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The research practice in management research is dominantly based on structural equation modeling (SEM), but almost exclusively, and often misguidedly, on covariance-based SEM. The purpose of this paper is to question the current research myopia in management research, because the paper adumbrates theoretical foundations and guidance for the two SEM streams: covariance-based and variance-based SEM; and improves the conceptual knowledge by comparing the most important procedures and elements in the SEM study, using different theoretical criteria.

The study thoroughly analyzes, reviews and presents two streams using common methodological background. The conceptual framework discusses the two streams by analysis of theory, measurement model specification, sample and goodness-of-fit.

The paper identifies and discusses the use and misuse of covariance-based and variance-based SEM utilizing common topics such as: first, theory (theory background, relation to theory and research orientation); second, measurement model specification (type of latent construct, type of study, reliability measures, etc.); third, sample (sample size and data distribution assumption); and fourth, goodness-of-fit (measurement of the model fit and residual co/variance).

The paper questions the usefulness of Cronbach's α research paradigm and discusses alternatives that are well established in social science, but not well known in the management research community. The author presents short research illustration in which analyzes the four recently published papers using common methodological background. The paper concludes with discussion of some open questions in management research practice that remain under-investigated and unutilized.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

The preponderance of studies that rely on self-report for both independent (e.g. stressors) and dependent (e.g. well-being) variables is often deplored, as it creates problems of common method variance, which may lead to inflated, or even spurious, correlations and predictions. It is sometimes suggested that alternative measures should yield more “objective” information on the phenomena under investigation. We discuss this issue with regard to: (a) observational measures of working conditions; (b) physiological measures of strain; and (c) event-based “self-observation” on a micro-level. We argue that these methods are not necessarily “objective.” Like self-report, they are influenced by a plethora of factors; and measurement artifacts can easily be produced. All this can make their interpretation quite difficult, and the conclusion that lack of convergence with self-report automatically invalidates self-report is not necessarily warranted. Especially with regard to physiological measures, one has to keep in mind that they refer to a different response level that follows its own laws and is only loosely coupled with psychological responses. Therefore, replacement is not a promising way to get more reliable estimates of stressor-strain relationships. We argue instead that each method contains both substantive and error variance, and that a combination of various methods seems more auspicious. After discussing advantages and pitfalls of observational, physiological, and self-observational measures, respectively, we report empirical examples from our own research on each of these methods, which are meant to illustrate both the advantages and the problems associated with them. They strengthen the overall conclusion that there is no “substitute” for self-report (which often is necessary to be able to interpret data from other methods, most notably physiological ones). They also illustrate that collecting such data is quite cumbersome, and that a number of conditions have to be carefully considered before using them, and we report some problems we encountered in this research. Altogether, we conclude that self-report measures, if carefully constructed, are better than their reputation, but that the optimal way is to complement them with other measures.

The purpose of this paper is to demonstrate that common method variance, specifically single-source bias, threatens the validity of a university-created student assessment of instructor instrument, suggesting that decisions made from these assessments are inherently flawed or skewed. Single-source bias leads to generalizations about assessments that might influence the ability of raters to separate multiple behaviors of an instructor.

Exploratory factor analysis, nested confirmatory factor analysis and within-and-between analysis are used to assess a university-developed, proprietary student assessment of instructor instrument to determine whether a hypothesized factor structure is identifiable. The instrument was developed over a three-year period by a university-mandated committee.

Findings suggest that common method variance, specifically single-source bias, resulted in the inability to identify hypothesized constructs statistically. Additional information is needed to identify valid instruments and an effective collection method for assessment.

Institutions are not guaranteed valid or useful instruments even if they invest significant time and resources to produce one. Without accurate instrumentation, there is insufficient information to assess constructs for teaching excellence. More valid measurement criteria can result from using multiple methods, altering collection times and educating students to distinguish multiple traits and behaviors of individual instructors more accurately.

This paper documents the three-year development of a university-wide student assessment of instructor instrument and carries development through to examining the psychometric properties and appropriateness of using this instrument to evaluate instructors.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

Citizens are substantial stakeholders in every e-government system, thus their willingness to use and ability to access the system are critical. Unequal access and information and communication technology usage, which is known as digital divide, however has been identified as one of the major obstacles to the implementation of e-government system. As digital divide inhibits citizen’s acceptance to e-government, it should be overcome despite the lack of deep theoretical understanding on this issue. This research aimed to investigate the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. In order to get a comprehensive understanding of digital divide, this study introduced a new type of digital divide, the innovativeness divide.

The research problems were approached by applying two-stage sequential mixed method research approach comprising of both qualitative and quantitative studies. In the first phase, an initial research model was proposed based on a literature review. Semi-structured interview with 12 users of e-government systems was then conducted to explore and enhance this initial research model. Data collected in this phase were analyzed with a two-stage content analysis approach and the initial model was then amended based on the findings. As a result, a comprehensive research model with 16 hypotheses was proposed for examination in the second phase.

In the second phase, quantitative method was applied. A questionnaire was developed based on findings in the first phase. A pilot study was conducted to refine the questionnaire, which was then distributed in a national survey resulting in 237 useable responses. Data collected in this phase were analyzed using Partial Least Square based Structural Equation Modeling.

The results of quantitative analysis confirmed 13 hypotheses. All direct influences of the variables of digital divide on e-government system success were supported. The mediating effects of trust in e-government in the relationship between capability divide and e-government system success as well as in the relationship between innovativeness divide and e-government system success were supported, but was rejected in the relationship between access divide and e-government system success. Furthermore, the results supported the moderating effects of demographic variables of age, residential place, and education.

This research has both theoretical and practical contributions. The study contributes to the developments of literature on digital divide and e-government by providing a more comprehensive framework, and also to the implementation of e-government by local governments and the improvement of e-government Readiness Index of Indonesia.

The purpose of this paper is to examine empirically the effect of cultural and social acceptance on CEO leadership.

Several instruments were used to capture key concepts (i.e. Organisational Culture Profile, Marlowe‐Crowne Social Desirability Scale, Transformational Leadership Inventory, and Leader Reward and Punishment Questionnaire), which were examined using confirmatory factor analysis. Data were collected from 635 Australian CEOs.

The results of hierarchical multi‐regression analysis clarified the importance of self‐deception and impression management as influential context factors, and how both operate at the pinnacle of organisations. The study also identifies that transformational and transactional leadership behaviours were uniquely influenced by specific cultural dimensions, and suggests that CEOs use combinations of these behaviours to respond to four cultural dimensions (i.e. emphasis on rewards, performance orientation, innovation, and stability) in order to produce competitive advantages.

The study highlights how CEOs are still vulnerable to conforming to the social norms of their organisation and also how CEOs use a repertoire of leadership behaviours, in response to the importance of different cultural dimensions.

The paper contributes to the leadership literature by directly addressing how context impacts on CEO leadership in three specific areas: social acceptance needs, demographics and culture. Further, the study investigates CEO transformational and transactional leadership behaviours rather than global constructs, and directly addresses the common method variance issue.

This study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.

This study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.

Based on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.

To the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

Several research studies on Lean Six Sigma (LSS) have been done using the survey methodology. However, the use of surveys often relies on the measurement of variables, which cannot be directly observed, with attendant measurement errors. The purpose of this study is to develop a methodological framework consisting of a combination of four tools for identifying and assessing measurement error during survey research.

This paper evaluated the viability of the framework through an experimental study on the assessment of project management success in a developing country environment. The research design combined a control group, pretest and post-test measurements with structural equation modeling that enabled the assessment of differences between honest and fake survey responses. This paper tested for common method variance (CMV) using the chi-square test for the difference between unconstrained and fully constrained models.

The CMV results confirmed that there was significant shared variance among the different measures allowing us to distinguish between trait and faking responses and ascertain how much of the observed process measurement is because of measurement system variation as opposed to variation arising from the study’s constructs.

The study was conducted in one country, and hence, the results may not be generalizable.

Measurement error during survey research, if not properly addressed, can lead to incorrect conclusions that can harm theory development. It can also lead to inappropriate recommendations for practicing managers. This study provides findings from a framework developed and assessed in a LSS project environment for identifying faking responses. This paper provides a robust framework consisting of four tools that provide guidelines on distinguishing between fake and trait responses. This tool should be of great value to researchers.