing DMTs, managers do not need to contemplate further investment in DMTs if the existing DMTs fit users. Software developers could always maintain their existing DMTs to reduce costs and avoid further investment in developing new versions of DMTs if the existing DMTs fit for the needs of users. This study also contributes to a thought on the formation of habit in using DMTs which is necessary for enterprises and software developers. Managers could offer training programs and relevant learning resources so that users can be easy to adopt DMTs in their tasks. As users get used to adopt DMTs to solve their decision-based tasks, the formation of habit in using DMTs is established. Users are also less prone to encounter errors in their tasks; therefore, software developers should not only maintain a good customer services for DMTs product, but reduce the risk of using DMTs in failure when users are working for their tasks to find decision solutions. If software developers keep these thoughts in mind, users could form habits and continue using DMTs. The theoretical contributions of this study are noted below. To the best of our knowledge, this is one of the first studies to contribute to a theoretical understanding of continuously adopting DMTs in this field. Few studies concerned intention to use DMTs from different perspectives in the first-time use. A comprehensive focus on the factors presented for the continued use is surprisingly absent in the literature. Accordingly, this study is proposed to extend the spectrum of DM research. Moreover, as argued previously for the importance of integration in an empirical view, we attempted to further empirically examine some comparative models in terms of a single-theory model or a two-theory model in order to clearly identify the value of this present model. Their results are reported in Table 4. This present model indicates a significant improvement for its explanatory power. Next, because new technical functions are continuously proposed in contemporary business settings, users may often change their considerations for continuing using DMTs. Therefore, this study suggests that further testing of the proposed model may be able to reveal its stability and robustness across different workplaces. This study also contributes to a thought that researchers could be inspired to perform. In an evolving business environment, new factors may emerge to change the nature of the relationships in the proposed model.Future research could be based on this foundation. This study is a cross-sectional research. A longitudinal investigation could be conducted in the future to gain more comprehensive results. This study concerns the factors at the individual level. There might be extrinsic motivation factors at the organization level, such as top management support or social norms, to extend this research model. As a new trend of business intelligence (BI) tools for business decisions, which is similar to DMTs, it could be examined by the current research model to find the differences and similarities. Although this research has produced some useful results, some limitations may be inherent in it. The response rate was lower than desirable, despite the various efforts to improve it. The reasons for this might be twofold. This might be a lack of rich experience in using DMTs for managerial personnel. Next, managerial personnel might be always busy for failing to complete the questionnaire.

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