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This research seeks to present a methodology for investigating the generalizability of a theory‐testing model. The methodology is used to examine the generalizability of a model of the antecedents and consequences of customer delight.

Theory testing of models in the marketing often fails to define an intended universe of generalization. This paper shows how multivariate generalizability theory can be used to estimate construct covariance components for specific sources of variance. These components can then be used to assess the generalizability of a structural equation model of a marketing phenomenon.

The parameters of a model of customer delight obtained from data that sample customers of a service or data that confound sources of variance do not generalize to data that capture variation across services or variation across raters. The relative impact of customer delight and satisfaction on behavioral intention varies with the source of variation being studied.

Previous research suggests that after controlling for customer satisfaction, customer delight accounts for very little variation in behavioral intention. But, for the source of variation of most relevance to managers, namely web sites, it is customer delight, not customer satisfaction, that is strongly associated with behavioral intention.

The methodology can be applied and can produce model parameters having substantially different managerial implications for the management of customer satisfaction and customer delight.

Classroom management is a prerequisite for effective instruction, yet research indicates that not all teachers implement evidence-based classroom management skills (CMS) in their classroom. Therefore, efficient professional development models are necessary to increase teachers’ use of CMS, but those models are predicated on valid and reliable screening tools to identify teachers CMS performance. This study is a psychometric evaluation of a direct observation CMS screening tool for elementary school teachers that can be used as part of a targeted CMS professional development model. Based on a three-facet generalizability study, the primary source of variance across observations was differences among teachers and differences across observations. A decision study was conducted and indicates that a generalizable estimate from the CMS screening tool requires four 30-min observations. These results are compared with prior research and recommendations for future research are discussed.

Identifying the dimensionality of a construct and selecting appropriate items for measuring the dimensions are important elements of marketing scale development. Scales for measuring marketing constructs such as service quality, brand equity, and marketing orientation have typically been developed using the influential classical test theory paradigm (Churchill, 1979), or some variant thereof. Users of the paradigm typically assume, albeit implicitly, that items and respondents are the only sources of variance and respondents are the objects of measurement. Yet, marketers need scales for other important managerial purposes, such as benchmarking, tracking, and perceptual mapping, each of which requires a scaling of objects other than respondents such as products, brands, retail stores, websites, firms, advertisements, or social media content. Scales that are developed without such objects in mind might not perform as expected. Finn and Kayande (2005) proposed a multivariate multiple objective random effects methodology (referred to here as M-MORE) could be used to identify construct dimensionality and select appropriate items for multiple objects of measurement. This chapter applies M-MORE to multivariate generalizability theory data collected to assess online retailer websites in the early 2000s to identify the dimensionality of and to select appropriate items for scaling website quality. The results are compared with those produced by traditional methods.

This paper aims to address the reporting of validation and evaluation criteria in qualitative management accounting studies, which is a topic of critical debate in qualitative social science research. The objective of this study is to investigate the ways researchers have reported the use of evaluation criteria in qualitative management accounting studies and whether they are associated with certain paradigmatic affiliations.

Building on the work of Eriksson and Kovalainen [Eriksson and Kovalainen (2008) Qualitative Methods in Business Research. London, Sage], the following three approaches are examined: the adoption of classic concepts of validity and reliability, the use of alternative concepts and the abandonment of general evaluation criteria. Content analysis of 212 case and field studies published during 2006 to February 2015 was conducted to be able to offer an analysis of the most recent frontiers of knowledge.

The key empirical results of this study provide partial support for the theoretical expectations. They specify and refine Eriksson and Kovalainen’s (2008) classification system, first, by identifying a new approach to evaluation and validation and, second, by showing mixed results on the paradigmatic consistency in the use of evaluation criteria.

This paper is not necessarily exhaustive or representative of all the evaluation criteria developed; the authors focused on the explicit reporting of criteria only and the findings cannot be generalized. Somewhat different results might have been obtained if other journals, other fields of research or a longer period were considered.

The findings of this study enhance the knowledge of alternative approaches and criteria to validation and evaluation. The findings can aid both in the evaluation of management accounting research and in the selection of appropriate evaluation approaches and criteria.

This paper presents a synthesis of the literature (Table I) and new empirical findings that are potentially useful for both academic scholars and practitioners.

Long‐term orientation (LTO) is an important cultural value, which has been shown to meaningfully affect the behavior of individuals. Bearden et al. developed and tested a two‐dimensional scale measuring LTO at the individual level. This study aims to replicate and extend the work of Bearden et al. examining the psychometric properties and generalizability of the scale across ten countries of the European Union (EU).

Survey‐based data were collected from 3,491 respondents across ten EU Member States via an internet questionnaire.

The LTO scale is found to possess adequate dimensional properties in the majority of country samples. Discriminant validity between the two LTO dimensions is not evidenced across four country samples. Significant association is found between LTO and individualistic orientation among respondents in nine of the ten countries with few significant associations found between LTO and uncertainty avoidance. Finally, the generalizability of the scale is assessed through Cronbach et al.'s (1963) generalizability theory and found to be satisfactory though discriminant validity is found to be lacking.

Overall, the scale is recommended for use in measuring LTO with caution. Further research is needed to clarify the difference between the two subscales of tradition and planning.

Measuring and better understanding cross‐cultural differences in customers' LTO can be a means to overcoming difficulties in effectively marketing products and services across cultures.

The paper presents an original and first presentation of a cross‐cultural validation of a parsimonious LTO scale.

The purpose of this paper is to examine the influence of innovativeness, change seeking and cognitive effort on consumer responses to traditional versus virtual testing environment.

The empirical study collects concept evaluations of five heterogeneous consumer appliances, from 400 members of an online panel. Generalizability theory (hereafter G theory) is used to assess the psychometric quality of the evaluation data in different testing environments.

The results show that subjects with high innovativeness and change seeking report significantly more favorable concept evaluations and generate better quality data. However, the effect of innovativeness on testing outcomes and data quality would be reduced in virtual testing environment.

The results indicate that using firm or industry norms to interpret the testing outcome will be biased unless it accounts for whether the screening processes result in equally innovative or variety seeking samples of respondents.

Managerially, the current results indicate that a product manager wanting to concept test a pool of appliance concepts can benefit from screening for the respondents, who will provide higher quality concept testing data in a traditional testing environment. However, the effects of traits on data quality are mitigated in a virtual testing environment. The findings provide a surprising insight that subject selection is not a more critical issue in virtual testing.

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.

Restates the case for accounting and management research from a grounded theory perspective, and advocates its informed and more frequent application. Examines the intellectual foundations and key tenets of grounded theory in the context of researchers’ theoretical assumptions and methodological characteristics, discussed in relation to Laughlin’s (1995) classification schema. Pays particular attention to grounded theory assumptions and methods in relation to other interpretive paradigms such as symbolic interactionism, ethnomethodology and hermeneutics. Describes the basic principles and methods of grounded theory research, and presents potential applications to the accounting and management research arenas. Argues that rigorous grounded theory research can offer the accounting and management literatures unique understandings that provide additional perspectives to those already being offered by major schools of thought, and discusses implications of grounded theory for informing contemporary professional practice.

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.

The purpose of this paper is to describe and discuss a few principal and crucial steps of “antecedents” and “postcedents” in relation to structural equation modeling (SEM) in social science research.

This paper is based upon a conceptual and reflective discussion of SEM in social sciences research.

SEM is not science per se, but just a tool that is used to extract meaning from the previous steps of research processes in terms of a research phenomenon in focus.

Based upon the steps of validity and reliability discussed in relation to predetermined measurement and structural models in SEM, application implications are provided.

Like many statistical tools, SEM can be misused to manipulate findings and fit of measurement and structural models. Researchers may become more interested in the tool than in the subject matter.

Using SEM does not elevate research to be science or make the researcher a scientist! An area of concern for further debate is whether the widespread and variable applications of SEM in social science (in particular, marketing) research are all science.