The first chapter has afforded readers an opportunity to review the definitions and components of knowledge management. The chapter also established that knowledge management is closely linked to technology. Explicit knowledge, which is a product of several major technologies, is the focus of this chapter. Specifically, this chapter addresses data mining.One among a host of recent technology innovations, data mining is making changes to the entire makeup of our skills and comfort zones in information analysis. Not only does it introduce an array of new concepts, methods, and phrases, it also departs from the well-established, traditional, hypothesis-based statistical techniques. Data mining is a new type of exploratory and predictive data analysis whose purpose is to delineate systematic relations between variables when there are no (or incomplete) a priori expectations as to the nature of those relations.Herman Hollerith's invention of punch cards in 1880 and of a counting machine for the 1890 census led to the development of modern data management and computing techniques. Thearling (1995) even chronicled the evolution of data as data collection in the 1960s, data access in the 1980s, data navigation in the 1990s, and data mining in the new century. Thearling (1995) and others foresaw the possibilities of data mining as a result of maturity of all three disciplines: massive data collection and storage, powerful multiprocessor computers, and data mining algorithms. According to Rubenking (2001), "data mining is a logical evolution in database technology. The earliest databases, which served as simple replacements for paper records, were data repositories that provided little more than the capability to summarize and report. With the development of query tools such as SQL
Purpose In an online shopping environment, individual reviews and aggregated ratings are important anchors for consumers’ purchasing decisions. However, few studies have considered the influence of aggregated ratings on consumer decision-making, especially at the neural level. This study aims to bridge this gap by investigating the consumer decision-making mechanism based on aggregated ratings to uncover the underlying neural basis and psychological processing. Design/methodology/approach An event-related potential experiment was designed to acquire consumers’ electrophysiological records and behavioral data during their decision-making processes based on aggregated ratings. The authors speculate that during this process, review valence categorization (RVC) processing occurs, which is indicated by late positive potential (LPP) components. Findings Results show that LPP components were elicited successfully, and perceptual review valence can modulate its amplitudes (one-star [negative] and five-star [positive] ratings evoke larger LPP amplitudes than three-star [neutral] ratings). The electroencephalogram data indicate that consumer decision-making processes based on aggregated ratings include an RVC process, and behavioral data show that easier review valence perception makes the purchase decision-making easier. Originality/value This study enriches the extant literature on the impact of aggregated ratings on consumer decision-making. It helps understand how aggregated ratings affect consumers’ online shopping decisions, having significant management implications. Moreover, it shows that LPP components can be potentially used by researchers and companies to evaluate and analyze consumer emotion and categorization processing, serving as an important objective physiological indicator of consumer behavior.
Knowledge management is about using the brain power of an organization in a systematic and organized manner in order to achieve efficiencies, ensure competitive advantage, and spur innovation. This chapter discusses the fundamentals of knowledge management, its definitions, components, processes, and relevance for higher education, in general, and institutional research, in particular.
Background Mobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians’ participation in mHealth apps. Objective This study aims to empirically explore factors affecting physicians’ usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings. Methods We collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians’ usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias. Results Our results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians’ cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention. Conclusions This study contributes to the existing literature on UTAUT2 extension of physicians’ acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians’ usage behaviors on mHealth apps in China and provide such apps’ managers with an insight into the promotion of physicians’ active acceptance and usage behaviors.
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