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The purpose of this research is to provide further evidence on the association between the IPO signaling mechanisms (i.e. retained ownership, auditor choice, and earnings forecast) by using a less restrictive sample and by performing additional empirical tests.

Single equations are used as the baseline approach to estimate the three models. In addition, Copley and Douthett's 2002 simultaneous equation systems are applied to examine whether the results remain the same. Moreover, ranked values of the risk proxies of IPOs are derived and general least squares are run on these ranked variables.

Findings indicate that auditor reputation and retained ownership are not substitute signals. It is observed that as firm risk increases, entrepreneurs are more likely to retain higher ownership to signal firm value. In addition, contended that positive earnings disclosure before IPO is not associated with retained ownership in a significant manner. An analysis of the economic implication of the results suggests that findings are more representative.

In this study the risk measures used (as well as those used in other studies) may not adequately proxy for offering firm risk. Additionally, the sample is restricted by missing values of the retained ownership variable. Further study can expand the sample using retained ownership obtained from other data sources. A study employing alternative approaches to control for the supply‐side effect of firm risk could be also productive.

Findings are of particular interest to firms that are planning to go to the public. They need to evaluate the benefit and cost of selecting a particular information system in signaling firm value to the market.

Using a larger sample, comprehensive testing periods, and ranked risk proxies contribute to the literature on evaluating singling mechanisms of IPOs.

Importance-performance analysis (IPA) is an effective tool for firms to prioritise service quality attributes, but has limitations in evaluating and enhancing service quality within a competitive environment. The purpose of this paper is to present an evolved model of IPA – importance-performance benchmark vectors (IPBV) – as a benchmarking tool and investigate its applicability in the context of hotel service quality.

Empirical studies based on self-completion survey data from 150 customers of two full-service hotels in Taiwan were conducted in to examine the practical utility of IPBV.

Eight key benchmark typologies were identified and expressed as vectors in the IPBV model which are as follows: “sustainable advantage”, “potential strength”, “false advantage or outstanding advantage”, “cease-fire competition”, “false disadvantage or on-hand disadvantage”, “potential weakness”, “dangerous warning” and “head-on competition”.

The paper extends the methodology to more cases, and other service industries to test further the discriminatory power of the model and to explore the descriptors in the IPBV vector model. Alternative seven-point or nine-point Likert scales could be explored to test the discriminant validity using means. The alternative IPA diagonal approach focussing on GAP analysis may reveal alternative interpretations for the IPBV vector model. Other extended models of IPA, which include competitor analysis, should be compared in practice using a data set where both quantitative and qualitative data could be generated.

The paper proposes the two-dimensional IPBV model which retains the advantages of IPA, but also includes competitor or benchmark comparisons which enable organisations to analyse their relative competitive position. The two-part model provides both quantitative information and qualitative interpretation of relativities. The graphical matrix models provide simple quantitative analysis of attributes, whilst the IPBV vector model provides qualitative interpretations of the eight competitive market positions. Vector analysis enables the development of competitive strategies relative to benchmarks, or within a competitive set. Importance is retained and means that organisations can benchmark against a range of competitors prioritising specific attributes for resource allocation.

The interpretive utility of the model should be explored with practitioners and decision makers in the service industries. The model has been designed for practical use in industry to inform operational and strategic decision making, its usefulness in practice should be explored and the attitudes of practitioners to the model should be tested.

Traditional approaches to benchmarking have adopted a one-dimensional approach that does not include a measure of the relative importance of the service quality dimensions in specific markets. This research develops a two-dimensional advanced model of IPA, called IPBV, which is based on vector relationships between key attributes of service quality. These vectors are explored and described in competitive terms and the model is discussed with regard to its implications for industry, practitioners and researchers.

The paper investigates the suitability of the Rasch model for establishing data equivalence. The results based on a real data set are contrasted with findings from standard procedures based on CFA methods.

Sinkovics et al.'s data on technophobia was used and re‐evaluated using both classical test theory (CTT) (multiple‐group structural equations modelling) and Rasch measurement theory.

Data equivalence in particular and measurement in general cannot be addressed without reference to theory. While both procedures can be considered best practice approaches within their respective theoretical foundation of measurement, the Rasch model provides some theoretical virtues. Measurement derived from data that fit the Rasch model seems to be approximated by classical procedures reasonably well. However, the reverse is not necessarily true.

The more widespread application of Rasch models would lead to a stronger justification of measurement, in particular, in cross‐cultural studies but also whenever measures of individual respondents are of interest.

Measurement models outside the framework of CTT are still scarce exceptions in marketing research.

This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors.

The study used a questionnaire to obtain basic information relating to identified project scope factors as well as information relating to the impact of the non-scope factors on the duration of building renovation projects. The study retrieved 121 completed questionnaires from construction firms on tertiary education trust fund (TETFund) building renovation projects. Artificial neural network was then used to develop the model using 90% of the data, while mean absolute percentage error was used to validate the model using the remaining 10% of the data.

Two artificial neural network models were developed – a multilayer perceptron (MLP) and a radial basis function (RBF) model. The accuracy of the models was 86% and 80%, respectively. The developed models’ predictions were not statistically different from those of actual duration estimates with less than 20% error margin. Also, the study found that MLP models are more accurate than RBF models.

The developed models are only applicable to projects that suit the characteristics and nature of the data used to develop the models. Hence, models can only predict the duration of building renovation projects.

The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it.

The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it; it also helps clients to effectively benchmark projects duration and contractors to accurately estimate duration at tendering stage.

The study presents models that combine both scope and non-scope factors in predicting the duration of building renovation projects so as to ensure more realistic predictions.

Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, helping to interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.

The purpose of this study is to empirically examine the factors influencing consumer behavioral intention (BI) to use cryptocurrency as a medium of transaction. Constructs from the unified theory of acceptance and use of technology model and an added variable, perceived risk (PR), are examined to predict BI. Age and gender as moderators are retained in this model.

An online survey was used to gather the respondents’ responses on a five-point Likert scale. G * Power was used to calculate the required minimum sample size. A non-probability sampling technique was used to gather data from the 290 respondents based in Malaysia. The final data set was analyzed using the statistical package for the social sciences and SmartPLS software using structural equation modeling.

The results show that three of the five proposed factors (performance expectancy, effort expectancy and facilitating condition) are significant predictors of BI to adopt cryptocurrency as a medium of transaction. Interestingly, PR is not a significant predictor even though prior research studies showed otherwise. Likewise, the relationship between BI and social influence became significant only when age is added as a moderator.

Malaysians are still wary of cryptocurrency, even though global tech firms such as Amazon and Microsoft are already accepting Bitcoin as a payment method. This study aims to provide relevant authorities and businesses (i.e. central bank, retail merchants and cryptocurrency exchangers) insights toward understanding the factors consumers focus on if they were to use cryptocurrency as a medium of transaction.

Most cryptocurrency research are done in developed countries (i.e. USA, UK and EU) perspective. This research addresses the lack of quantitative literature on significant factors influencing BI to use cryptocurrency in developing country context while taking a PR, age and gender into consideration.

This study aims to assess health workers’ level of emotional intelligence (EI) in Accra North and recommend a simple but robust statistical technique for compulsorily validating EI measurement scales.

The researchers used a self-reported questionnaire to collect data from 1,049 randomly selected health workers. Two non-nested models, BNK MODEL and CMODEL, were compared to see which of them better fits the study population and yields a better level of EI. The one-sample and independent-samples t-tests, exploratory factor analysis and confirmatory factor analysis were used to present results.

The study found that health workers were appreciably emotionally intelligent for both models at the 5 per cent significance level. However, EI was higher for the CMODEL. The CMODEL also better fits the study population (χ² = 132.2, p = 0.487, Akaike information criterion = 124.932) and thus better underlies EI in it. This study recommends proper validation of the two EI scales evaluated in this study, and possibly other scales, before the use of their data in research, as failure to do so could lead to unrealistic results.

Apart from its contribution to the literature, this study provides a robust statistical approach for assessing health workers’ EI and validating EI scales. By comparing two models of EI in the validation process, this paper suggests that the researcher’s choice of a measurement scale can influence his/her results.

The paper seeks to explore the role of the employer brand in influencing employees' perceived differentiation, affinity, satisfaction and loyalty – four outcomes chosen as relevant to the employer brand.

A multidimensional measure of corporate brand personality is used to measure employer brand associations in a survey of 854 commercial managers working in 17 organisations. Structural equation modelling is used to identify which dimensions influence the four outcomes. Models are built and tested using a calibration sample and tested on two validation samples, one equivalent to the calibration sample and another drawn from a single company.

Satisfaction was predicted by agreeableness (supportive, trustworthy); affinity by a combination of agreeableness and (surprisingly) ruthlessness (aggressive, controlling); and perceived differentiation and loyalty by a combination of both enterprise (exciting, daring) and chic (stylish, prestigious). Competence (reliable, leading) was not retained in any model.

Further work is required to identify how appropriate improvements in employee associations can be managed.

The findings emphasise the importance of an employer brand but the results also highlight the complexity in its management, as no one aspect has a dominant influence on outcomes relevant to the employer. At issue is which function within an organisation should be tasked with managing the employer brand.

Employer branding is relatively new as a topic but is attracting the attention of both marketing and HR academics and practitioners. Prior work is predominantly conceptual and this paper is novel in demonstrating empirically its role in promoting satisfaction, affinity, differentiation and loyalty.

This study sets out to examine conflict style preferences in India and the predictive effects of various demographic variables on conflict style preference.

Data were gathered in India (n=827) among Muslims and Hindus. Conflict was measured using Oetzel's Conflict Style Measure. To answer the research questions, repeated measures ANOVA and multiple regressions were conducted.

The findings reveal that conflict style preference among Hindus in India differs significantly and that Hindus prefer the integrating and dominating styles, whilst showing the least inclination towards the avoiding and obliging styles. Muslims prefer the integrating and compromising styles and least prefer the dominating and avoiding styles. Analyses of the demographic variables' predictive influence reveal that age and sex are significant predictors for all five conflict styles for both Muslims and Hindus. Education has a mixed predictive influence on conflict style among Hindus and Muslims.

Use of self‐report instruments and the majority of the participants coming from middle‐class backgrounds could limit the generalizability of the study.

The study calls for conflict mediators to consider the influence of group membership and educational level on conflict management/resolution. The research also discusses national/international conflict intervention.

This paper informs individuals regarding the way in which two large religious and cultural groups differ in their approaches to conflict. For a nation that has experienced a history of conflict between these two cultural/religious groups, this paper can help shed light on how to bridge the conflict between them.

There are no studies directly comparing the conflict styles of Muslims and Hindus.

This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.

This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.

This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.

This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.