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Understanding the multifaceted images of tourism destinations is critical for effective destination marketing and management strategies. Traditional approaches, including conceptualization of destination images or analysis of their antecedents and consequences, are commonly used. This study aims to advocate the inclusion of visitors’ latent profiles based on cognitive images to enrich the evaluation and formulation of destination marketing and management strategies.

The analysis focuses on Serbia, an emerging destination, that attracts an increasing number of first-time, repeat and prospective visitors. Exploratory factor analysis and confirmatory factor analysis were used to test the potential dimensions (tangible and intangible cultural destination; infrastructural and accessible destination; active, nature and family destination; sensory and hospitable destination; and welcoming, value for money (VFM) and safe destination) of the cognitive destination image factors scale while subtypes (profiles) were obtained using latent profile analysis (LPA).

The cognitive image component encompasses the perceived attributes of a destination, whether derived from direct experience or acquired through other means. The study identified the following profiles: conventional destination; sensory and hospitable destination; welcoming, VFM and safe destination; secure and active family destination and accessible cultural destination, which are presented individually with their sociodemographic assets.

The main contribution of the paper is the application of a novel method (LPA) for profiling visitor segments based on cognitive destination image. From a theoretical perspective, this research contributes to the extant body of literature pertaining to the destination image, thereby facilitating the identification of discrete latent visitor segments and elucidating noteworthy differences among them concerning a cognitive image.

The purpose of this paper is to design a system for measuring the level of internationalisation of companies in the field of developing countries, through latent variables based on multiple indicators, external and internal orientation.

From a sample of 112 international companies in Colombia, the methodology of latent variable analysis (LPA) is applied to a series of complementary tools, such as a model of structural equations, regression models and cluster analysis of companies.

The paper allows to verify the identification of six latent variables and their relationships, as well as to identify four levels of internationalisation from the structure of latent variables identified.

This is the first application of this recent and sophisticated statistical technique to the field of measuring the level of business internationalisation, especially indicated in the Latin American area, where an increasing number of companies are advancing in their process of international expansion.

Diseñar un sistema de medición del nivel de internacionalización de las empresas en el ámbito de los países en vías de desarrollo, mediante variables latentes basadas en múltiples indicadores, de orientación externa e interna.

A partir de una muestra de 112 empresas internacionales de Colombia, se aplica la metodología de Análisis de Variables Latentes (LPA) unod a una serie de herramientas complementarias, como un modelo de ecuaciones estructurales, modelos de regresión and análisis cluster de empresas.

Que permite verificar la identificación de seis variables latentes and sus relaciones, así como identificar cuatro niveles de internacionalización a partir de la estructura de variables latentes identificadas.

Se trata de la primera aplicación de esta reciente and sofisticada técnica estadística al ámbito de la medición del nivel de internacionalización empresarial, especialmente indicada en el ámbito latinoamericano, donde un creciente número de empresas están avanzando en su proceso de expansión internacional.

This paper aims to identify distinct consumption patterns among Asian consumers and examine how these relate to cultural antecedents and key human values.

This study uses a large, representative sample of almost 7,000 Asian consumers in 10 culturally varying markets, using latent profile analysis (LPA) to identify the consumption profiles.

The findings empirically demonstrate that the two profiles are “inner-directed nationalistic frugals” (IDNF) and “outer-directed self-eekers” (ODSS). IDNF consumers spend more time and money on education and prefer ethnocentric consumption. ODSS consumers emphasize individuality, self-expression, seeking novelty and impressing others. Consumers with more collective values in Schwartz's typology tend to demonstrate the IDNF pattern; those with more individualistic values demonstrate the ODSS pattern. The distribution of IDNF and ODSS profiles is influenced by demographics, religion and geographical region: IDNF is greater than ODSS in Southeast Asia; ODSS is greater than IDNF in East Asia; IDNF is roughly equal to ODSS in Northeast Asia. IDNF tends to be found among older and more religious consumers, while ODSS is the opposite. Importantly, in the more religious Southeast Asian countries, even younger consumers are more IDNF than ODSS.

This research uses an exploratory and discovery-oriented approach; future research can use more confirmatory approaches to systematically examine the relationship between cultural dimensions (e.g. individualism-collectivism) and consumption patterns.

For their brands to grow in Asian markets, marketing practitioners are advised to use multiple brands to segment Asian consumers based on their values, demographics, geographical location and what religious/faith traditions they follow.

This is the first paper to identify consumption profiles in Asian markets using LPA without prior conceptual biases and relate them to cultural values and demographic variables.

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 this study is to identify latent profiles based on 3 dimensions of organizational commitment (OC) using 14 Korean samples.

This study used unpublished raw data. Allen and Meyer’s (1996) measurement was used to maintain consistency and facilitate comparison across samples. To determine the number of latent profiles that exist in Korea based on employees’ level of OC, a latent profile analysis (LPA) was performed.

The findings showed that there are two or three profiles (i.e. fully committed, weakly committed and/or moderately committed) across various types of organizations in Korea. These findings are similar to those based on LPA conducted in the West. The number of profiles is more limited in Korean samples than it is in Western samples. Like Western studies, this Korean-focused study identified fully committed, weakly committed and moderately committed profiles, but it did not show any other types of profiles found in the Western sample.

Based on these results, this study suggests academic and practical implications for researchers and practitioners.

The purpose of this paper is to identify different job types in the Belgian electricity sector and their relations with employee outcomes such as work engagement and innovative work behaviour (IWB).

This paper uses a combination of latent profile analysis and relative operating characteristics (ROC) analysis.

Depending on the job resources and demands, five different job types are identified corresponding largely to the Karasek and Theorell (1990) job types. Their relation with the outcomes is not parallel with low-strain jobs performing best for work engagement, and active jobs for IWB.

The combination of methods used in this study increases significantly the ease of communication of the findings, yet an external benchmark for the ROC analysis would be preferable.

To foster engagement and IWB with employees one should focus on the job content and only increase demands if they are combined with sufficient resources.

This research is the first in its kind that relates latent job types with different employee outcomes using a combination of latent profile and ROC analysis.

This study aims to identify and validate the different clusters of wine consumers in India based on the wine-related lifestyle (WRL) instrument. It also investigates how the identified clusters differ in terms of socio-demographic characteristics, such as age, gender, income, education, employment and marital status.

The authors conducted a survey using a structured questionnaire to collect data from wine consumers in India. The number of participants totalled to 432. The authors first identified the clusters using latent profile analysis. The authors then used the decision tree analysis based on a recursive partitioning algorithm to validate the clusters. Finally, the authors analysed the relationship between the identified clusters and socio-demographic characteristics using correspondence analysis.

Three distinct segments emerged after data were subjected to latent profile analysis, namely, curious, ritualistic and casual. The authors found that the curious cluster had a high mean score for situational and social consumption while the ritualistic cluster had a high mean for ritualistic consumption. The findings also suggest that the casual cluster had more female wine consumers.

This study makes methodological contributions to the wine consumer segmentation approach. First, it adopts a latent profile analysis to profile Indian wine consumers. Second, it validates the obtained clusters using the decision tree analysis method. Third, it analyses the relationship between the identified clusters and socio-demographic variables using correspondence analysis, a technique far superior to the Chi-square methods.

While it is incontestable that eating in restaurants leads to a higher energy intake than eating at home, this paper explores the even more environmentally relevant connection between meat intake and the location of eating.

Based on secondary data from the US National Health and Nutrition Examination Survey (NHANES), the authors apply a latent class model (LCM), combining latent profile analysis (LPA) and regression analysis. Different (latent) consumer classes are modeled based (1) on share of meat consumption and (2) share of eating out by means of LPA, while class-specific socio-demographic characteristics are estimated by means of ordered logistic regression.

Results of the LPA reveal four (latent) consumer classes with regard to the share of meat consumption and the share of eating out. One class consists mostly of male meat lovers with a high share of eating out, which, however, only represents 7% of the sample. A much larger class represents an affluent social group that consumes the majority of food outside of the home but does not consume significantly more meat than the large group of moderates who mostly eat at home. The fourth class mostly consists of children with a very low intake of meat.

By applying a LCM, the authors shed some light on the relation between meat consumption and eating out. The authors demonstrated that commonly assumed relations, such as men eating more meat than women, do not necessarily apply. Similar findings apply to factors potentially influencing meat consumption, such as education, marital status and income.

The need for dynamic and innovative skills and the importance of resources and individuals in pursuit of new opportunities prove to be extremely vital for the higher education institutions (HEI). The purpose of this paper is to develop a taxonomy of academics from HEI, based on their individual entrepreneurial orientation (EO).

The population in study was composed of teachers and researchers from worldwide HEI. The data collection was conducted through a questionnaire sent by an e-mail and the authors used the I-ENTRE-U scale to identify entrepreneurial-oriented teachers and researchers from HEI. A Latent Profile Analysis (LPA) was conducted to identify profiles of researchers with similar values in four EO dimensions.

The study allowed the authors to identify five profiles of researchers: downers, achievers, followers, defenders and rebels.

The results can be an important starting point for other researchers and practitioners hoping to evaluate academics’ EO in a higher education sector. The taxonomy also allows wider predictions about the behaviour of the profile members of profiles and relates it with other variables such as performance. Further contributions may be added by extending the data gathering from different geographical areas and/or different academic contexts, such that future studies could apply other LPA techniques and compare the results.

Only few studies have focussed on individual EO of scientists/academics, considering different national and regional contexts. To the best of the authors’ knowledge, this is the first empirical study that develops a taxonomy of academics from HEI, based on their individual EO.

Collaboration is essential to BIM-enabled construction projects (BECPs). To facilitate collaboration, various strategies have been proposed. Usually, project team adopts single collaboration strategy for convenience. However, BIM-based collaboration is rather dynamic and exposed to a lot of barriers and risks, which easily changes collaboration conditions among partners. Therefore, better understanding of collaboration conditions in BECPs is crucial to develop flexible and suitable strategies. To fill in the gap, this study intends to identify typical collaboration profiles in BECPs and develop a profile-oriented strategy selection framework.

This study establishes a six-dimensional collaborative behavior matric based on a comprehensive literature review to profile collaboration. Then a questionnaire survey is conducted to collect the collaboration data from 162 BECPs in China. Finally, latent profile analysis (LPA) is utilized to identify the typical collaboration profiles in BECPs.

Through LPA, four profiles are identified, including restricted collaboration profile, smarmy collaboration profile, intuitive collaboration profile, and modest collaboration profile. A profile-oriented strategy selection framework is also established to match strategies with collaboration profiles. Two major categories of strategies are proposed to promote these collaboration profiles including trust building and elevating strategies and collaborative behaviors facilitating strategies.

These identified profiles can provide an insightful understanding on the diversified collaboration conditions in BECPs.

The profile-oriented strategy selection framework will help managers develop strategies flexibly to promote collaboration in BECPs.

This study validates four typical collaboration profiles in BECPs. Moreover, a profile-oriented strategy selection framework is also established for collaboration facilitation in BECPs.