A comparison between structural equation modelling (SEM) and Bayesian SEM approaches on in-store behaviour

Purpose: The purpose of this paper is to examine the effects of atmospherics and affective state on shoppers’ in-store behaviour using the two approaches in structural equation modelling (SEM), i.e. Frequentist and Bayesian approaches. Shoppers’ affective state was tested for its mediating effect on in-store shopping behaviour. Design/methodology/approach: The final sample consists of 382 respondents who were drawn from shoppers at selected apparel stores in six of the most popular shopping malls around Kuala Lumpur (Malaysia). A frequentist approach to SEM is common among researchers and offers generally an analysis of the relationships between multiple latent variables and constructs. Alternatively, the Bayesian SEM (BSEM) approach stems from the diffusion of the model’s posterior distributions using the Markov Chain Monte Carlo technique. More specifically, this technique is inherently more flexible and substantive in determining parameter estimates as compared to the more conventional, the frequentist approach to SEM. Findings: The results show the mixed effects of atmospheric cues in retail setting on shoppers’ affective state. More specifically, the positive direct effect of atmospheric cues (music) on in-store behaviour was confirmed while other atmospheric cues (colour and store layout) were found to be fully mediated by affective state. The Bayesian approach was able to offer more distinctive results complementing the frequentist approach. Research limitations/implications: Although the current sample size is adequate, it will be interesting to examine how a bigger sample size and different antecedents of in-store behaviour in retailing can affect the comparison between the frequentist approach in SEM and BSEM. Practical implications: The authors found that a combination of well-designed store atmospherics and layout store can produce pleasurable effects on shoppers resulting in positive affective state. This study found that results from both frequentist and Bayesian approaches complement each other and it may be beneficial for future studies to utilise both approaches in SEM. Originality/value: This paper met the aim to compare the approaches in SEM and the need to consider both approaches on in-store shopping environment. Overall, the authors contend that the Bayesian approach to SEM is a potentially viable alternative to frequentist SEM, especially when studies are conducted under dynamic conditions such as apparel retailing

Contemporary approaches to modelling the consumer

Modelling is an exciting area of consumer psychology, with application to many problems and contexts. We have covered the founding principles and objectives of the modelling process, which have remained largely unchanged over the course of time. What has changed are the constant innovations in methodologies (especially analyses) and software development that keep pushing the boundaries of modelling. These developments have given rise to some interesting opportunities to work in multidisciplinary teams (especially around exploiting big data in a meaningful way) and to the opening up of new and innovative areas of research in understanding the consumer

Conceptualizing and quantifying body condition using structural equation modelling:A user guide

Body condition is an important concept in behaviour, evolution and conservation, commonly used as a proxy of an individual's performance, for example in the assessment of environmental impacts. Although body condition potentially encompasses a wide range of health state dimensions (nutritional, immune or hormonal status), in practice most studies operationalize body condition using a single (univariate) measure, such as fat storage. One reason for excluding additional axes of variation may be that multivariate descriptors of body condition impose statistical and analytical challenges. Structural equation modelling (SEM) is used in many fields to study questions relating multidimensional concepts, and we here explain how SEM is a useful analytical tool to describe the multivariate nature of body condition. In this ‘Research Methods Guide’ paper, we show how SEM can be used to resolve different challenges in analysing the multivariate nature of body condition, such as (a) variable reduction and conceptualization, (b) specifying the relationship of condition to performance metrics, (c) comparing competing causal hypothesis and (d) including many pathways in a single model to avoid stepwise modelling approaches. We illustrated the use of SEM on a real-world case study and provided R-code of worked examples as a learning tool. We compared the predictive power of SEM with conventional statistical approaches that integrate multiple variables into one condition variable: multiple regression and principal component analyses. We show that model performance on our dataset is higher when using SEM and led to more accurate and precise estimates compared to conventional approaches. We encourage researchers to consider SEM as a flexible framework to describe the multivariate nature of body condition and thus understand how it affects biological processes, thereby improving the value of body condition proxies for predicting organismal performance. Finally, we highlight that it can be useful for other multidimensional ecological concepts as well, such as immunocompetence, oxidative stress and environmental conditions

Unobserved heterogeneity in hospitality and tourism research

Despite the growing complexity of structural equation model (SEM) applications in tourism, it is surprising that most applications have estimated these models without accounting for unobserved heterogeneity. In this article, we aim to discuss the concept of unobserved heterogeneity in more detail, highlighting its serious threats to the validity and reliability of SEMs. We describe a Bayesian finite mixture modeling framework for estimating SEMs while accounting for unobserved heterogeneity. We provide a comprehensive description of this model, and provide guidance on its estimation using the WinBUGS software. We illustrate the importance of unobserved heterogeneity and the finite mixture modeling framework using a didactic application on brand equity where heterogeneity is likely to play an important role because of the differences in how consumers perceive the different dimensions of brand equity. We compare between various models and illustrate the differences between the standard and heterogeneous SEM and discuss the implications for research and practice

Using causal models to distinguish between neurogenesis-dependent and -independent effects on behaviour

There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past fifteen years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenesis mediates the effect of an intervention on behaviour. Previous studies may have incorrectly attributed changes in behavioural performance to neurogenesis because the role of known (or unknown) neurogenesis-independent mechanisms were not formally taken into consideration during the analysis. Causal models can tease apart complex causal relationships and were used to demonstrate that the effect of exercise on pattern separation is via neurogenesis-independent mechanisms. Many studies in the neurogenesis literature would benefit from the use of statistical methods that can separate neurogenesis-dependent from neurogenesis-independent effects on behaviour

Learning Large-Scale Bayesian Networks with the sparsebn Package

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure

Exploring user behavioral data for adaptive cybersecurity

This paper describes an exploratory investigation into the feasibility of predictive analytics of user behavioral data as a possible aid in developing effective user models for adaptive cybersecurity. Partial least squares structural equation modeling is applied to the domain of cybersecurity by collecting data on users’ attitude towards digital security, and analyzing how that influences their adoption and usage of technological security controls. Bayesian-network modeling is then applied to integrate the behavioral variables with simulated sensory data and/or logs from a web browsing session and other empirical data gathered to support personalized adaptive cybersecurity decision-making. Results from the empirical study show that predictive analytics is feasible in the context of behavioral cybersecurity, and can aid in the generation of useful heuristics for the design and development of adaptive cybersecurity mechanisms. Predictive analytics can also aid in encoding digital security behavioral knowledge that can support the adaptation and/or automation of operations in the domain of cybersecurity. The experimental results demonstrate the effectiveness of the techniques applied to extract input data for the Bayesian-based models for personalized adaptive cybersecurity assistance

Spatial epidemiology of lung-cancer mortality : geographical heterogeneity and risk-factors assessment

PhD ThesisCancer is the leading cause of mortality in Andalucía (southern Spain) for both men and women, and lung cancer is the main cause of cancer mortality for men. Radon-gas exposure is the second most important cause of lung-cancer after tobacco-smoking, which also causes larynx cancer, and Chronic Obstructive Pulmonary Disease (COPD). Radon-gas is a radioactive decay element which originates from radium. Consequently, presence in the soil varies according to lithology (rock composition) which is a surrogate measure for potential radon-gas exposure. Lithology can explain some lung-cancer deaths, but not deaths due to either larynx cancer or COPD. A small-area analysis was implemented for the period 1986-1995. Fully-Bayesian regression analysis was used to assess the association between lithology and the spatial distribution of lung-cancer deaths (25,006 cases). Area-level deprivation, a surrogate measure for tobacco-smoking, was accounted for. The number of deaths due to larynx cancer (3,653 cases) and COPD (5,143 cases) were also modelled for comparison purposes. Computation was accomplished via Markov Chain Monte Carlo methods, using WinBUGS software. The spatial distribution of lung-cancer deaths (but neither larynx cancer, nor COPD) was positively associated with lithology, which is consistent with current epidemiological knowledge. These results remained after adjusting for area-level deprivation. The model used allows for separate estimation of risk due to both lithology (RR = 1.02; 95% Credible Interval (CI) = 1.015 – 1.031) and deprivation (RR = 1.04; 95% CI = 1.033 – 1.048). This lithology score overcomes the difficulties in obtaining actual radon-gas measurements, and can be further improved. The results go some way to explaining the regional variability in lung cancer mortality in Southern Spain.Fellowship granted by the Spanish body Instituto de Salud Carlos III (ref BAE06/90003

Consumer behaviour in online shopping - understanding the role of regulatory focus.

The behaviour of consumers on the Internet is increasingly a focus of marketing research. In particular, consumers behaviour in online shopping, from adoption motivation to post-usage behaviour, has become a major focus of research in the field of marketing, especially within consumer behaviour. Yet it has been acknowledged that while aspects such as adoption and usage motivation are now better understood, there are many questions that remain unanswered, and this warrants continued research effort. In line with the above, this research addresses an issue in online consumer behaviour that is currently under researched and which relates to the role that the consumers regulatory focus trait plays in their manifested behaviour in online shopping. The research argues that it is important to understand the role of regulatory focus in online shopping because this psychological trait has been shown to affect other aspects of human behaviour such as in response to advertising, dieting and sports. Drawing upon research from consumer behaviour and the wider fields of marketing and psychology, this research proposes a number of hypotheses relating the consumers regulatory focus to her perception of online shopping, motivation for online shopping, and actual usage behaviour in a structural manner. The resulting structural equation model is then tested using empirical data obtained from 306 Internet shoppers in the United Kingdom. The results of the research confirm that regulatory focus has an influence on consumer behaviour in online shopping by affecting their perception, motivation and usage of online shopping. The research makes a unique contribution by demonstrating that regulatory focus is a valid and robust predictor of online shopping behaviour and behavioural outcomes, a conclusion which is relevant to both marketing research and marketing practice. Finally, the research identifies and recommends areas for future studies