<p>The purpose of this study was to explore graduate students’ competencies in computer use and their attitudes toward online learning in asynchronous online courses of distance learning programs in a Graduate School of Education (GSOE) in Taiwan. The research examined the relationship between computer literacy and the online learning attitudes of these students. Data were collected via a survey through 252 GSOE students in Taiwan. Results revealed a significant positive relationship between computer literacy and online learning attitude among the students. Higher computer literacy was correlated with higher online learning attitude. However, no statistically significant difference was found in online learning attitude by gender or by age group. Suggestions and managerial implications were discussed in the study, and would provide contribution both to the body of knowledge in the filed of education management.</p>
The purpose of this study is to explore the associations that potentially impact time perception of waiting customers. Using the constructs of servicescape, motivation, and conformity, the current study tries to figure out the definite causal relationship among variables. Survey questionnaire was administrated to collect data from 335 customers in Taiwanese food and restaurant industry. The results show that waiting motivation has significantly direct effects on servicescape, conformity, time perception, and behavioral intentions. Furthermore, servicescape has a significantly direct effect on behavioral intentions, and conformity has a significantly direct effect on time perception. The findings indicate customers' waiting motivation should be key factor to affect the full structural model, specifically reference group influence, such as word-of-mouth from friends and media coverage. Suggestions and managerial implications are discussed in the study, and would provide contribution both to the body of knowledge in the filed of marketing and mangers to improve quality of consumer relationship.
Studies on the customer order scheduling problem have been attracting increasing attention. Most current approaches consider that either component processing times for customer orders on each machine are constant or all customer orders are available at the outset of production planning. However, these assumptions do not hold in real-world applications. Uncertainty may be caused by multiple issues including a machine breakdown, the working environment changing, and workers’ instability. On the basis of these factors, we introduced a parallel-machine customer order scheduling problem with two scenario-dependent component processing times, due dates, and ready times. The objective was to identify an appropriate and robust schedule for minimizing the maximum of the sum of weighted numbers of tardy orders among the considered scenarios. To solve this difficult problem, we derived a few dominant properties and a lower bound for determining an optimal solution. Subsequently, we considered three variants of Moore’s algorithm, a genetic algorithm, and a genetic-algorithm-based hyper-heuristic that incorporated the proposed seven low-level heuristics to solve this problem. Finally, the performances of all proposed algorithms were evaluated.
Among the well-known scheduling problems, the customer order scheduling problem (COSP) has always been of great importance in manufacturing. To reflect the reality of COSPs as much as possible, this study considers that jobs from different orders are classified in various classes. This paper addresses a tri-criteria single-machine scheduling model with multiple job classes and customer orders on which the measurement minimizes a linear combination of the sum of the ranges of all orders, the tardiness of all orders, and the total completion times of all jobs. Due to the NP-hard complexity of the problem, a lower bound and a property are developed and utilized in a branch-and-bound for solving an exact solution. Afterward, four heuristics with three local improved searching methods each and a water wave optimality algorithm with four variants of wavelengths are proposed. The tested outputs report the performances of the proposed methods.
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