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This paper analyses how the purchase channel and customer complaint goals affect the sequential choice of post–purchase complaint channels when customers experience a service failure followed by a service recovery failure (double deviation).

An online survey involving a scenario manipulation was conducted with 577 apparel shoppers. The study employs multi-group latent class analysis to estimate latent customer segments within both online and offline groups of shoppers and compare latent classes between the two groups.

The results show that the purchase channel has a lock-in effect on the complaint channel, which is stronger for offline buyers. Moreover, there is evidence of channel synergy effects in the case of having to complain twice: shoppers who complain in store in the first attempt turn to online channels in the second complaint attempt, and vice versa. Complaint goals shape the choice of complaint channels and define different shopper segments.

The present study is the first to adopt a cross-stage approach that analyses the dependencies between the purchase channel and the complaint channel used on two subsequent occasions: the first complaint after a service failure and the second following a service recovery failure.

The purpose of this study is to explore how consumers apply clean-eating criteria to a range of food characteristics, and the extent to which individuals are consistent in how they apply clean-eating criteria across products. Further, this study investigates how the clean-eating approach relates to underlying food choice motives.

Data were collected in a consumer survey (n = 666) in Sweden, where participants were prompted about the importance of a set of intrinsic food attributes of the “free-from” and “added” types, for three different food product types (bread, processed meat, ready meals). Data were analyzed using latent class cluster analysis, to explore segments of consumers that place similar importance to the food characteristics and hold similar food choice motives.

Clean eating can be described by two distinctly different attainment strategies: avoiding undesirable characteristics or by simultaneously approaching desirable characteristics. Notably, individuals who apply clean-eating criteria in their food choices strive for healthy, natural and environmentally friendly food, but the clean-by-approach strategy implies a stronger focus on personal health in the form of weight control.

While claims and labels on food packages concerning clean eating are implemented by food manufacturers, it remains unregulated. This study provides information for future regulations on how consumers apply clean-eating criteria, and their motives thereof. Further, the results provide insights food manufacturers regarding motives for clean eating in different consumer segments.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

This study aims to advance the understanding on individual variations in PhD candidates’ personal interest in their doctorate and supervisory and research community support, and several individual and structural attributes potentially having an impact on the profiles.

The authors explored the interrelationship between personal interest – social support profiles, and nationality, gender, research group and study status and the risk of dropping out. A total of 768 PhD candidates from a research-intensive university in Finland responded to a modified version of the cross-cultural doctoral experience survey. Latent profile analysis was used to explore the individual variations in PhD candidates’ interest and support from the supervisor and research community.

Three distinctive PhD interest-social support profiles were detected; the high interest–high support profile (74.4%, n = 570), the high interest–moderate support profile (18.2%, n = 140) and the moderate interest–moderate support profile (7.4%, n = 56). The profiles exhibited high to moderate levels of research, development and instrumental interest. Individuals in the high interest–moderate support and in the moderate interest–moderate support profiles were more prone to consider dropping out from their PhD than in the high interest–high support profile.

The results indicate that by cultivating PhD candidates’ interest and providing sufficient supervisory and the research community offers a means for preventing candidates from discontinuing their doctorate. Hence, building a supportive learning environment that cultivates a PhD candidate’s personal interest is likely to reduce high dropout rates among the candidates.

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.

This paper focuses on patterns of film consumption within cultural consumption more broadly to assess trends in consumerism such as eclectic consumption, individualised consumption and omnivorous/univorous consumption and whether economic background and status feature in shaping cultural consumption. We focus on film because it is widely consumed, online and offline, and has many genres that vary in terms of perceived artistic and entertainment value. In broad terms, film is differentiated between mainstream commercially driven film such as Hollywood blockbusters, middlebrow “feel good” movies and independent arthouse and foreign language film. Our empirical statistical analysis shows that film consumers watch a wide range of genres. However, films deemed to hold artistic value such as arthouse and foreign language feature as part of broad and wide-ranging pattern of consumption of film that attracts its own dedicated consumers. Though we found that social and economic factors remain predictors of cultural consumption the overall picture is more complex than a simple direct correspondence and perceptions of other cultural forms also play a role. Those likely to consume arthouse and foreign language film consume other film genres and other cultural forms genres and those who “prefer” arthouse and foreign language film have slightly more constrained socio-economic characteristics. Overall, we find that economic and cultural factors such income, education, and wider consumption of culture are significant in patterns of film consumption.

The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge.

In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized.

The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences.

The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.