Preference Segments Research Articles

Consumer perception of restaurant and ready-to-eat cheese dips.

Cheese dips are an expanding category sold as ready to eat (RTE) in grocery stores or served hot in restaurants (RST). The purpose of this study was to determine key consumer attributes for cheese dips and evaluate if key drivers of purchase for cheese dips were distinct between grocery store or restaurant purchase. An online survey (n = 931) was conducted. Participants were asked two different sets of questions based on the location they most frequently purchased and consumed cheese dip in the past 6 months, at a restaurant (n = 480) or from a grocery store (n = 451). Consumers first evaluated psychographic and agree/disagree questions regarding cheese dip and then completed maximum difference exercises focused on color and other extrinsic cheese dip attributes. Finally, an adaptive choice-based conjoint was used to determine the relative importance of cheese dip attributes. Clustering of conjoint utility scores revealed differences in preference for spiciness, but similar preferences for other attributes within both consumer groups. RTE and RST consumers indicated that their ideal cheese dip was white in color, moderately thick, and of medium spiciness with small visible pepper pieces and jalapeno pepper flavor. For both consumer groups, spiciness was the most important characteristic of cheese dips, followed by package for RTE consumers and pepper flavor and consistency for RST consumers. Regardless of consumption context, consumers have similar ideal characteristics for cheese dips. PRACTICAL APPLICATION: The primary drivers of purchase for cheese dip consumers are similar, regardless of context. Segmentation of consumer preferences reveals opportunities for product innovation. The data collected will aid in product development of cheese dips that better meet the needs of consumers.

Involuntary, Limited, and Contiguously Repeating Musical Imagery (InLaCReMI): Reconciling Theory and Data on the Musical Material Acquired by Earworms

“Earworms” have been proposed as a particular type of involuntary musical imagery (INMI) where musical material is repeated in the mind. The structure of the repetition is investigated by proposing a spreading activation model (SAM), where mental experience consists of priming and activation of nodes that represent objects, events, and relationships, including music. Music consists of chaining together nodes representing small music segments within hierarchical structures. Listening to music at a point in time activates the music's represented segment, which then primes the node representing the segment that follows. Repeating musical segments are coded recursively, with an additional layer of “context” nodes tracking global, structural location. From this basis, two hypotheses were proposed: H1 “Contiguous repetition at encoding” and H2 “Low environmental focus.” H1 predicts that when an INMI episode is a contiguously repeating segment, it must be based on music that contains contiguous repetition: it will be perceived as a subset of INMI – involuntary, limited, and contiguously repeating musical imagery (InLaCReMI). H1 challenges current views about preferred segments for looping, such as the “hook” of a tune. H2 predicts that InLaCReMI occurs when an individual is not focused on the immediate environment. In such a state there is less social imperative to activate high attentional-demand contextual information and so adherence to contextual integrity in thought is relaxed, leading to looping of recurrently activated nodes that were encoded with contiguous repetition. Additional predictions were made using SAM, demonstrating the potential for SAM to provide a unifying understanding of INMI. InLaCReMI is proposed as a frequently occurring species of INMI and confirmation of this phenomenon through more structured empirical investigation will provide novel insights into mental operation, and the nature of INMI.

Consumer Preference Segments for Plant-Based Foods: The Role of Product Category.

A survey of willingness to consume (WTC) 5 types of plant-based (PB) food was conducted in USA, Australia, Singapore and India (n = 2494). In addition to WTC, emotional, conceptual and situational use characterizations were obtained. Results showed a number of distinct clusters of consumers with different patterns of WTC for PB foods within different food categories. A large group of consumers did not discriminate among PB foods across the various food categories. Six smaller, but distinct clusters of consumers had specific patterns of WTC across the examined food categories. In general, PB Milk and, to a much lesser extent, PB Cheese had highest WTC ratings. PB Fish had the lowest WTC, and two PB meat products had intermediate WTC. Emotional, conceptual and situational use characterizations exerted significant lifts/penalties on WTC. No penalty or lifts were imparted on WTC by the situational use of ‘moving my diet in a sustainable direction’, whereas uses related to ‘when I want something I like’ and ‘when I want something healthy’ generally imparted WTC lifts across clusters and food categories. The importance of this research for the study of PB foods is its demonstration that consumers are not monolithic in their willingness to consume these foods and that WTC is often a function of the food category of the PB food.

Segmentation of Consumer Preferences for Vegetables Produced in Areas Depressed by Drought

The megadrought in Chile’s north and central zones has impacted the horticultural production of family farming. One way to mitigate these effects is by adding value to vegetables. However, no studies show the main attributes consumers value of vegetables produced in the megadrought zone. The study’s objective was to evaluate consumer preferences and identify segments based on the extrinsic attributes of vegetables grown in areas depressed by drought. We surveyed 946 vegetable buyers from the Antofagasta, Valparaiso, and Maule regions. Through the conjoint analysis technique, we identified that the main attributes preferred by consumers were presentation (45.3%) and type of vegetables (21.8%), followed by labeling (15.9%), producers (10.3%), and origin (6.7%). The cluster analysis revealed the existence of three segments. The largest segment is motivated by the way vegetables are presented (49.7%), followed by a second segment that values multi-attributes (31.9%) and a smaller segment that is interested in labeling (18.4%). These findings can help position these products in the market and raise awareness of family farming and the economic and production problems that they currently face.

Efficient Johnson-SB Mixture Model for Segmentation of CT Liver Image.

To overcome the problem that the traditional Gaussian mixture model (GMM) cannot well describe the skewness distribution of the gray-level histogram of a liver CT slice, we propose a novel segmentation method for liver CT images by introducing the Johnson-SB mixture model (JSBMM). The Johnson-SB model not only has a flexible asymmetrical distribution but also covers a variety of other distributions as well. In this article, the parameter optimization formulas for JSBMM were derived by employing the expectation-maximization (EM) algorithm and maximum likelihood. The implementation process of the JSBMM-based segmentation algorithm is provided in detail. To make better use of the skewness of Johnson-SB and improve the segmentation accuracy, we devise an idea to divide the histogram into two parts and calculate the segmentation threshold for each part, respectively, which is called JSBMM-TDH. By analyzing and comparing the segmentation thresholds with different cluster numbers, it is illustrated that the segmentation threshold of JSBMM-TDH will tend to be stable with the increasing of cluster number, while that of GMM is sensitive to different cluster numbers. The proposed JSBMM-TDH is applied to segment four randomly obtained abdominal CT image sequences, and the segmentation results and robustness have been compared between JSBMM-TDH and GMM. It is verified that JSBMM-TDH has preferable segmentation results and better robustness than GMM for the segmentation of liver CT images.

Plant-based alternatives vs dairy milk: Consumer segments and their sensory, emotional, cognitive and situational use responses to tasted products

Plant-based alternatives to dairy milk are becoming more popular in many parts of the world. The present research explored this product category using a multi-variable research strategy where consumers from New Zealand (n = 345) tasted samples for degree of liking/disliking and described their perceived sensory, emotional/cognitive and situational use characteristics using check-all-that-apply (CATA) questions. A total of 10 products were included in the study, three of which were dairy-based (cow’s milk, two at 3.4% fat and one at 0.1% fat), while the remaining seven products were a blend of three plant-based ingredients (coconut, rice and oats) or made from either soy, oat, rice or cashew nuts (including variations for sweetened/unsweetened products and reduced-fat products). Across all participants, the full-fat dairy milks were most liked, but heterogeneity was apparent, especially for the plant-based alternatives. Four consumer segments were identified that fell along a continuum from those people who exclusively liked full-fat dairy products (n = 107) to those who preferred a wide range of plant-based products (n = 111), with two smaller intermediary clusters, Dairy and tri-blend Likers (n = 55) and Full-fat dairy / tri-blend / soy Likers (n = 72). Each cluster had distinct and characterising sensory, emotional/cognitive and situational uses for the tested dairy and plant-based beverages, and penalty/lift analysis was used to determine how these positively or negatively impacted product liking. The penalty/lift analysis was supplemented with a test that for each CATA term (sensory, emotional/cognitive, situational use) enabled a comparison of the four clusters to understand for which of these the effect on liking scores was larger/smaller. Overall, the present research contributed new knowledge for plant-based beverages (which remain understudied relative to plant-based meat alternatives) by providing detailed product insights into a broad range of tasted samples, showing that complex consumer preference segmentation exists, driven by a combination of sensory, emotional/cognitive, and situational use perceptions.

Research on the enterprise online marketing mode based on data mining

With the development and popularity of mobile networks, online shopping has gradually become a trend. For enterprises, the traditional marketing mode has been difficult to play an effective role when facing the emerging online shopping mode. This study aims to improve the revenue benefits of online shopping. This paper first introduces the traditional marketing mode and then selects the data mining model used for consumption preference segmentation to build an online marketing mode. An example analysis was conducted on a book sales company and a real estate company. The results showed that more users in this community preferred five types of books, and the percentages from high to low were teaching and learning materials, modern novels, popular science books, historical literature, and classical novels; more customers preferred online platforms among the channels for collecting information on home purchase. No matter it was the book sales company or the real estate company, compared with no fluctuation in the company’s turnover under the traditional marketing mode, the turnover of the company increased month by month after adopting the online marketing mode.

Optimizing urban electric vehicle incentive policy mixes in China: Perspective of residential preference heterogeneity

The large-scale diffusion of electric vehicles (EVs) helps pave the way towards carbon peak and neutrality goals, while it is affected by the governmental incentive policies at different levels. This paper studies the optimization of demand-side policy mixes from the perspective of residential preference heterogeneity with 18 EV pilot cities in China as the case. A latent class model is used to investigate urban residents’ preferences for demand-side policies and characterization of urban heterogeneities of the preferences. Valid survey data from 1455 respondents were collected from a stated preference experiment, which segmented urban residents into a “policy-sensitive” group (8.25% of the sample), “policy-indifferent” group (27.97%), and “policy-cognitive” group according to their preferences. The 18 pilot cities were grouped into three clusters according to the different compositions of the preference segments. Our analysis shows that it may not be necessary for the municipal governments to provide subsidies for the installations of home chargers currently. Besides, different optimized policy mixes of bus lane access privileges, replacement subsidies, toll discounts, and preferential parking fees for charging were proposed for the three clusters of cities. Our research paradigm applies to cities in other countries that need to optimize EV policy mixes.

How Demographic Factors Impact Consumers’ Product Choice During Online Shopping : An Empirical Study of Tier-III Markets

E-commerce is proliferating in India, and not just the tier-I cities but the tier-II and III cities are also contributing significantly to the total e-commerce sales in India. Hence, to study this upcoming market, this study tried to fill the gap in the area of the tier-III markets and product category analysis during online shopping. For this study, six tier-III cities from Maharashtra were selected, and 537 sample responses were collected from these cities in the year 2019. A questionnaire was used for data collection, and stratified random sampling technique was used to select the respondents. The collected data were analyzed using SPSS.16.0, and various statistical tests such as Cochran’s Q and crosstabulation analysis were used to conduct the analysis. The results suggested that consumers from this upcoming market engaged in online shopping, and mobile phones and related accessories and apparel and accessories were the most preferred segments for online shopping. The results also revealed that various demographic factors such as gender, age, marital status, profession, and income impacted consumers’ product choice during online shopping as for these factors p, Phi (Φ), and Cramer’s V(φc) values were reported to be significant. This research contributes to the existing literature related to online consumer buying behavior and can also help the marketers to select the right segment to design their marketing and distribution strategies specifically for the tier-III market.

Choosing the Optimal Segmentation Technique to Understand Tourist Behaviour

This study compares and contrasts five different segmentation techniques to determine which one(s) best describe behaviour among independent tourists who visited Bali, Indonesia. The five techniques included geographic, demographic, behavioural and motivation segmentation, plus a hybrid technique that included geographic, demographic and behavioural variables. The study revealed geographic segmentation proved to be the most efficacious, with motivation segmentation least reliable. However, the authors caution that the preferred segmentation technique depends on the objectives of the study, with each method adopted serving a specific purpose.

Last Teen Pixels for Arabic Font Size and Style Recognition

Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.

Predicting Work-Life Conflict: Types and Levels of Enacted and Preferred Work-Nonwork Boundary (In)Congruence and Perceived Boundary Control.

In 2020, everyday life changed dramatically for employees worldwide as a result of the outbreak of the Covid-19 pandemic, where an estimated 558 million employees started working from home. The pandemic, therefore, marks a fundamental shift of individuals’ work-nonwork boundaries, which can impact work-life conflict. In particular, the interplay between individuals’ enacted boundaries (degree to which they separate/segment or blend/integrate work-nonwork), preferred boundaries (degree of preferred segmentation or integration of work-nonwork), and perceived control over work-nonwork boundaries, may relate to work-life conflict. This study, the first to the best of our knowledge, examines whether different types and levels of work-nonwork boundary (in)congruence matter for work-life conflict, and whether perceived boundary control moderates these relationships. Boundary (in)congruence represents the degree of (mis)fit between enacted and preferred segmentation or integration. Several types of (in)congruence are distinguished: “segmentation congruence” (enacting and preferring segmentation); “integration congruence” (enacting and preferring integration); “intrusion” (enacting integration but preferring segmentation) and “distance” (enacting segmentation but preferring integration). Data from 1,229 managers working in public and private organizations in Sweden was analyzed using polynomial regression analysis with response surface modeling and moderation analysis in SPSS Process. Findings showed that “integration congruence” was related with higher work-life conflict than “segmentation congruence.” Moreover, a U-shaped relationship between incongruence and work-life conflict was found: the more incongruence, the more work-life conflict. Specifically, “intrusion” was related to higher work-life conflict than “distance.” Finally, boundary control mitigated the effect of incongruence (especially “intrusion”) on work-life conflict. From our findings, we may conclude that work-life conflict is impacted differently depending on the type and level of boundary (in)congruence. Particularly enacted and/or preferred integration may be problematic when it comes to work-life conflict, rather than just (in)congruence per se. Moreover, boundary control can be viewed as a key factor in combating work-life conflict, especially among individuals who enact integration, but prefer segmentation. Taken together, our study contributes new and substantial knowledge by showing the importance for research and HRM-policies that take into account different types and levels of boundary (in)congruence, as these are associated with different levels of work-life conflict, which, in turn, are moderated by boundary control.

Identifying latent shared mobility preference segments in low-income communities: Ride-hailing, fixed-route bus, and mobility-on-demand transit

Concepts of Mobility-on-Demand (MOD) and Mobility as a Service (MaaS), which feature the integration of various shared-use mobility options, have gained widespread popularity in recent years. While these concepts promise great benefits to travelers, their heavy reliance on technology raises equity concerns as socially disadvantaged population groups can be left out in an era of on-demand mobility. This paper investigates the potential uptake of MOD transit services (integrated fixed-route and on-demand services) among travelers living in low-income communities. Specially, we analyze people's latent attitude towards three shared-use mobility services, including ride-hailing services, fixed-route transit, and MOD transit. We conduct a latent class cluster analysis of 825 survey respondents sampled from low-income neighborhoods in Detroit and Ypsilanti, Michigan. We identified three latent segments: shared-mode enthusiast, shared-mode opponent, and fixed-route transit loyalist. People from the shared-mode enthusiast segment often use ride-hailing services and live in areas with poor transit access, and they are likely to be the early adopters of MOD transit services. The shared-mode opponent segment mainly includes vehicle owners who lack interests in shared mobility options. The fixed-route transit loyalist segment includes a considerable share of low-income individuals who face technological barriers to use the MOD transit. We also find that males, college graduates, car owners, people with a mobile data plan, and people living in poor-transit-access areas have a higher level of preferences for MOD transit services. We conclude with policy recommendations for developing more accessible and equitable MOD transit services.

Preferences for COVID-19 vaccine distribution strategies in the US: A discrete choice survey.

BackgroundThe COVID-19 vaccination campaign in the US has been immensely successful in vaccinating those who are receptive, further increases in vaccination rates however will require more innovative approaches to reach those who remain hesitant. Developing vaccination strategies that are modelled on what people want could further increase uptake.Methods and findingsTo inform COVID-19 vaccine distribution strategies that are aligned with public preferences we conducted a discrete choice experiment among the US public (N = 2,895) between March 15 to March 22, 2021. We applied sampling weights, evaluated mean preferences using mixed logit models, and identified latent class preference subgroups. On average, the public prioritized ease, preferring single to two dose vaccinations (mean preference: -0.29; 95%CI: -0.37 to -0.20), vaccinating once rather than annually (mean preference: -0.79; 95%CI: -0.89 to -0.70) and reducing waiting times at vaccination sites. Vaccine enforcement reduced overall vaccine acceptance (mean preference -0.20; 95%CI: -0.30 to -0.10), with a trend of increasing resistance to enforcement with increasing vaccine hesitancy. Latent class analysis identified four distinct preference phenotypes: the first prioritized inherent “vaccine features” (46.1%), the second were concerned about vaccine “service delivery” (8.8%), a third group desired “social proof” of vaccine safety and were susceptible to enforcement (13.2%), and the fourth group were “indifferent” to vaccine and service delivery features and resisted enforcement (31.9%).ConclusionsThis study identifies several critical insights for the COVID-19 public health response. First, identifying preference segments is essential to ensure that vaccination services meet the needs of diverse population subgroups. Second, making vaccination easy and promoting autonomy by simplifying services and offering the public choices (where feasible) may increase uptake in those who remain deliberative. And, third vaccine mandates have the potential to increase vaccination rates in susceptible groups but may simultaneously promote control aversion and resistance in those who are most hesitant.

Students’ Preference Analysis on Online Learning Attributes in Industrial Engineering Education during the COVID-19 Pandemic: A Conjoint Analysis Approach for Sustainable Industrial Engineers

The decline of enrollees for industrial engineering during the COVID-19 pandemic and the increasing demand for professional industrial engineers should be explored. The purpose of this study was to determine the preference of industrial engineering students of different educational levels on online learning during the COVID-19 pandemic. Specifically, this study utilized conjoint analysis with orthogonal design considering seven attributes: delivery type, layout, term style, final requirements, Coursera requirements, seatwork and practice sets, and platforms. Among the attributes, 20 stimuli were created through SPSS and were answered voluntarily by 126 respondents utilizing a 7-point Likert Scale. The respondents were comprised of 79 undergraduate, 30 fully online master’s degree, and 17 master’s and doctorate degree students collected through purposive sampling. One university from the two available universities that offer all educational levels of IE in the Philippines was considered. The results showed that undergraduate students considered the final requirements with multiple-choice as the highest preference, followed by non-modular term style, and no seatwork and practice sets. In addition, fully online master’s degree students considered delivery type with the mix as the highest preference, followed by layout, and no seatwork and practice sets. Finally, master’s and doctorate degree students considered final requirements with publication as the highest preference, followed by no seatwork and practice sets, and mix delivery type. The students are technologically inclined, want to learn at their own pace, know where and how to get additional online learning materials, but still need the guidance of teachers/professors. The results would help contribute to the theoretical foundation for further students’ preference segmentation, specifically on online learning during the COVID-19 pandemic worldwide. Moreover, the design created could be utilized for other courses in measuring students’ preference for online learning even after the COVID-19 pandemic.

Quality Characteristics of Wheat Malts with Different Country of Origin and Their Effect on Beer Brewing

The characteristics of wheat malt, wort, and beer from two Korean wheat varieties (Keumkang and Anzunbaengi; KM and AM, respectively) were compared to those of two commercial wheat malt controls from the USA and Germany (UM and GM), to examine the feasibility of Korean wheat for brewing. The quality parameters of four wheat malts, chemical properties of wort using them, volatile compounds, sensory attributes, and consumer acceptability of four final beers (KB, AB, UB, and GB) were analysed. Additionally, the relationship of each parameter was determined through multiple factor analysis. Korean wheat malts were different from control malts in free amino nitrogen (FAN), soluble and total nitrogen, and Kolbach index. The extract (81.8–83.2%) and diastatic power (407–477 WK°) of all samples were in the brewing field’s recommended range. The FAN and yeast cell population of the wort samples were similar during fermentation except on the initial day, which showed a high FAN and yeast cell population for KM wort. Eleven volatile compounds with variable importance in projection scores above 1.0 were responsible for discrimination of the beer samples by partial least squares discriminant analysis. The four beer samples’ overall acceptance scores were not significantly different. However, their acceptance trends were different depending on consumer preference segments by agglomerative hierarchical clustering analysis, implying the need for product development depending on the target consumer. Regarding sensory attributes, KB had a less fruity flavour than the other beer samples ( p < 0.05 ) and sweetness of AB tended to be higher than the others. An association plot based on multiple factor analysis of the wheat beer samples, volatile compounds, sensory properties, and overall acceptance showed that sweetness, alcohol aroma, and fruity flavour were closely associated with AB and overall acceptance, while 2,3-butanediol and turbidity were placed opposite side of there.

Does Controlling for Scale Heterogeneity Better Explain Respondents' Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand.

Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents' randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

In Vitro Reassortment between Endemic Bluetongue Viruses Features Global Shifts in Segment Frequencies and Preferred Segment Combinations.

Bluetongue virus (BTV) is an arthropod-borne pathogen that is associated with sometimes severe disease in both domestic and wild ruminants. Predominantly transmitted by Culicoides spp. biting midges, BTV is composed of a segmented, double-stranded RNA genome. Vector expansion and viral genetic changes, such as reassortment between BTV strains, have been implicated as potential drivers of ongoing BTV expansion into previously BTV-free regions. We used an in vitro system to investigate the extent and flexibility of reassortment that can occur between two BTV strains that are considered enzootic to the USA, BTV-2 and BTV-10. Whole genome sequencing (WGS) was coupled with plaque isolation and a novel, amplicon-based sequencing approach to quantitate the viral genetic diversity generated across multiple generations of in vitro propagation. We found that BTV-2 and BTV-10 were able to reassort across multiple segments, but that a preferred BTV-2 viral backbone emerged in later passages and that certain segments were more likely to be found in reassortant progeny. Our findings indicate that there may be preferred segment combinations that emerge during BTV reassortment. Moreover, our work demonstrates the usefulness of WGS and amplicon-based sequencing approaches to improve understanding of the dynamics of reassortment among segmented viruses such as BTV.

Segmentation of passenger preferences for using digital technologies at airports in Norway

Airports are increasingly focused on implementing digital technologies at key stages of the airport journey to enhance the passenger experience. However, passengers have different preferences. TwoStep Cluster Analysis is used to reveal the presence of distinct segments according to their preferences. The findings are based on a survey of 6082 passengers at airports in Norway. Three distinct segments are identified: those that prefer traditional manual processes, those that prefer automated technology-based processes, and those that prefer more personalised technology-based processes. Significant differences are revealed for each segment according to passenger and trip characteristics and opinions regarding how the use of digital technologies at airports impact on personal privacy and human dignity. The findings contribute to knowledge on passenger preferences at airports and can help to inform airport decision making.

Industrial applications of selected JFS articles

Industrial applications of selected <i>JFS</i> articles