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Outsourcing of accounting increasingly attracts research interest, but research concerning the impact of the benefits of outsourcing on firm capabilities and performance across firms remains limited. This paper aims to reveal the unobservable latent classes of firms that outsource their accounting functions by testing a research model concerning the topic.

The authors build on accounting outsourcing research and adapt a research model from the literature on business services outsourcing. The authors analyze the data from 261 small and medium-sized enterprises in Europe using finite mixture structural equation modeling (FMSEM) and additional methods.

The authors reveal three latent classes with different research models. Thriving outsourcers (N = 103) have a positive attitude toward accounting outsourcing and associate competitive capabilities with mediating the relationship from outsourcing benefits to firm performance. Annoyed outsourcers (N = 143) are dissatisfied with their accounting service provider and only associate outsourcing benefits with competitive capabilities. Convenient outsourcers (N = 15) feel comfortable with their current accounting service provider and associate outsourcing benefits with neither capabilities nor with firm performance.

The study initiates the discussion about the unobservable heterogeneity among accounting outsourcers. The study introduces the use of the FMSEM method in accounting outsourcing research.

The study offers novel insights concerning accounting outsourcers and proposes original explanations for their outsourcing decisions that would help both the outsourcers and accounting service providers.

The study might be the first to categorize accounting outsourcers using FMSEM.

On many occasions, organizational science research has been referred to as fragmented and disjointed, resulting in a literature that is, in the opinion of many, difficult to navigate and comprehend. One potential explanation is that scholars have failed to comprehend that organizations are complex and intricate systems. In order to move us past this morass, we recommend that researchers extend beyond traditional rational, mechanistic, and variable-centered approaches to research and integrate a more advantageous pattern-oriented approach within their research program. Pattern-oriented methods approximate real-life phenomena by adopting a holistic, integrative approach to research wherein individual- and organizational-systems are viewed as non-decomposable organized wholes. We argue that the pattern-oriented approach has the potential to overcome a number of breakdowns faced by alternate approaches, while offering a novel and more representative lens from which to view organizational- and HRM-related issues. The proposed incorporation of the pattern-oriented approach is framed within a review and evaluation of current approaches to organizational research and is supplemented with a discussion of methodological and theoretical implications as well as potential applications of the pattern-oriented approach.

Multilevel approaches are generally well suited to group communication because what people say and do in groups is a function of intra- and trans-individual mechanisms. This chapter first provides a brief overview of group research as a multilevel problem and then describes more modern approaches to modeling nested data using latent variable models, including multilevel structural equation modeling and latent class analysis. The chapter concludes by addressing conceptual opportunities provided by multilevel latent modeling approaches to group communication.

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.

This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.

The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.

To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.

The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Under investigation is Prosecco wine, a sparkling white wine from North‐East Italy. Information collection on consumer perceptions is particularly relevant when developing market strategies for wine, especially so when local production and certification of origin play an important role in the wine market of a given district, as in the case at hand. Investigating and characterizing the structure of preference heterogeneity become crucial steps in every successful marketing strategy. The purpose of this paper is to investigate the sources of systematic differences in consumer preferences.

The paper explores the effect of inclusion of answers to attitudinal questions in a latent class regression model of stated willingness to pay (WTP) for this specialty wine. These additional variables were included in the membership equations to investigate whether they could be of help in the identification of latent classes. The individual specific WTPs from the sampled respondents were then derived from the best fitting model and examined for consistency.

The use of answers to attitudinal question in the latent class regression model is found to improve model fit, thereby helping in the identification of latent classes. The best performing model obtained makes use of both attitudinal scores and socio‐economic covariates identifying five latent classes. A reasonable pattern of differences in WTP for Prosecco between CDO and TGI types were derived from this model.

The approach appears informative and promising: attitudes emerge as important ancillary indicators of taste differences for specialty wines. This might be of interest per se and of practical use in market segmentation. If future research shows that these variables can be of use in other contexts, it is quite possible that more attitudinal questions will be routinely incorporated in structural latent class hedonic models.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.

Cyber-attacks on hotel information systems could threaten the privacy of consumers and the integrity of the data they exchange upon connecting their mobile devices to hotel networks. As the perceived cyber-security risk may be reflected heterogeneously within the US consumer population traveling internationally, the purpose of this study is to examine such heterogeneity to uncover classes of US consumers based on their perceptions of risk of using tablets for various tasks when staying in hotels abroad.

Using data collected from 1,016 US consumers who stayed in hotels abroad, this study used latent profile analysis (LPA) to classify the consumers based on their perceptions of risk associated with several tablet use behaviors in hotels.

The analysis uncovered four latent classes and produced a characterization of these classes according to several common behavioral (frequency of travel, the continent of the destination, duration of stay and purpose of travel) and demographic (gender, age, income and education) consumer characteristics.

Being the first study that classifies consumers based on the risk of using tablets in hotels while traveling internationally, this study brings the following contributions: offers a methodology of classifying (segmenting) consumer markets based on their cyber-security risk perceptions, uses LPA, which provides opportunities for an accurate and generalizable characterization of multivariate data that comprehensively illustrate consumer behavior and broadens the perspective offered by the current literature by focusing on consumers who travel from their US residence location to international destinations.

酒店信息系统的网络攻击可能会泄露消费者隐私以及其通过酒店网络连接移动网络而进行的数据交换。由于美国消费者在出国旅游中对于网络安全危险的感知因人而异，因此，本论文旨在研究这种个人差异性，对美国消费者在出国旅游居住酒店中使用平面电脑的危险感知，进行特点分类。

本论文样本为居住在国外酒店的1,016名美国消费者。本论文采用潜在剖面分析（LPA），对其使用酒店平板电脑的危险感知进行分类。

本论文研究结果归纳四种潜在类别，以及根据集中常见行为变量（旅游频率、旅游目的地所在的洲、酒店居住时长、旅游目的地）和统计人口信息（性别、年龄、收入、和教育），进行消费者特点归纳。

本论文是首篇基于消费者出国旅游时使用酒店平板电脑的危险感知而进消费者分类的文章。因此，本论文结果有以下贡献：（1）贡献一种分类研究方法（市场细分），基于网络安全危险感知而划分;（2）使用潜在剖面分析方法，为多变量数据分析消费者行为，产出精确而概括特性的研究，提供机会;（3）对现有文献对国际旅游的美国消费者的研究拓宽了视野。

网络安全危险、平板电脑、潜在剖面分析、酒店、国际旅游

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.

This article addresses simultaneously two important features in random utility maximisation (RUM) choice modelling: choice set generation and unobserved taste heterogeneity. It is proposed to develop and to compare definitions and properties of econometric specifications that are based on mixed logit (MXL) and latent class logit (LCL) RUM models in the additional presence of prior compensatory screening decision rules. The latter allow for continuous latent bounds that determine choice alternatives to be or not to be considered for decision making. It is also proposed to evaluate and to test each against the other ones in an application to home-to-work mode choice in the Paris region of France using 2002 data.

Students’ evaluation of teaching quality plays a major role in higher education. Satisfaction is not directly observable, nevertheless it can be measured through multi-item measurement scales. These instruments are extremely useful and their importance requires accurate development and validation procedures. The purpose of this paper is to show how latent class (LC) analysis can improve the procedures for developing and validating a multi-item measurement scale for measuring students’ evaluation of teaching and, at the same time, provide a deeper insight in the phenomenon under investigation.

The traditional literature highlights specific protocols along with the statistical instruments to be used for achieving this goal. However, these tools are suited for metric variables but they are adopted even when the nature of the observed variables is different, as it often occurs, since in many cases the items are ordinal. LC analysis takes explicitly into account the ordinal nature of the variables and also the fact that the object of interest is unobservable.

The data refer to the questionnaire to evaluate didactics to the students of the University of Padua. Within LC analysis allows an insight of scale properties, such as dimensionality, validity and reliability. Moreover, the results provide a deeper view in the way students use the scale to report satisfaction suggesting to revise the instrument according to the suggestion by the National Agency for University Evaluation.

The paper gives an original contribution on two sides. On the side of methods, it introduces a more accurate methodology for evaluating scales to measure the students’ satisfaction. On the side of applications, it provides important suggestions to the university management to improve the process of quality of the didactics evaluation.