As more and more consumers become part of the net population, retailers and manufacturers as well as dot-com storefronts are touting consumers by providing an ever-increasing amount of product information. Their long-term survival and profitability may be determined by how much and how well their product information is presented to and processed by the consumers. By combining both the traditional and structural approaches to the informationoverload phenomenon, this study investigates the impact of Web site information on consumer choice and psychological states in an environment. Varying the number of alternatives and on-line attributes (traditional measure) and attribute level distribution across alternatives (structural measure), this study asks subjects to choose the best (dominant) CD player in a given set. Their subjective states such as satisfaction, confidence, and confusion are also measured. Results show that the number of attributes and attribute level distribution are good predictors of the effect of information overload on consumer choice. In addition, the study finds that online information overload results in less satisfied, less confident, and more confused consumers. Implications and suggestions for future research are provided. ᭧

Despite frequent acknowledgment that interactivity is multidimensional, previous studies have measured and treated the construct as if it were unidimensional, failing to see the differences that exist among latent dimensions. This study investigates how the latent factors of perceived interactivity differ in terms of their relationships with various social factors, including social network density and frequency of interactions, as well as with psychological factors, such as individuals' need for cognition. By conducting a factor analysis and a series of multiple regression analyses, it was found that the dimensions were differentially influenced by social and psychological indicators.

As more and more consumers spend more money on the Internet, traditional retailers and manufacturers as well as entrepreneurial dot‐coms are jousting to explore and shape this new business opportunity. Their long‐term survival and profitability may be determined by how well the Web site helps form and sustain positive attitudes toward the site and, eventually, toward the product or the company. The purpose of this study is two‐fold: (1) to examine if and how attitude toward the Web site (Ast) affects consumer brand choice; and (2) to examine the association between Ast and consumers' confidence in choice, and the moderating effect of consumer product knowledge in its relationship. The study asked participants to choose a laptop brand after visiting three laptop manufacturer Web sites for a total of 30 minutes. Their product knowledge and attitude toward the three Web sites were also measured. The study found that attitude toward the Web site is a good predictor of consumer brand choice. In addition, confidence in choice seemed to be affected by Ast, depending on product knowledge. For a group with low product knowledge (novices), Ast was likely to influence confidence in choice. For a group with high product knowledge (experts), however, Ast did not seem to affect confidence in choice.

This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately. In sequence, the sensor messages are converted to the message type of the other protocol and the payloads are transferred to an in-vehicle diagnosis module (In-VDM). The LAVS informs the diagnosis result of Cloud or road side unit(RSU) by the internet of vehicles (IoV) and of drivers by Bluetooth. To design the LAVS, the following two modules are needed. First, a multi-protocol integrated gateway module (MIGM) converts sensor messages for communication between two different protocols, transfers the extracted payloads to the In-VDM, and performs IoV to transfer the diagnosis result and payloads to the Cloud through wireless access in vehicular environment(WAVE). Second, the In-VDM uses random forest to diagnose parts of the vehicle, and delivers the results of the random forest as an input to the neural network to diagnose the total condition of the vehicle. Since the In-VDM uses them for self-diagnosis, it can diagnose a vehicle with efficiency. In addition, because the LAVS converts payloads to a WAVE message and uses IoV to transfer the WAVE messages to RSU or the Cloud, it prevents accidents in advance by informing the vehicle condition of drivers rapidly.

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