Descriptive statistics and the application of multivariate data analysis techniques such as regression analysis and factor analysis belong to the core set of statistical instruments, and their use has generated findings that have significantly shaped the way we see the world today. The increasing reliance on and acceptance of statistical analysis, as well as the advent of powerful computer systems that allow for handling large amounts of data, paved the way for the development of more advanced next-generation analysis techniques. Structural equation modeling (SEM) is among the most useful advanced statistical analysis techniques that have emerged in the social sciences in recent decades. SEM is a class of multivariate techniques that combine aspects of factor analysis and regression, enabling the researcher to simultaneously examine relationships among measured variables and latent variables as well as between latent variables. Considering the ever-increasing importance of understanding latent phenomena such as consumer perceptions, attitudes, or intentions and their influence on organizational performance measures (e.g., stock prices), it is not surprising that SEM has become one of the most prominent statistical analysis techniques today. While there are many approaches to conducting SEM, the most widely applied method is certainly covariance-based SEM (CB-SEM). Since its introduction by Karl Joreskog in 1973, CB-SEM has received considerable interest among empirical researchers across virtually all social sciences disciplines. Recently, however, partial least squares SEM (PLS-SEM) has gained massive attention in the social sciences as an alternative means to estimate relationships among multiple latent variables, each measured by a number of manifest variables. Along with the ongoing development of both SEM techniques, research has recently witnessed an increasing debate about the relative advantages of PLS-SEM vis-a-vis other SEM methods, which resulted in the formation of two opposing camps. One group of scholars, supportive of the PLS-SEM method, has emphasized the method’s prediction-orientation and capabilities to handle complex models, small sample sizes, and formatively specified constructs. The other group has noted that PLS-SEM is not a latent variable method, producing biased and inconsistent parameter estimates, calling for the abandonment of the method. Tying in with these debates, in this manuscript, we highlight five different perspectives on comparing results from CB-SEM and PLS-SEM. These perspectives imply that the universal rejection of one method over the other is shortsighted as such a step necessarily rests on assumptions about unknown entities in a model and the parameter estimation. We argue that researchers’ functional background and adherence to a specific position in philosophy of science contribute to the confusion over which method is “right” and which one is “wrong.” Based on our descriptions, we offer five recommendations that share a common theme: The comparison of results from CB-SEM and PLS-SEM—despite considerable research interest—is misguided, capable of providing both false confidence and false concern. Instead of seeking confidence in the comparison of results from the different approaches, researchers should instead focus on more fundamental aspects of modeling, measurement, and statistical analysis.

On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations

This research extends the theory of planned behaviour (TPB) and aims to study the underlying factors that influence Chinese consumers' purchase intentions towards green products. The conceptual model encompasses four elements (subjective norms, perceived behaviour control, moral norms and attitude) and one consumer response (purchase intention).,The current research employs a questionnaire survey and two experiments. In Study 1, the hypotheses were tested using structural equation modelling with 485 consumers in China. Study 2 employed a single-factor, two-condition (morally engaged vs control), between-subject design.,The findings reveal that the morally extended TPB framework is more applicable in predicting Chinese consumers' green purchase intentions than the original TPB model. Attitude plays the most significant role in predicting purchase intentions, and moral norms prove to be a mediator of the relationship between the original construct of subjective norms and purchase intentions. The findings further revealed that moral norms comprise the underlying mechanism of the relationship between subjective norms and attitude.,This study therefore expands the TPB theory by including moral norms. Moreover, it contributes to the literature by clarifying the direct, indirect and total effects of each TPB element on the purchase intentions towards green products. Finally, managerial implications are given.

Moral norm is the key: An extension of the theory of planned behaviour (TPB) on Chinese consumers' green purchase intention

Purpose – Values-attitudes hierarchical models are quite frequent in the consumer behaviour literature. In attitudinal models specific to food produced in an environmentally friendly way (i.e. “green” food), past research evidence mainly originating in Western cultures posits that the strongest path of the model can be found among collectivistic consumer values, general environmental attitudes, and attitudes specific to green food. On the other hand, in non-Western cultures (i.e. China), green food (e.g. organic) is perceived by consumers as safer to consume. With this as point of departure, the present paper aims to examine whether attitudes towards green food in a values-attitudes model in China are determined as postulated in past Western research.Design/methodology/approach – A “typical” (i.e. Western research evidence-based) values-attitudes hierarchical model was developed and a questionnaire comprising 34 items reflecting the conceptual model was designed. Data collection was focused on six major Chinese cities, as this is where the current changes in eating habits are predominantly taking place. Data were collected by personal interviews conducted by local researchers between January and March 2009 through a mall-intercept method. A total number of 479 respondents were recruited, equally distributed among the six cities.Findings – Collectivistic values and environmental attitudes were still found to be strong determinants of Chinese consumers’ attitudes towards green foods; contrary to relevant Western findings, however, collectivism also influenced technological attitudes; which, in turn, influenced attitudes towards green food jointly with environmental attitudes. These findings point to the conclusion that Chinese consumers, possibly guided by altruistic predispositions, see technology as a positive determinant of both food safety and environmental friendliness in food production. Empirical findings like these highlight the need for adaptation of well-substantiated models to completely customised research approaches within new globally rising environments.Originality/value – It is still not quite clear how green food products are perceived in South-East Asian consumer cultures, as well as in other non-Western contexts. This scarcity makes the empirical examination of well-established values-attitudes hierarchies in such contexts valuable from an academic and practitioner’s point of view.

Testing and Validation of a Hierarchical Values-Attitudes Model in the Context of Green Food in China


Purpose
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.


Design/methodology/approach
The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.


Findings
The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).


Research limitations/implications
The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.


Practical implications
The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.


Originality/value
There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.


PLS-SEM’s most wanted guidance

Latent class analysis in PLS-SEM: A review and recommendations for future applications

Sustainable Development for Vietnam’s Economy in the Context of Globalization and Industrial Revolution 4.0

https://doi.org/10.1016/j.dib.2022.108198

Dong Phong Nguyen

Papers

Sustainable Development for Vietnam’s Economy in the Context of Globalization and Industrial Revolution 4.0

Dataset for cognition processes, motivations, spatial presence experience, and customer engagement in retail mobile apps

Vietnam and the Global Economy

Human resource management practices in higher education: a literature review using co-word analysis