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The purpose of this paper is to examine the number and nature of latent classes of delinquency that exist among male juvenile offenders incarcerated in prisons in Pakistan.

The sample consisted of 415 young male offenders incarcerated in prisons in Khyber Pakhtunkhwa (KPK) Pakistan. Latent class analysis was employed to determine the number and nature of delinquency latent classes. Multinomial logistic regression was used to estimate the associations between latent classes and the three factors of criminal social identity (cognitive centrality, in-group affect, and in-group ties) whilst controlling for criminal friends, period of confinement, addiction, age, and location.

The best fitting latent class model was a three-class solution. The classes were labelled: “minor delinquents” (the baseline/normative class; Class 3), “major delinquents” (Class 1), and “moderate delinquents” (Class 2). Class membership was predicted by differing external variables. Specifically, Class 1 membership was related to having more criminal friends; while Class 2 membership was related to lower levels of in-group affect and higher levels of in-group ties.

Findings are discussed in relation to refining current taxonomic arguments regarding the structure of delinquency and implications for prevention of juvenile delinquent behaviour.

First, most previous studies have focused on school children, whereas, this paper focuses on incarcerated juvenile offenders. Second, this research includes delinquents from Pakistan, whereas, most previous research has examined delinquent behaviour in western cultures.

We discuss adoption as a diverse family structure in America. Adoption has existed in some form throughout the history with the portrayal varying by historical epoch. Adoption has been both disparaged and idealized to perpetuate the interest of elite players. This chapter discusses adoption in terms of the changing demographic which 21st century families face. In this manuscript, we first discuss the history of adoption in the United States including its impact as social control of premarital sex. Then the three players in the adoption triad are discussed and analyzed. Finally, we highlight how demographics of race, class, gender, and sexuality impact the adoption experience by 21st century families. Specifically, we explore the recent National Survey of Adoptive Parents from the United States Center for Disease Control and look at the modern adoption experience

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.

Discusses and empirically tests special cases of multiple‐chain mixed Markov latent class models with business data. The switches between negative and positive changes in earnings‐per‐share of firms are captured by alternative Markov models. The estimated response probabilities and state transition probabilities show interesting changes in the transformation patterns of the firms over time. Shows that Markov models can be valuable tools in predicting switches in profitability of firms.

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.

The purpose of this paper is to investigate the effectiveness of a job resources-based intervention aimed at proactively increasing work engagement and team innovativeness during organizational restructuring using a person-centered approach.

The intervention was conducted in two organizations: two departments served as participants (n=82) and two as controls (n=52). The aim was to first identify sub-groups of employees with different developmental patterns of work engagement, and then to determine whether these sub-groups benefited differently from the intervention with respect to team innovativeness and work engagement.

Latent profile analysis identified three different patterns of work engagement among the participants: high and stable (n=64), moderate and decreasing (n=13), and low and decreasing (n=5). The χ²-test yielded no significant difference between participants and controls (n=52) with respect to team innovativeness over time. However, t-tests showed that team innovativeness increased in the high work engagement class and somewhat decreased in the moderate and low work engagement classes.

During organizational changes, those initially work-engaged seem to be able to proactively build their team innovativeness via a job resources-based intervention and remain engaged; whereas those initially not work-engaged may not, and their work engagement may even decrease.

This study reveals that an initial level of work engagement is a prerequisite why some employees profit more from a job resources-based intervention than others and provides tailored knowledge on the effectiveness of the intervention.

Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation.

The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments.

The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market.

The proposed statistical models are new in the field of financial markets.

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.

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

The purpose of the paper is the analysis of the evolution of students’ satisfaction over time in a large Italian university and the effects on it because of some characteristics of the teachers: didactic practices, beliefs and needs with regard to teaching and learning.

The first step of the analysis identifies a latent construct, measured with items composing the questionnaire, and proposes a reduced set of indicators to measure satisfaction and to model its evolution over time (information collected in three consecutive academic years is available). A second step clusters teachers in homogenous groups with reference to their opinions, beliefs and needs, collected with a new survey conducted at the University of Padova, with the aim of developing strategies to support academic teachers. Then, a mixture conditional latent growth model is estimated with covariates affecting the latent parameters and class membership.

Model estimation identifies a large group of university courses with a high level of satisfaction, which stays constant over time, and a small group of problematic courses with low satisfaction, moreover, that decreases over the three considered academic years. Interesting significant effects of covariates related to both the teacher and the didactic activity are estimated.

Statistical analyses show that the implementation of innovative didactic practices and commitment to quality of teaching are important factors to be encouraged by the university management. On the contrary, the traditionalist way of teaching and a low passion for teaching do not improve students’ satisfaction.