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The search for flexible models has led the simple multinomial logit model to evolve into the powerful but computationally very demanding mixed multinomial logit (MMNL) model. That flexibility search lead to discrete choice hybrid choice models (HCMs) formulations that explicitly incorporate psychological factors affecting decision making in order to enhance the behavioral representation of the choice process. It expands on standard choice models by including attitudes, opinions, and perceptions as psychometric latent variables.

In this paper we describe the classical estimation technique for a simulated maximum likelihood (SML) solution of the HCM. To show its feasibility, we apply it to data of stated personal vehicle choices made by Canadian consumers when faced with technological innovations.

We then go beyond classical methods, and estimate the HCM using a hierarchical Bayesian approach that exploits HCM Gibbs sampling considering both a probit and a MMNL discrete choice kernel. We then carry out a Monte Carlo experiment to test how the HCM Gibbs sampler works in practice. To our knowledge, this is the first practical application of HCM Bayesian estimation.

We show that although HCM joint estimation requires the evaluation of complex multi-dimensional integrals, SML can be successfully implemented. The HCM framework not only proves to be capable of introducing latent variables, but also makes it possible to tackle the problem of measurement errors in variables in a very natural way. We also show that working with Bayesian methods has the potential to break down the complexity of classical estimation.

The objective of this chapter is to provide strategy researchers with a general resource for applying structural equation modeling (SEM) in their research. This objective is important for strategy researchers because of their increased use of SEM, the availability of advanced SEM approaches relevant for their substantive interests, and the fact that important technical work on SEM techniques often appear in outlets that may not be not readily accessible. This chapter begins with a presentation of the basics of SEM techniques, followed by a review of recent applications of SEM in strategic management research. We next provide an overview of five types of advanced applications of structural equation modeling and describe how they can be applied to strategic management topics. In a fourth section we discuss technical developments related to model evaluation, mediation, and data requirements. Finally, a summary of recommendations for strategic management researchers using SEM is also provided.

This paper introduces a behavioral framework to model residential relocation decision in island areas, at which the decision in question is influenced by the characteristics of island regions, policy variables related to accessibility measures, and housing prices at the proposed island area, as well as personal, household (HH), job, and latent characteristics of the decision makers.

The model framework corresponds to an integrated choice and latent variable (ICLV) setting where the discrete choice model includes latent variables that capture attitudes and perceptions of the decision makers. The latent variable model is composed of a group of structural equations describing the latent variables as a function of observable exogenous variables and a group of measurement equations, linking the latent variables to observable indicators.

An empirical study has been developed for the Greek Aegean island area. Data were collected from 900 HHs in Greece contacted via telephone. The HHs were presented hypothetical scenarios involving policy variables, where 2010 was the reference year. ICLV binary logit (BL) and mixed binary logit (MBL) relocation choice models were estimated sequentially. Findings suggest that MBL models are superior to BL models, while both the policy and the latent variables significantly affect the relocation decision and improve considerably the models' goodness of fit. Sample enumeration method is finally used to aggregate the results over the Greek population.

In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.

How much goodness do we expect from our neighbours? People living nearby might be locked with each other in interdependencies by spatial proximity but often do not know each other more personally. This chapter explores the question of how latent neighbourliness – an expectation that neighbours will have our back even though we might not know them – emerges. We draw on statistical analyses of survey data from four neighbourhoods in Berlin, Germany, and a pre- and post-COVID-19 methodology, therefore capturing a time when people were asked to stay home and within their neighbourhoods. Our findings demonstrate that latent neighbourliness is neither significantly associated with personal support from neighbours when facing important challenges, nor personal support experienced in the neighbourhood with others whom we know, but who are not neighbours (e.g., family members). Neither is latent neighbourliness a fixed attitude that can be explained by individual characteristics and/or positions (such as age, gender, education, income, one’s employment situation and others). In contrast, we find that, apart from individual generalised trust towards others, the neighbourhood setting itself shapes levels of latent neighbourliness among all demographics. Additionally, those with younger children show higher latent neighbourliness, most likely a result of moral geographies. We argue that caring for children in public and experiencing or displaying moral codes that others can read makes it easier to develop an expectation of goodwill (or for that matter, hostility) from neighbours, without having more durable ties to them.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

In this study, the mediating effects of perceived behavior control and attitudes toward being an entrepreneur were investigated in the relationship between family business experience and entrepreneurial intentions of university students. First, the variables of perceived behavioral control and attitude toward being an entrepreneur were defined as the mediators used in explaining the entrepreneurial intention. Then, the process of investigating the mediation effects with the structural equation modeling (SEM) approach in two cases with one and two mediating latent variables is explained.

In this study, the process of investigating the mediation effects in two situations where there is one and two mediating latent variables by SEM is presented. In addition, the decomposition of the effects for the model consisting of two mediating latent variables is given in detail with matrix notation.

It has been determined that the latent variable of perceived behavior control functions as a “full mediator” in the relationship between the family ownership story and the entrepreneurial intention. The study also revealed that students whose family's business ownership score is high and who are self-confident in the process of becoming an entrepreneur have stronger entrepreneurial intentions.

In the research, the distinction between the model used in determining the entrepreneurial intentions of university students and their mediation and indirect effects is explained in detail with matrix notations with the SEM approach.

The purpose of this project was to determine if there was a difference in the longevity of the latent fingerprints of children vs adults. It is generally believed that a subject’s age does not affect the evaporation rates of fingerprints. However, based upon a recent criminal investigation of child abduction, it was hypothesized that children’s latent fingerprints do not last as long as those of adults. Participation in this study was voluntary and informed consent obtained. A total of 97 subjects pressed their fingers on glass slides and their latent fingerprints were lifted one, three, five and seven days later. A comparison was then made between the longevity of the prints of children vs adults. Almost all of the adult prints were still present on day seven. Of the children’s prints, 20 percent were unclear on day three; 54 percent were unclear on day five; and 76 percent were unclear on day seven. This has implications for law enforcement and forensic science in that time may become a critical variable in criminal investigations requiring the lifting of latent fingerprints of children.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

The purpose of this paper is to incorporate explicitly consumer heterogeneity into market response models estimated with store‐level scanner‐data.

Latent structures in market response to a product category using aggregated scanner data registered by a supermarket are identified. Specifically, latent consumer segments with diverse preferences towards brands and different responses to marketing stimuli from data consisting of daily marketing actions (i.e. price, promotions, advertising, etc.) and sales of competing brands are identified.

The existence of different latent segments with diverse preferences and response patterns to marketing stimuli were detected. More specifically, the fit of the statistical analysis for the different model possibilities made it possible to identify four market segments. It was also found that the intrinsic brand attractiveness as a measure of consumer brand preference is different between segments. Finally, the price sensitivity is also different between segments.

The time cost necessary to obtain the parameter estimates is too high, which is usual in the models estimated with iterative EM algorithms.

This work deepens one's knowledge of the identification and selection of latent market structures, specifically latent segments with different purchase patterns and behaviours. The possibility of developing the analysis with aggregated data at the store level increases the potential utility for academics and marketing managers.

Although most applications use weekly data, this proposal models daily fluctuations in sales – as a result, making it possible to obtain consumer segments based on daily changes.