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This study applied the GM(1,1) model of Grey theory to forecast sales of eight sub‐category non‐alcoholic beverages in Taiwan between 2001 and 2003. According to our measured results, the accuracy of the new forecasting model exceeds 95 per cent. The validity of predictions using the new model is clearly high. The model estimates that the total beverages market will grow, but growth rates will vary for individual sub‐categories. In relation to current growth, from 2001 to 2003, tea drinks, carbonated drinks, functional drinks and sports drinks will experience decreased market growth, while bottled water and fruit and vegetable juices will be a high growth market and coffee drinks and other drinks will enjoy improved sales. These results provide a valuable reference for the Taiwanese beverage industry developing marketing plans.

Silicon wafer slicing manufacturing process exhibits several characteristics. They are: (1) the product type is small batch production, (2) saw cutting must be very precise, (3) the process run time is long, and (4) inspecting samples is difficult. Furthermore, the process involves several synchronously occurred multiple quality characteristics, such as thickness (THK), bow and warp, which must be closely monitored and controlled. Synchronously monitoring multiple quality characteristics is more expensive than monitoring a single quality characteristic in the manufacturing process. The sizes of inspected samples in the existing process are difficult to maintain the quality control chart. Grey situation decision method is used to screen the worst quality control chart. Grey situation decision method is used to screen the worst quality characteristic from the synchronously occurred multiple quality characteristics to monitor the process. Finally, a case study is presented to demonstrate the feasibility and effectiveness of proposed decision method. The exponential weighted moving average (EWMA) control chart is used to verify that the process quality is more reliable.

This study describes a novel algorithm for optimizing the quality yield of silicon wafer slicing. 12 inch wafer slicing is the most difficult in terms of semiconductor manufacturing yield. As silicon wafer slicing directly impacts production costs, semiconductor manufacturers are especially concerned with increasing and maintaining the yield, as well as identifying whey yeilds decline. The criteria for establishing the proposed algorithm are derived from a literature review and interviews with a group of experts in semiconductor manufacturing. The modified Delphi method is then adopted to analyze those results. The proposed algorithm also incorporates the analytic hierarchy process (AHP) to determine the weights of evaluation. Additionally, the proposed algorithm can select the evaluation outcomes to identify the worst machine of precision. Finally, results of the exponential weighted moving average (EWMA) control chart demonstrate the feasibility of the proposed AHP‐based algorithm in effectively selecting the evaluation outcomes and evaluating the precision of the worst performing machines. So, through collect data (the quality and quantity) to judge the result by AHP, it is the key to help the engineer can find out the manufacturing process yield quickly effectively.

The purpose of this paper is to identify and evaluate the relationships among the flexibility enablers and to prepare a hierarchy of these enablers to know their influences over each other in global supply chain. The framework suggests that the priority of enablers in supply chain should be determined on the basis of their driving power and dependency.

Various enablers used by researchers and practitioners for flexibility management of global supply chain have been identified. These enablers have been classified as strategic, operational and performance‐based enablers. Interpretive structural modeling (ISM) is used to establish mutual relationships among the flexibility enablers and to prepare a hierarchy‐based model.

It has been observed that some enablers having high‐driving power and low dependency are of strategic importance. These enablers require more attention while other enablers based on operations and performances are dependents of strategic enablers.

The index of enablers based on driving power and dependency provide an insight for supply chain managers to make the entire supply chain highly flexible, that would help them to respond to global uncertainties.

Presentation of enablers in the form of hierarchy using ISM and ranking them into various driving power and dependent categories is a good effort to make flexible global supply chain.

This study develops a model for selecting Internet advertising networks. The proposed model adopts the “analytic hierarchy process” (AHP) to determine the relative weights of evaluative criteria, then ranks the alternatives and selects the optimum Internet advertising network for advertisers. Additionally, a famous Taiwanese food company is used herein as an example of how an Internet advertising network can be selected using this model. The proposed model helps advertisers to effectively select Internet advertising networks, making it highly applicable for academia and commerce.

Knowledge loss impact on the performance of management information system (MIS) departments due to its influence on absorptive capacity. Furthermore, knowledge retention in human resource management and information system application is also included in the research model, in order to examine their effectiveness in mitigating knowledge loss. The paper aims to discuss these issues.

Survey method and hypotheses are tested with the collected data from 191 Taiwanese IS personnel.

The results not only indicate knowledge loss has impacts on the absorptive capacity and performance of MIS departments while information systems could mitigate knowledge loss, but also provide deeper understanding of knowledge loss and potential issues for academics; as for practitioners, this study could serve as a reference to design and adopt knowledge management (KM) mechanisms.

KM is an important issue for organizations since it is the source of competitive advantages and excellent performance, although the investment in KM is not necessary effective in retaining knowledge. This study conducts survey to examine the influences of knowledge loss in MIS departments, since the high turnover rate of employees and the rapid environmental changes in technical world may worsen the situation. According to dynamic capability theory, absorptive capacity is a critical ability to have while facing turbulent environment.

Mobile e-commerce has brought convenience to consumers. But for goods such as shoes, mobile e-commerce has failed to provide the same experience as consumers would have in physical stores, and that also causes problems for online merchants, such as high return rates. As a result, the augmented reality (AR) virtual shoe-try-on function appeared. The way that AR virtual shoe-try-on study different from other AR virtual try-on studies is that AR virtual shoe-try-on study only satisfies consumers' visual experience and consumers cannot judge whether the shoes are comfort or not. Whether consumers would accept AR virtual try-on function to help them make purchase decision due to the visual experience provided by AR virtual try-on function is worth discussion. Measuring users' perceptions and preferences can help companies design AR shoe-trying functions and provide services more cost-effectively.

To promote the continuous use and better development of such mobile e-commerce based on the technology acceptance model (TAM), this study explored the influencing factors for users' intentions to continue using the AR virtual shoe-try-on function, including the perceived usefulness, perceived ease of use, system quality, perceived playfulness and attitude.

The results of this study showed that TAM is a powerful theoretical tool of the new technology in mobile e-commerce and that the system quality and perceived playfulness also have a positive impact on the original variables of TAM. System quality and perceived playfulness are important predictors of users' continuance intentions to use the AR virtual shoe-try-on function.

The main contribution of this study to model iteration and theoretical update is to verify the applicability of the TAM in the AR shoe-try-on function and to expand TAM model with system quality and perceived playfulness. The authors' results will help shoe enterprises win users' recognition through AR shoe-try-on function and improve users' continuance intention of use.

Facing a new round of global industrial restructuring, it is vital for less-developed yet populous regions to build a happy, engaged workforce to achieve competitiveness. The purpose of this paper is to integrate an indigenous cultural perspective, i.e. Muslim religious belief, with the job embeddedness theory to delve into employee well-being-turnover issues in a large developing country ingrained with Muslim culture.

The authors conducted a questionnaire survey on employees from the media sector in the Muslim country, Pakistan. The data were analysed using moderated hierarchical regression models (investigating three-way interactions), while the significance tests of simple slopes and simple slope differences were also used to support the analysis.

The results suggest that all predictors analysed (Muslim religious belief, organisational embeddedness, life satisfaction, and work engagement) were negatively and significantly related to turnover intention. Furthermore, the employee well-being-turnover intention mechanisms are jointly moderated by Muslim religious belief and organisational embeddedness.

The research considers turnover intention as an outcome of a three-way interaction among employee well-being, Muslim religious belief, and organisation embeddedness, thus proposing insightful implications for other developing country enterprises, particularly those ingrained with Muslim culture.

The authors propose a novel model which demonstrates the effects of employee work- and life-related well-being on turnover intention from a unique angle by incorporating Muslim religious belief with organisational embeddedness, contributing to the existing body of knowledge. The applicability of western concepts to immature markets is also examined.

Traditional statistical methods to study the financial performance of any industry have many barriers and limitations in terms of the statistical distribution of the financial ratios, and, in particular, it considers only its positive values of it. The purpose of this paper is to estimate the financial performance of 24 Indian life insurance companies for the period from 2013 to 2016 using Grey relational analysis (GRA) proposed by Deng (1982) that accommodates the negative values in the analysis.

Financial performance of 24 Indian life insurance companies for the years from 2013–2014 to 2015–2016 is examined using a total of 14 indicators from capital adequacy ratios, liquidity ratios, operating ratios and profitability ratios (PR). The methodology used is GRA to obtain the Grey grades to rank the performance indicators, where higher relational grade shows better financial performance, and a lower score depicts the scope for improving the performance.

The results rank the insurance companies according to their financial performance in which Shriram insurance stands first with higher relational grade score, followed by the companies like IDBI Insurance, Sahara Insurance and Life Insurance Corporation of India. The main finding is that PR which have negative values are playing a crucial role in determining the financial performance of Indian life insurance companies.

This study has far-reaching practical implications in twofold: first, for the Indian life insurance industry, they have to concentrate more on PR for better financial health and, second, for any financial performance analysis, ignoring negative value ratios produce biased inference and GRA can be used for better inference.

This study is the first attempt to evaluate the financial performance of Indian life insurance using the GRA methodology. The advantage of GRA is that there is no restrictions on the statistical distribution of the data and it also accommodates the negative values, whereas all the other traditional methods insist on the statistical distribution of data, and, more importantly, they cannot handle negative values in the performance analysis.