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Resource allocation is an important area of strategic planning in any organization, including higher education. In comparing two case examples of a college's efforts in shared governance in allocating budget to its five departments, the purpose of this paper is to show the importance of a college's awareness and understanding of team behavioral mindsets and contextual factors when practicing continuous improvement each time it applies shared governance.

The authors propose five models that represent five distinct behavioral mindsets. The authors examine the fit of real data based on two case examples, and then identify and compare the behavioral mindsets and possible coalition formed in each case.

The results show the type of behavioral mindset evolves over time, and the contextual factors such as changes in the macro-environment, the composition of decision makers, and their personalities, play an important role in shaping the final outcome. Continuously monitoring and learning, which leads to a more thorough understanding and awareness of the changes in contextual factors, is imperative.

Team decision making involving resource allocation is a critical problem in higher education when applying shared governance. A clear understanding of the type of behavioral mindset exhibited in the process and its causes is critical. The paper proposes a model that can help higher education administrators identify the behavioral mindset. In addition, the authors find support from established theories for the evolution of team behavioral mindsets in a college's budget allocation.

The purpose of this paper is to provide a practical method to be used in team decision making when allocating resources.

The paper proposes the following hypothesis: the selected team consensus strategy from among all available strategies must have minimum sum of squares of monetary regrets. A general algebraic representation of the above hypothesis is developed.

This hypothesis can be interpreted as a Nash equilibrium involving mixed strategies when the entire problem is viewed in game theoretic framework. The paper provides an explanation in quantitative terms of the reasoning process pursued by five business college department chairs faced with three strategies, in an actual consensus decision making to illustrate the above hypothesis. By making observations of the behavior of decision makers in the selection of a budget allocation formula, the paper shows that the hypothesis holds true for the specific reasoning process pursued by the chairs in arriving at the consensus solution. However, the chairs' consensus solution is found to be a local solution vis‐à‐vis the global optimal solution found by solving the game theoretic model.

The authors plan to conduct further empirical testing of the hypothesis using allocation strategies found in diverse decision‐making environments involving diverse decision makers such as business executives, government officers, education administrators, and others.

If this hypothesis can be validated to be true, decision makers should propose for consideration only those rational strategies that have minimal or low variance in monetary regrets since these are the strategies that would most likely be selected in team decision making.

Team decision making involving resource allocation abounds in all organizations, at all levels and in diverse applications. The practical procedure proposed in this paper, based on analytical foundation of game theory, provides decision makers a viable tool for allocating resources that results in consensus of all rational parties involved.

In searching for a new budget formula for the College of Business at California State University at Long Beach, a major university in the west, several rational budget formulas were explored. This report develops an explanation in quantitative terms of the reasoning process pursued by the department chairs in arriving at the compromise budget allocation model that is currently in place in the college. It shows that in a group decision‐making process concerning the allocation of resources, compromises are made between decision‐makers in order to come to some common agreement, if one in fact exists. Rational models based on some formula are introduced, and resources can be allocated based on the formula. However, among the models presented using a decision matrix, the model that will eventually be selected is the one that has the minimal variance in ranking regrets and monetary regrets if the highest‐ranking model is not chosen. The ranking regret provides a good guide and quick identification of the “most‐likely‐to‐succeed” compromise model. However, the monetary regret appears to be the final compromise determinant.

The purpose of this paper is to examine the determinants of the EVA performance evaluation model for the Chinese banking industry. The authors investigate the impact of six bank-specific factors and corporate governance factors on financial performance.

The authors use the ordinary least square regression to examine the determinants of the EVA performance evaluation model for the Chinese banking industry. The findings are generally robust to alternative proxies of performance.

The empirical results indicate that credit risk, operational efficiency and the degree of innovation are positively related to banks’ EVA while capital management has a negative impact on it. In addition, although board size and independent directors are not related to the bank’s EVA, from the perspective of the traditional performance evaluation indicators, executive compensation has a positive impact on the bank’s profitability.

This paper has some limitations. First, due to the large number of adjustments to accounting items are required in the application of EVA when evaluating business performance, some items of the EVA model in this paper have been simplified, which may cause the bank’s EVA value to deviate slightly from the actual situation. Moreover, the sample includes only listed banks, so our results cannot generalize to non-listed banks, such as some small- and medium-sized commercial banks.

This paper contributes to the limited body of literature concerning the use and the determinants of EVA in emerging markets. The authors construct an EVA model which is suitable for China’s banks and reports comprehensive evidence on the drivers of EVA as a measurement tool.

Researchers continue to seek understanding of industrialization as a state managed process. How to create and implement new industries based on advanced knowledge is on the policy agenda of many advanced nations. Measures that promote these developments include national capacity building in science and technology, the formation of technology transfer systems, and the establishment of industrial clusters. What these templates often overlook is an analysis of use. This chapter aims to increase the understanding of the processes that embed new solutions in structures from an industrial network perspective. The chapter describes an empirical study of high-technology industrialization in Taiwan that the researcher conducts to this end. The study shows that the Taiwanese industrial model is oversimplified and omits several important factors in the development of new industries. This study bases its findings on the notions that resource combination occurs in different time and space, the new always builds on existing resource structures, and the users are important as active participants in development processes.

As publishers and academia swiftly head towards e-textbooks, it is important to understand how students feel about using e-textbooks as a primary learning tool. This paper discusses results of a small-scale study looking into how a group of language learners view and use e-textbooks as learning tools in ESL classrooms. The paper concludes by offering teaching implications that could ease integrating e-textbooks in language classrooms in a more effective and efficient manner.

Suggests another dimension of research in, and application of, knowledge management. This theoretical paper adopts a conceptual, multi‐disciplinary approach. First, knowledge can be stored and transmitted via institutions. Second, knowledge “subnetworks” or smaller groupings within larger networks can become key repositories of knowledge. The concept of knowledge “subnetworks” needs to be tested against empirical evidence, which should include a cross‐national comparison of knowledge‐based cities. The paper provides some insights to policy makers in designing or developing global cities. It is one of the few papers that discusses the connection between knowledge management and growth of global cities.

The purpose of this paper is to develop further the concept of increasing returns in technology industries, including social and critical mass factors. The paper applies this framework to the twenty‐first century with the example of the three‐way competition among Nokia, Microsoft and Linux for the global mobile software standards competition.

A multidisciplinary and conceptual methodology was used, integrating theories from economics, marketing, technology, innovation, sociology and psychology.

The study finds that increasing returns frameworks need to combine technology as well as social and psychology effects to reflect the dynamics of global competition in the twenty‐first century.

This paper illustrates how a multidisciplinary and integrated approach to analysing increasing returns and a critical mass framework can provide a richer and more holistic analysis of global competition, including Nokia, Microsoft and Linux, in the global competition for mobile software in the twenty‐first century.

The advent of mobile telephony devices with strong internet capabilities has laid the foundation for mobile commerce (m-commerce) services. The purpose of this paper is to empirically examine predictors of m-commerce adoption using a modification of the widely used technology acceptance model and the unified theory of acceptance and use of technology model.

The data were collected from 213 respondents by means of an online survey. The data were analyzed through multi analytic approach by employing structural equation modeling (SEM) and neural network modeling.

The SEM results showed that variety of services, social influence, perceived usefulness, cost and perceived trust have significant influence on consumer’s intention to adopt m-commerce. The only exception was perceived ease of use which observed statistically insignificant influence on adoption of m-commerce. Furthermore, the results obtained from SEM were employed as input to the neural network model and results showed that perceived usefulness, perceived trust and variety of services as most important predictors in adoption of m-commerce.

The findings of this study give an insight of key determinants that are important to develop suitable strategic framework to enhance the use of m-commerce adoption. In addition, it also provides an opportunity to academicians and researchers to use the framework of this study for further research.

The study is among a very few studies which analyzed m-commerce adoption by applying a linear and non-linear approach. The study offers a multi-analytical model to understand and predict m-commerce adoption in the developing nation like India.