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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.

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.

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.

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.

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.

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.

Since a firm’s management performance can be evaluated in terms of financial ratios, efficient management using financial factors is proposed as the key element for upgrading a firm’s productivity. Investigates productivity in terms of certain financial factors of large‐scale manufacturing firms in Taiwan. First determines several influential financial factors using factor analysis. Based on these factors, employs fuzzy clustering approaches to categorize the manufacturing firms into several patterns with distinct characteristics of financial factors. Using the characteristics of productivity and financial factors for each pattern, makes two kinds of analysis, and proposes some suggestions to improve the firms’ productivity.

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.

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.

Proposes an approach to improving production management (PM) of a firm through examining its resource utilization and product competence. To understand the status of PM in Taiwan, evaluates the achievement levels of 14 production management subjects from 50 large‐scale manufacturing firms. The subjects are further classified into four planning ranges of PM. Then productivity measures of these firms for resource utilization and product competence are determined. Two groups of patterns with distinct PM characteristics are found through a fuzzy clustering analysis based on resource utilization and product competence. Significant correlation has been found between resource utilization and product competence and PM. The PM patterns with good performance in terms of resource utilization and product competence are considered targets for firms of other patterns. The ways of adjusting PM for these firms of other patterns are then described in terms of the characteristics of the target patterns.

By the end of this chapter, you should be able to demonstrate an understanding of:

The importance of big data in the information revolution.

The resource-based view of the firm and dynamic capabilities as they relate to big data.

The use of big data in marketing decisions.

Consumer security concerns over the storage and processing of big data.