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.

Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.

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.

Background: The popularity of the internet aggravated by its excessive and uncontrolled use has resulted in psychological impairment or addiction. Internet addiction is hypothesized as an impulse-control disorder of internet use having detrimental impacts on daily life functions, family relationships, and emotional stability. The goal of this review is to provide an exhaustive overview of the empirical evidence on internet addiction and draw attention to future research themes. Methods: We performed a literature search on ScienceDirect and PubMed to review original research articles with empirical evidence published on peer-reviewed international journals from 2010 to 2019. Eight hundred and 26 articles were eligible for analysis. Frequency and descriptive statistics were calculated by Microsoft Excel. Results: A substantial contribution has been coming from researchers from China, Turkey, Korea, Germany, and Taiwan respectively. Despite controversies regarding its definition and diagnostic procedures, internet addiction has become the focal point of a myriad of studies that investigated this particular phenomenon from different exposures. Given observed literature review data regarding research design, data acquisition, and data analysis strategies, we proposed the 3C paradigm which emphasizes the necessity of research incorporating cross-disciplinary investigation conducted on cross-cultural settings with conscientious cross-validation considerations to gain a better comprehension of internet addiction. Conclusions: The findings of the present literature review will serve both academics and practitioners to develop new solutions for better characterize internet addiction.

Purpose: This study aims to develop an assessment scale for university service quality (university SQ) and examine University Service Quality assessment model (UNIQUAL) of higher education during the pandemic.Design/methodology/approach: Two studies applied a mixed-method design to develop and validate the UNIQUAL scale. In-depth interviews and literature reviews were conducted to refine the initial dimensions and items of UNIQUAL in Study 1. Item analysis, EFA and CFA were then conducted to purify item refinement, scale refinement, purification and validation in Study 2. Finally, a confirmed UNIQUAL model was analyzed via partial least squares structural equation modeling (PLS-SEM) using Smart-PLS 4.0.Findings: The research confirms the four-factor structure of UNIQUAL, with a total of 16 items, to be a valid and reliable scale for the assessment of the service quality (SQ) of universities. Having adopted the bias-corrected and accelerated (BCa) bootstrap approach to study 5,000 subsamples from 27 countries, the authors found “responsiveness” and “empathy” to be significantly associated and have positive relationships with students' satisfaction with university SQ. Furthermore, university SQ and satisfaction were mediated by “health and safety” concerns.Practical implications: The newly developed UNIQUAL scale would be of value to educators and authorities of higher education to assess the SQ of their universities to enhance the effectiveness of student learning. The improvement in satisfaction with higher education's SQ ultimately helps in retaining both international and local students amidst concerns about traveling and studying during the pandemic.Social implications: COVID-19 has affected the private and public sectors worldwide. Millions of students have been affected by schools being shut down and substituted with distance-learning programs. Thus, the assessment of the quality of university services has become an important support mechanism for retaining the sustainability of higher education.Originality/value: The UNIQUAL scale provides a conceptual model and validates an assessment tool. The research hypotheses confirm the relationship between university SQ and satisfaction from the perspective of international students.