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This study seeks to develop a framework of analysis that categorizes features of e‐government web site design in a matrix of knowledge‐acquiring process versus learners' values. The proposed framework supports a development plan for a cyber governmental web site that may involve all aspects of a learning process.

A framework for analysis is first proposed. Data were obtained from 137 part‐time students during a class. Participants were requested to enter the Bureau of Foreign Trade (BOFT) web site to learn a subject of their choice in a computer lab. The instruments used for obtaining their opinions were open‐ended questionnaires. Two focus group interviews were also conducted. An iterative analyzing strategy based on grounded theory was employed to obtain the results.

Analytical results of the data based on the framework reveals many insights. Participants indicated that navigational aids, knowledge contents and other designs related to interactive knowledge sharing are important. Nevertheless, e‐government webmasters often ignore these important features. The proposed framework is a valuable tool for web site development and maintenance.

Although many theories about the usability of e‐government web sites have been proposed, the needs of individual learners have seldom been addressed. Since learning is critical to economic prosperity and social cohesion, the factors that make an e‐government web site supportive for learners should be explored. This study is, to our knowledge, the first to examine an e‐government web site from a learner perspective. Furthermore, this investigation presents a framework that facilitates analyzing and improving e‐government web sites.

The purpose of this paper is to propose and verify that the technology acceptance model (TAM) can be employed to explain and predict the acceptance of mobile learning (M‐learning); an activity in which users access learning material with their mobile devices. The study identifies two factors that account for individual differences, i.e. perceived enjoyment (PE) and perceived mobility value (PMV), to enhance the explanatory power of the model.

An online survey was conducted to collect data. A total of 313 undergraduate and graduate students in two Taiwan universities answered the questionnaire. Most of the constructs in the model were measured using existing scales, while some measurement items were created specifically for this research. Structural equation modeling was employed to examine the fit of the data with the model by using the LISREL software.

The results of the data analysis shows that the data fit the extended TAM model well. Consumers hold positive attitudes for M‐learning, viewing M‐learning as an efficient tool. Specifically, the results show that individual differences have a great impact on user acceptance and that the perceived enjoyment and perceived mobility can predict user intentions of using M‐learning.

There is scant research available in the literature on user acceptance of M‐learning from a customer's perspective. The present research shows that TAM can predict user acceptance of this new technology. Perceived enjoyment and perceived mobility value are antecedents of user acceptance. The model enhances our understanding of consumer motivation of using M‐learning. This understanding can aid our efforts when promoting M‐learning.

Marketing the products of other companies within an individual company intranet can benefit both employees and the company itself. This study examines the dimensions of satisfaction with a business‐to‐employees (B2E) benefit system and the linkages between satisfaction and organizational citizenship behavior (OCB). Structural equation modeling of data shows that convenience, delivery, interface, accuracy, price and security are factors that affect employee satisfaction with the B2E benefit system. Price is more important to the satisfaction of males than females. Significant, positive relationships were found between satisfaction and OCB. Perceived organizational support plays a stronger mediating role in leading to OCB for females than it does for males.

Gray market activities have become global, occurring not only in less developed or volatile markets, but also in many well‐developed markets. Although the gray market problem has been discussed in the literature, pertinent research from a demand perspective remains scarce. This study establishes a valid measure of consumer attitude toward gray market goods and investigates the relationships between consumer attitude toward gray market goods and their antecedents. Data analysis reveals that both price‐quality inference and risk averseness significantly and negatively affect consumer attitude toward gray market goods. Strategies for managers of international brands to address gray market problems are presented.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Shrewd companies have always acknowledged the value of teamwork. And in today's fiercely competitive business world, a willingness to collaborate has become even more important. Traditional notions of teamwork involve a group of people working together in the same space, sharing the same language, values and backgrounds. Thanks to technology, it does not always have to be that way now. Virtual collaboration has become all the rage and an increasing number of organizations are allocating projects to teams staffed with geographically‐dispersed members who speak, think and act differently from each other. A sure‐fire recipe for chaos and disaster, then? Not necessarily. Indeed, those able to overcome the many barriers inherent to this way of working have produced some pretty impressive results that conventional teams would struggle to match.

Shrewd companies have always acknowledged the value of teamwork. And in today's fiercely competitive business world, a willingness to collaborate has become even more important

The paper provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

Looks at consumer research in Greater China including Mainland China, Hong Kong and Taiwan. Maps out the contributions within this area and guides future research. Examines the state of the art over the 1979‐97 period, with particular emphasis on the topics that have been researched, the extent of the theory development in the field and the methodologies used in conducting research. Uses content analysis to review 75 relevant articles. Suggests that, while a considerable breadth of topics have been researched, there remains much to be done, there is further room for theoretical development in Chinese consumer behaviour studies; and the methodologies used need improvement and further refinement.

The purpose of this paper is to empirically test a research model that incorporated antecedents of praise feedback behaviour (fear of confrontation and incentive for reducing nuisance costs), praise feedback behaviour (deliberatively praise feedback, casual praise feedback, and true compliment feedback) and consequences (trust and repurchase intention).

A structural equation model was employed to test the relationships of the research model using survey data collected from 398 Taobao consumers.

The results showed that fear of confrontation and incentive for reducing nuisance costs had a significant positive influence on deliberatively praise feedback and true compliment feedback, respectively, and both antecedents had a significant positive influence on casual praise feedback of consumers. It also showed that trust was influenced negatively by deliberatively praise feedback, and positively by casual praise feedback and true compliment feedback. Meanwhile, deliberatively praise feedback and true compliment feedback were found to have negative and positive influences on repurchase intention, respectively.

This research was a pilot study to identify a three-dimension conceptualization of praise feedback behaviour from the perspective of customer satisfaction, and to understand positive review bias from the perspective of input processes.

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.