Accounting for Uncertainty in the Measurement of Unobservable Marketing Phenomena

Rigdon, Edward E.; Sarstedt, Marko

doi:10.1108/s1548-643520220000019003

Cited by 15 publications

(10 citation statements)

References 51 publications

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“…Specifically, we advise researchers to thoroughly select, or develop, their CE scales for optimal theoretical rigor (Suddaby, 2010). That is, in addition to testing the instrument's reliability and validity (e.g., through confirmatory factor analysis or confirmatory composite analysis; Bagozzi et al, 1991; Hair et al, 2020; Schuberth et al, 2018), we also urge researchers to verify its theoretical underpinnings and hallmarks (MacKenzie, 2003), particularly in terms of the proposed concept's definitional clarity, which is crucial in reducing psychological measurement‐based variability and promoting the study's replicability (Rigdon & Sarstedt, 2022; Rigdon et al, 2020).…”

Section: Discussion Implications and Limitationsmentioning

confidence: 99%

Hallmarks and potential pitfalls of customer‐ and consumer engagement scales: A systematic review

Hollebeek

Sarstedt

Menidjel

et al. 2023

Psychology and Marketing

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Multiple scales measuring a customer's, or consumer's, engagement (CE) with a brand or specific brand elements (e.g., advertising/social media content) have been proposed in the literature, offering researchers different options to gauge CE. However, the myriad proposed operationalizations can yield confusion among scholars regarding how to best capture CE, exposing a growing issue for CE research. Addressing this issue, we take stock of major scales measuring a CE with a brand or specific brand elements. To achieve this objective, we performed a systematic review of major CE scale development articles (2005 to January 2023) using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach. We systematically evaluated these scales in terms of their respective CE conceptualization, dimensionality, itemization, and underlying theoretical perspective. We also identify potential scale-related risks, or pitfalls, exposing important insight for CE researchers.Overall, the results suggest the existence of theoretical contamination in specific CE measures (e.g., through the inclusion of related concepts in the proposed CE definition), compromising their theoretical rigor and raising a need for scholars to verify the theoretical underpinnings of their adopted CE scales.

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Section: Discussion Implications and Limitationsmentioning

confidence: 99%

Hallmarks and potential pitfalls of customer‐ and consumer engagement scales: A systematic review

Hollebeek

Sarstedt

Menidjel

et al. 2023

Psychology and Marketing

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“…A key assumption concerns which statistical representation to use for constructs in the model. Notes: SE = standard error; CI = 95% percentile bootstrap confidence interval While using factors as statistical proxies for constructs is standard in marketing research, researchers increasingly question the reflex-like assumption of their universal applicability (Cho et al, 2022a;Rhemtulla et al, 2020;Rigdon and Sarstedt, 2022). Many research situations call for the specification of components, especially when the constructs are conceptually rather close to an aggregation of their indicators.…”

Section: Discussionmentioning

confidence: 99%

“…The measurement model generally identifies the relationships between constructs and indicators. As a construct is not directly observable, it needs to be represented by some entity linked to the empirical data (indicators) – that is, a statistical representation of the construct (Rigdon and Sarstedt, 2022), thereby permitting statistical testing of hypotheses regarding the construct. When using IGSCA, researchers distinguish between two statistical representations of constructs, which leads to two different measurement models (Hwang et al , 2021b).…”

Section: Principles Of Integrated Generalized Structured Component An...mentioning

confidence: 99%

A primer on integrated generalized structured component analysis

Hwang

Sarstedt

Cho

et al. 2023

EBR

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Purpose The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method. Design/methodology/approach By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software. Findings Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit). Research limitations/implications Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field. Originality/value To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.

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“…Few researchers seem to be aware that this practice turns their confirmatory research into an exploratory fishing expedition (e.g., Cliff, 1983), which will likely produce results that do not 10 generalize beyond the sample at hand (Green et al, 1998;Rigdon & Sarstedt, 2022;Tomarken & Waller, 2003). The p-values arising from such analyses can easily be about as informative as a randomly selected p-value.…”

Section: Disclaimermentioning

confidence: 99%