Abstract:The era of Big Data analytics has begun in most industries within developed countries. This new analytics tool has raised motivation for experts and researchers to study its impacts to business values and challenges. However, studies which help to understand customers' views and their behavior towards the applications of Big Data analytics are lacking. This research aims to explore and determine the pros and cons of applying Big Data analytics that affects customers' responses in an e-commerce environment. Data analyses were conducted in a sample of 273 respondents from Vietnam. The findings found that information search, recommendation system, dynamic pricing, and customer services had significant positive effects on customers' responses. Privacy and security, shopping addiction, and group influences were found to have significant negative effects on customers' responses. Customers' responses were measured at intention and behavior stages. Moreover, positive and negative effects simultaneously presented significant effect on customers' responses. Each dimension of positive and negative factors had different significant impacts on customers' intention and behavior. Specifically, information search had a significant influence on customers' intention and improved customers' behavior. Shopping addiction had a drastic change from intention to behavior compared to group influences and privacy and security. This study contributes to improve understanding of customers' responses under big data era. This could play an important role to develop sustainable consumers market. E-vendors can rely on Big Data analytics but over usage may have some negative applications.

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Manufacturing firms are always faced with the problem of promoting operational performance and labor‐force management. The utilization of human resources is closely correlated with operations and production performance. This study investigates the correlation between human resource management (HRM) and business performance of large‐scale manufacturing firms in Taiwan. First, 16 subjects of HRM are designed to survey the importance level and achievement level of HRM by the sample firms. Productivity indices are also defined to measure business performance. Based on the survey, four critical HRM factors including 12 subjects are extracted by factor analysis. The difference between importance level and achievement level of subjects contained in each factor is examined. Furthermore, considering importance and achievement levels of HRM as features, fuzzy clustering analysis is employed to categorize the firms into four patterns. With various HRM characteristics, each pattern has different business performance in terms of productivity. Using a pattern approach, these findings can aid the firms in each pattern to improve their productivity by improving their HRM strategies.

Purpose -To propose a pattern analysis method to help firms rectify weaknesses of production management (PM) and thus promote their business performance. Design/methodology/approach -Total factor productivity and the associated partial productivity indices are defined, and four kinds of production planning ranges, i.e. long-range planning, medium-range planning, short-range planning, and execution, are defined based on 14 PM issues. A fuzzy clustering approach is applied to group the sampled firms into several patterns based on the achievement degrees of production planning in order to investigate the particular characteristics of each pattern. Findings -After analyzing the productivity characteristics of each pattern, the correlation between productivity and production management can be determined. In this study, the business performance seems to be not completely correlated with the achievements of production management, since moderate production planning can provide optimal business performance.Research limitations/implications -The patterns produced from the proposed approaches depend on the sampled data set. A solid sampling method is important to this study. Practical implications -The sampled data are collected from the top 50 large-scale manufacturing firms in Taiwan. The results obtained from this paper may not be consistent with the situations in the other countries. Originality/value -Referring to the findings from each pattern, a firm can further investigate its position in the industry to find ways of increasing its competitiveness.

Consumer opinions are one of the most valuable assets that enterprises have, and thus questionnaires are often employed to investigate the views of consumers. However, this approach requires a large amount of human labor and time, and, most importantly, it cannot automatically find out consumers' needs. However, many consumers now share their appraisals of products or services through electronic word-of-mouth (eWOM). Since these usually reflect consumer needs, and thus their demands, collecting and analyzing eWOM data has become a key task for many businesses. Nonetheless, current eWOM-related research focuses on its transmission, influence, issues, and marketing, and there seem to be very few studies that apply eWOM to develop consumer needs analysis systems. In order to effectively collect and analyze eWOM data, this study proposes a computer-based approach for analyzing consumer demands. The approach utilizes sentiment analysis to develop extraction methods for use with eWOM appraisals. It thus uses eWOM appraisals to find out consumer demands. This work integrates eWOM with information technology to develop an approach to computerize consumer needs analysis. It is expected that the results will help enterprises to improve the quality of their products and market competitiveness.