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This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner.

Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software.

The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed.

The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages.

This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

This paper aims to provide insights into Arabic-Australian community attitudes regarding social innovation of a new shared model of accommodation for the 65+ age group to facilitate independent behavior within a shared living environment.

A survey of 520 people of whom 65 per cent were Arabic speakers either by mother or second language. Survey responses were filtered to Arabic speakers and further analyzed to identify groups characterized by the latent attitudes underlying responses.

The results confirmed the presence of two small groups representing in aggregate 13 per cent of sample variance who have positive attitudes toward 65+ age group shared accommodation for either themselves or their parents. These respondents focused on companionship and cultural factors rather than potential financial or medical benefits from the new model.

The application of an empirical Bayes methodology to the limited data in this research implicitly restricts the interpretation of the results to the Australian-Arabic community that was investigated.

The results of this research provide a sound basis for private sector interest in exploring differentiated architectures and business models that will facilitate choices of shared accommodation by the Australian-Arabic 65+ year age group.

This finding aligns with increasing health and mobility more widely among the rapidly growing 65+ year old segment of the Australian population and with recent Australian Government restructuring of age care to introduce greater personal accountability for self-care.

This research is original and important in setting future directions for expanding the richness of choice in Australian-Arabic community retirement living.

Practice demonstrates and research validates that entrepreneurship is moving from the individual to the organization and from the private sector to the social and not‐for‐profit sectors. The present study endeavors to complement the emerging public entrepreneurship literature by aiming to identify which of those factors that stimulate corporate entrepreneurship in the public sector are preferred by entrepreneurial civil servants.

A questionnaire was completed by a random sample (n=247) of public servants across 15 Greek prefectures. Results are based on Bayesian factor analysis, conjoint analysis and cluster analysis.

Results provide preliminary evidence about entrepreneurial civil servants' preferences and make available a well‐documented framework for addressing corporate entrepreneurship in the public sector.

The reported research relied on self‐reporting. In addition, because the sample consisted entirely of public servants across Greek prefectures, findings may not be applicable to other public sector contexts (such as hospitals) and other countries. Data are cross‐sectional and alternative relationships may exist. Future research should be multinational and longitudinal to test the assumptions of the present study.

The results of the study are useful both to academics and policy makers interested in formulating a strategy that fosters corporate entrepreneurship in the public sector.

During the last 15 years, considerable effort has been devoted to developing more effective, more efficient, and more flexible public organizations. Using qualitative methods, the results of the present exploratory research identify which factors that foster corporate entrepreneurship in the public sector are preferred by entrepreneurial public servants.

Although previous research acknowledges that the process of entrepreneurship is also a regional and a peripheral activity, empirical evidence concerning the personal and contextual factors affecting business start‐ups due to the identification of opportunities in rural contexts is limited. The purpose of the present research is to verify whether prior entrepreneurship theories conducted in urban contexts are useful for predicting entrepreneurial activities in rural contexts.

A short, self‐report questionnaire was administered to a random sample of 81 business owners located at a small town in southern Crete, Greece. Bayesian confirmatory factor analysis and logistic regression analysis were the main analytical tools used.

Results suggest that entrepreneurs' personality, prior knowledge, expectation of future social status, and level of education are significant predictors of opportunity entrepreneurship.

The reported research relied on self‐reports and on a sample from the Greek public sector. Moreover, data are cross‐sectional. Future research should be multinational and longitudinal to test the assumptions of the present study.

The present study provides evidence about the utility of existing opportunity entrepreneurship theories in rural contexts. Results could be of value to policy makers focusing on the development of small businesses and entrepreneurship and the promotion of entrepreneurial and innovative capabilities in rural contexts.

This paper aims to respond to John Rossiter's call for a “Marketing measurement revolution” in the current issue of EJM, as well as providing broader comment on Rossiter's C‐OAR‐SE framework, and measurement practice in marketing in general.

The paper is purely theoretical, based on interpretation of measurement theory.

The authors find that much of Rossiter's diagnosis of the problems facing measurement practice in marketing and social science is highly relevant. However, the authors find themselves opposed to the revolution advocated by Rossiter.

The paper presents a comment based on interpretation of measurement theory and observation of practices in marketing and social science. As such, the interpretation is itself open to disagreement.

There are implications for those outside academia who wish to use measures derived from academic work as well as to derive their own measures of key marketing and other social variables.

This paper is one of the few to explicitly respond to the C‐OAR‐SE framework proposed by Rossiter, and presents a number of points critical to good measurement theory and practice, which appear to remain underdeveloped in marketing and social science.

The purpose of this paper is to examine the relationships of service marketing mix (SMM) as service input and service output in terms of students’ performance, satisfaction and referral act in context to higher and technical education (HTE) through the application of structural equation modeling.

A quantitative research, conducted through a self-administered survey composed by a closed-ended structured questionnaire, was incorporated for the students who were enrolled in the technical educational institutions situated in the Khandesh region of India.

The findings of this study revealed that traditional SMM is statistically linked with the performance of students in terms of skill and knowledge enhancement, satisfaction and referral act of students, which are perceptible new emerging SMM; performance, pleasure and pointing out in terms of service output.

Integrating SMM as service input and service output are productive for HTE in enhancing growth (quantitatively) by the inclusivity of diversified students and development (qualitatively) by enhancing their performance for global standing, making them satisfied and motivating them for recommending their institution to others. This integration can be utilized as a yardstick by the institutions for staying ahead in students’ market with a distinctive competitive advantage.

Growth and development of HTE will raise a society’s quality of life and thereby increase a country’s socio-economic status.

The study has exhibited SMM as input and output of a service system that is useful for the growth and development of HTE. The measurement tool presented is effective in (re)framing policies on SMM as service input based on desired service output.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

The impact of volatility crush can be devastating to an option buyer and results in a substantial capital loss, even with a directionally correct strategy. As a result, most volatility plays are for option sellers, but the profit they can achieve is limited and the sellers carry unlimited risk. This paper aims to demonstrate the dynamics of implied volatility (IV) as being influenced by effects of persistence, leverage, market sentiment and liquidity. From the exploratory factor analysis (EFA), they extract four constructs and the results from the confirmatory factor analysis (CFA) indicated a good model fit for the constructs.

This section describes the methodology used for conducting the study. This includes the study area, study approach, sources of data, sampling technique and the method of data analysis.

Although there is extensive literature on methods for estimating IV dynamics during earnings announcement, few researchers have looked at the impact of expected market maker move, IV differential and IV Rank on the IV path after the earnings announcement. One reason for this research gap is because of the recent introduction of weekly options for equities by the Chicago Board of Options Exchange (CBOE) back in late 2010. Even then, the CBOE only released weekly options four individual equities – Bank of America (BAC.N), Apple (AAPL.O), Citigroup (C.N) and US-listed shares of BP (BP.L) (BP.N). The introduction of weekly options provided more trading flexibility and precision timing from shorter durations. This automatically expanded expiration choices, which in turned offered greater access and flexibility from the perspective of trading volatility during earnings announcement. This study has demonstrated the impact of including market sentiment and liquidity into the forecasting model for IV during earnings. This understanding in turn helps traders to formulate strategies that can circumvent the undefined risk associated with trading options strategies such as writing strangles.

The first limitation of the study is that the firms included in the study are relatively large, and the results of the study can therefore not be generalized to medium sized and small firms. The second limitation lies in the current sample size, which in many cases was not enough to be able to draw reliable conclusions on. Scaling the sample size up is only a function of time and effort. This is easily overcome and should not be a limitation in the future. The third limitation concerns the measurement of the variables. Under the assumption of a normal distribution of returns (i.e. stock prices follow a random walk process), which means that the distribution of returns is symmetrical, one can estimate the probabilities of potential gains or losses associated with each amount. This means the standard deviation of securities returns, which is called historical volatility and is usually calculated as a moving average, can be used as a risk indicator. The prices used for the calculations are usually the closing prices, but Parkinson (1980) suggests that the day’s high and low prices would provide a better estimate of real volatility. One can also refine the analysis with high-frequency data. Such data enable the avoidance of the bias stemming from the use of closing (or opening) prices, but they have only been available for a relatively short time. The length of the observation period is another topic that is still under debate. There are no criteria that enable one to conclude that volatility calculated in relation to mean returns over 20 trading days (or one month) and then annualized is any more or less representative than volatility calculated over 130 trading days (or six months) and then annualized, or even than volatility measured directly over 260 trading days (one year). Nonetheless, the guidelines adopted in this study represent the best practices of researchers thus far.

This study has indicated that an earnings announcement can provide a volatility mispricing opportunity to allow an investor to profit from a sudden, sharp drop in IV. More specifically, the methodology developed by Tan and Bing is now well supported both empirically and theoretically in terms of qualifying opportunities that can be profitable because of the volatility crush. Conventionally, the option strategy of shorting strangles carries unlimited theoretical risk; however, the methodology has demonstrated that this risk can be substantially reduced if followed judiciously. This profitable strategy relies on a set of qualifying parameters including liquidity, premium collection, volatility differential, expected market move and market sentiment. Building upon this framework, the understanding of the effects of persistence and leverage resulted in further reducing the risk associated with trading options during earnings announcements. As a guideline, the sentiment and liquidity variables help to qualify a trade and the effects of persistence and leverage help to close the qualified trade.

The authors find a positive association between the effects of market sentiment, liquidity, persistence and leverage in the dynamics of IV during earnings announcement. These findings substantiate further the four factors that influence IV dynamics during earnings announcement and conclude that just looking at persistence and leverage alone will not generate profitable trading opportunities.

The impact of volatility crush can be devastating to the option buyer with substantial capital loss, even for a directionally correct strategy. As a result, most volatility plays are for option sellers; however, the profit is limited and the sellers carry unlimited risk. The authors demonstrate the dynamics of IV as being influenced by effects of persistence, leverage, market sentiment and liquidity. From the EFA, they extracted four constructs and the results from the CFA indicated a good model fit for the constructs. Using EFA, CFA and Bayesian analysis, how this model can help investors formulate the right strategy to achieve the best risk/reward mix is demonstrated. Using Bayesian estimation and IV differential to proxy for differences of opinion about term structures in option pricing, the authors find a positive association among the effects of market sentiment, liquidity, persistence and leverage in the dynamics of IV during earnings announcement.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Empirical data on the impacts of entrepreneurial effectuation on firm performance are scattered and controversial. This study conducts a meta-analysis to obtain evidence on whether and under what conditions effectuation is valuable. A contextual framework is proposed that highlights the boundary condition of the performance impact of effectuation.

Both the traditional approach of meta-analysis and advanced techniques of Bayesian meta-analytic tests are used to combine numerous studies from varied research contexts.

Meta-analytic results show that effectuation generally has a positive impact on firm performance, and is context-dependent, leading to stronger performance for older firms and firms in high-tech industries and emerging countries.

The findings provide important implications for entrepreneurs who are considering applying effectuation. This study highlights that effectuation is context-dependent and the performance implications of effectuation are contingent on contextual factors at organizational, industrial and institutional levels. This study extends the contextual understanding of the effectuation–performance relationship.