A combinatorial constraint satisfaction problem dichotomy classification conjecture

Neš,etř,il, Jaroslav,Siggers, Mark H.,Zá,dori, Lá,szló Elsevier 2010 European journal of combinatorics : Journal europ& Vol.31 No.1

<P><B>Abstract</B></P><P>We further generalise a construction–the <I>fibre construction</I>–that was developed in an earlier paper of the first two authors. The extension in this paper gives a polynomial-time reduction of CSP(H) for any relational system H to CSP(P) for any relational system P that meets a certain technical partition condition, that of being <SUB>K3</SUB>-<I>partitionable</I>.</P><P>Moreover, we define an equivalent condition on P, that of being <I>block projective</I>, and using this show that our construction proves NP-completeness for exactly those CSPs that are conjectured to be NP-complete by the CSP dichotomy classification conjecture made by Bulatov, Jeavons and Krohkin, and by Larose and Zádori. We thus provide two new combinatorial versions of the CSP dichotomy classification conjecture.</P><P>As with our previous version of the fibre construction, we are able to address restricted versions of the dichotomy conjecture. In particular, we reduce the Feder–Hell–Huang conjecture to the CSP dichotomy classification conjecture, and we prove the Kostochka–Nešetřil–Smolíková conjecture. Although these results were proved independently by Jonsson et al. and Kun respectively, we give different, shorter, proofs.</P>

간호교육에 있어서의 Team Teaching에 대하여

Doris A. Geitgey,이경자 대한간호협회 1967 대한간호 Vol.6 No.6

주제강연 : 21세기 체육과 스포츠의 위상과 과제

DORIS R . CORBETT 한국체육학회 1993 국제스포츠 학술대회 Vol.1993 No.-

ONLINE COLLABORATIVE CONSUMPTION FOR FASHION GOODS AMONG GEN Y - A QUANTITATIVE APPROACH

Doris Berger-Grabner,Marion Fett 글로벌지식마케팅경영학회 2017 Global Fashion Management Conference Vol.2017 No.07

Initial situation and Problem Statement The world faces extraordinary challenges relating to the environment and society. Rapidly increasing demand is colliding with declining resources, the awareness of the need for environmental and social sustainability has grown (Martin & Schouten, 2014, p. 20). Due to these facts collaborative consumption has emerged and has disrupted various established industries all over the world. People’s attitude towards ownership and the way societies consume are changing (Chen, 2009, p. 926). Today, consumers are willing to pay for using or accessing a product rather than buying or owning it (Chen, 2009, p. 926). Gradually, consumers are turning their backs on the traditional consumer-oriented paradigm and over-consumption and are progressively looking for ways which downshift or simplify their lives (Albinsson, Wolf, & Kopf, 2010, p. 414). Collaborative consumption is driven by a variety of factors including the global recession, anti-hyper-consumerism, cost-consciousness as well as awareness of the need for a waste-reducing and sustainable living (Gansky, 2010, p. 16). The main drivers are technological advances, such as the internet and social media, which allow new ways of accessing and sharing. By using information technology, products and services can be reused, distributed and shared at the right time and location to the right customer (Gansky, 2010, p. 16). The most successful field of collaborative consumption is the one of tangible assets, such as clothing, which are not used to their full potential by their owners and can be therefore temporarily shared (Botsman & Rogers, 2010, p. XVI). Within the fashion industry many collaborative consumption concepts are developing and thriving (Pedersen & Netter, 2015, p. 259). Generation Y is interested in collaborative consumption and can identify with this phenomenon as it fits their increasingly ideological lifestyles (H?flehner, 2015). According to a study published by Mindshare in November 2015, 44 % of persons of Generation Y in Austria are aware of the prominent online collaborative consumption platforms and one out of ten has already participated in a fashion-sharing activity (Mindshare, 2015). In 2014, 8.5 billion euros were spent on clothing (Statista, 2016), whereas almost 60 % consisted of fast fashion items (?kosoziales Forum, 2013). On average, a person has 90 clothing items in their wardrobe, and more than half, is not used at all any more. Moreover, the average item is worn for about a month before its usage drops significantly (Threadflip, 2014) or the item gets disposed of in spite of still being in good condition (Shephard & Pookulangara, 2014, p. 11). Online collaborative consumption for fashion can help expand products’ life-cycles, as unneeded or unwanted fashion items can be easily rented, lent, given or sold to other consumers by using various online platforms available (Fletcher & Grose, 2012, p. 88). However, hardly any research was found that specifically focus on online collaborative consumption for Gen Y and examine the influencing factors Research Aim and Research Methods The aim of this study was to find out about the determinants influencing the intention to participate in online collaborative consumption for fashion items among Gen Y taking as an example young adults in Austria. Additionally, the factors, which can increase participation should be identified. In order to find out the influencing determinants, the Unified Theory of Acceptance and Use of Technology 2 of Venkatesh et al. (2012) will serve as the basis for the applied research construct. The following research questions are to be answered in this study: RQ: Which factors have an influence on consumer's intention to participate in online collaborative consumption for fashion among Generation Y in Austria? SubQ: Which measures help Generation Y in Austria to be more motivated to participate in online collaborative consumption for fashion?. In order to be able to provide answers to the research questions a quantitative study in the form of an online questionnaire (n= 219) among respondents of Gen Y in Austria was carried out. A number of research hypotheses have been developed in order to identify a possible influence of several variables on the intention to participate in online collaborative consumption for fashion items, mainly focusing on the Theory of Planned Behaviour (Ajzen, 1991) and on the Theory of Acceptance and Use of Technology 2 of Venkatesh et al. (2012). Theoretical Background Theory of Planned Behaviour by Ajzen (1991) The Theory of Planned Behaviour (TPB) was presented by Ajzen in 1991 as an extension of the Theory of Reasoned Action (TRA) by Ajzen and Fishbein, introduced in 1975 (Teo & Lee, 2010, p. 60). Both models are part of the multiattribute models. The theory of reasoned action tries to offer an explanation for a performed behaviour. The idea behind the theory is that behaviour is performed due to an intention to perform that behaviour. The intention is influenced by the subjective norm and the attitude towards the behaviour. The TRA thereby attempts to predict the possibility of the occurrence of a specific behaviour (Schwenkert, 2006, p. 27). Although it can be said that no behaviour can be predicted with a 100% certainty there are several studies that unveil that the intention to perform a behaviour makes a significant contribution to the actual performance. The intention is the motivational factor behind the behaviour. It indicates “how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behaviour” (Ajzen, 1991, p. 180). The main variables of the model are the Subjective Norm and the Attitude towards the Behaviour. Subjective Norm (SN) can be described as the assumption of the individual that people important to the individual are expecting the behaviour to be performed. In other words, that people close to the individual think that the behaviour should be performed by him or her (Schwenkert, 2006, pp. 27-28; Teo & Lee, 2010, p. 61). This belief somehow conveys a sense of unconscious social pressure for the individual – the pressure to perform as the norm demands. Some researchers have found that the social norm does not have as much influence on the intention to perform a specific behaviour as originally assumed (Li, Mizerski, Lee & Liu, 2009, p. 233). It has been found that the culture also tends to have an impact on the influence of the Subjective Norm. People living in an individualistic culture have a tendency to be not as influenced by social pressure as people living in a collectivistic culture (Li et al., 2009, p. 234). This may result in the fact that in collectivistic cultures the opinion of the group is highly important and therefore often followed. In individualistic cultures people are not that influenced by others which may explain those differences in the impact of the Subjective Norm. The TRA involves another variable – the Attitude towards the Behaviour (AtB). As the phrase already suggests, it does not describe the attitude towards an object, but the attitude towards the behaviour itself (Schwenkert, 2006, p. 27). The individual has a positive or negative feeling about the behaviour which influences the intention to perform it, is influenced. The attitude towards a specific behaviour is connected with one’s inner beliefs about the consequences of executing it. Therefore, also the possible consequences and outcomes of the behaviour are evaluated. If they are seen or predicted as positive the possibility of the intention to exert the behaviour is high, and so is the possibility of an actual performance (Li et al., 2009, p. 233; Teo & Lee, 2010, p. 61). As the Theory of Planned Behaviour (TPB) is an extension of the TRA, there has been added another variable – the Perceived Behavioural Control (PBC). The PBC points out the “perceived ease or difficulty of performing the behaviour” (Li et al., 2009, p. 234). By adding this variable Ajzen tried to include the element of uncertainty and to increase the ability to predict the behaviour. Studies show that the accuracy of prediction is higher than with the TRA but still the cognitive processes are in the foreground (Kroeber-Riel & Gr?ppel-Klein, 2013, p. 236). Unified Theory of Acceptance and Use of Technology by Venkatesh et al. (2012) The original Unified Theory of Acceptance and Use of Technology (UTAUT) was constructed by Venkatesh et al. in 2003 after reviewing eight existing popular models for IT adoption. The eight models revised were The Model of PC Utilisation, Innovation Diffusion Theory, Social Cognitive Theory (SCT), Motivational Model (MM), Theory of Reasoned Action (TRA), Technology of Acceptance Model (TAM and TAM2), Theory of Planned Behaviour (TPB) and the Combined TAM/TPB (Venkatesh, Morris, Davis, & Davis, 2003, pp. 428-433). The UTAUT was mainly created for IT corporate use. The extension, the UTAUT2, was developed in 2012 by Venkatesh et al. to make it applicable in consumer contexts and it is used to study new technology applications (Venkatesh, Thong, & Xu, 2012, p. 158). The UTAUT2 consists of seven variables that have an influence on Behavioural Intention. Behavioural Intention is a determinant for adoption of the Use Behaviour. Variables that refer to an individual’s difference, namely age, gender and experience, are used to moderate various relationships within the model. Performance Expectancy is the extent to which consumers gain benefits in their performance when using a technology. From reviewing the eight prior models, Venkatesh et al. (2003) have developed the construct Performance Expectancy, which pertains to Perceived Usefulness (TAM/TAM2), Outcome Expectations (SCT) and Extrinsic Motivation (MM), which refers to the behaviour that is driven by external rewards. According to Venkatesh et al. (2003) it is the strongest predictor of Behavioural Intention (Venkatesh, Morris, Davis, & Davis, 2003, p. 447). Effort Expectancy is the “degree of ease associated with consumers’ use of technology” (Venkatesh, Thong, & Xu, 2012, p. 159). In general, the easier a technology it is to use in the adoption phase, the more positive the attitude towards said technology (ibid.). Social Influence is defined as the degree to which consumers think it is important that others, such as family and friends, believe they should use a certain technology. This construct refers to the Subjective Norm in TRA and TBP. Prior research suggests that individuals, who are not well informed about the technology in question, are more likely to intend to use the technology, if other, well-informed, peers use said technology (Venkatesh, Morris, Davis, & Davis, 2003, p. 453). Facilitating Conditions refer to the extent to which consumers “believe that an organisational and technical infrastructure exists to support use of the system” (Venkatesh, Morris, Davis, & Davis, 2003, p. 453). Hedonic Motivation, or intrinsic motivation, refers as the enjoyment or pleasure derived from using a technology. It is shown to be an important factor in determining technology acceptance and use in consumer contexts (ibid.). Price Value is an important value in a consumer use setting as the consumer usually pays for the technology application or the use of it. The Price Value is positive and adds to explaining the Behavioural Intention to use when the benefits of using the technology perceive to outweigh the costs (ibid.). Finally, Habit is the degree to which people tend to behave automatically because of learning (Limayem, Hirt, & Cheung, 2007, p. 718). Conclusions Summary The empirical study has shown that the factors Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value and Sustainability have an influence on the Behavioural Intention to participate in online collaborative consumption for fashion. Firstly, it has been hypothesized that Performance Expectancy has an influence on the intention to use online collaborative consumption for fashion. As it turns out, the results showed no significant difference. Therefore, consumers might find other ways of acquiring clothing more appropriate and better suited. The second examined factor is Effort Expectancy, defined as how easy it is for consumers to use the technology. The results display that this factor shows a significant, positive influence on the intention to participate. This is in accordance with results of Venkatesh et al. (2012, p. 159), stating that the easier a technology is to use, the more likely is its adoption. The third factor, Social Influence, explains whether consumers feel it is important that other people, such as family and friends, think they should use a technology. As it turns out, this variable is positively and highly significantly related to the adoption of online collaborative consumption for fashion. Prior research proposes that persons, who are less informed about the technology, are more likely to use it if other influential persons use said technology (Venkatesh, Morris, Davis, & Davis, 2003, p. 453). Next, Facilitating Conditions were investigated as a potential factor influencing the intention to participate in online collaborative consumption for fashion. It was hypothesized that Facilitating Conditions, such as the access to a technological device with access to the Internet, have an influence on the adoption of online collaborative consumption. According to the results of the study, this variable has a highly significantly correlation to the outcome variable Behavioural Intention. The results also show that Hedonic Motivation is significantly and positively related to the Behavioural Intention. Thus, utilitarian and hedonic components of online consumption need to be taken in consideration, as has been shown by previous research in the context of traditional online shopping (Childers, Carr, Peck, & Carson, 2001, p. 533). The sixth investigated factor was Price Value, which is considered positive when the benefits of using the technology perceive to outweigh the costs. In this research, Price Value is a positive and highly significant predictor for the intention to use online collaborative consumption for fashion. Therefore, the outcomes of the study show that Price Value influences the intention to participate in online collaborative consumption for fashion. The next two factors of the research model, Trust and Sustainability, played a tremendous role when discussing collaborative consumption and even are considered main principles of collaborative consumption in the literature. As expected, Sustainability showed a highly significant and positive impact on Behavioural Intention. As mentioned before, this predictor accounted for 28 % of the total contribution of the research model. Trust did not significantly influence the adoption of online collaborative consumption for fashion. This result might be based on the fact that respondents feel insecure when interacting with strangers on the Internet in order to sell, buy or rent fashion items. Managerial Implications Several implications for retailers could be deduced from the results: According to this study, especially the factors Hedonic Motivation and Sustainability account each for 28 % of explaining the contribution to Behavioural Intention to use online collaborative consumption for fashion. Considering these insights, platforms dedicated to online collaborative consumption for fashion might put emphasis on these factors and increase their level of enjoyment and social interaction. For instance, platforms could boost these factors by including the usage of gamification. This way, the level of fun and pleasure for persons can be enhanced which in turn may lead to the adoption of online collaborative consumption behaviour. Additionally, in order to increase the social factor, online forums and discussion groups might not only help to express users’ experiences, reviews or interests, but also generate general social interaction, which is considered as very important for the participation in collaborative consumption. Furthermore, the study has shown that two thirds of the sample would welcome the possibility of fast fashion retailers offering online collaborative consumption activities. Since there are still a limited number of persons aware of the phenomenon of collaborative consumption, the alternative of peer-to-peer exchange in collaboration with a prominent fast fashion retail chain would be a perfect alternative. Instead of being tied to relatively unknown platforms dedicated to collaborative consumption for fashion, persons would most certainly be more aware and more willing to participate in such practices, if a well-known, established retailer would allow collaborative activities. Moreover, the aspect of sustainability seemed to be a crucial factor for the intention to use online collaborative consumption platforms for fashion. For this reason, platforms should communicate this aspect and publicise the fact that using collaborative consumption activities is an environmentally friendly, To conclude with, more than half of respondents would need more information about the topic in order to participate. Existing online platforms should invest in effective communication strategies or inexpensive guerrilla campaigns in order to attract more users. As the results of this study show, if persons are better informed and more aware of this phenomenon and its advantages, the better are the chances for adoption of collaborative consumption for fashion.

Generational Consumer Segments and Shopping Process Characteristics

Doris H,Kincade,Jihyun Kim,Fay Gibson 한국마케팅과학회 2010 Journal of Global Fashion Marketing Vol.1 No.1

Understanding consumers by examining their characteristics within segments is a key activity for business success. Many apparel businesses use this strategic tool for focusing their promotions efforts and their assortment selections on a group or segment of consumers. For practitioners and academicians, two of the largest and most intriguing consumer segments in the 2000s are Baby Boomers and Echo Boomers (i.e., Gen Ys). The Echo Boomers are the children of the Baby Boomers or the second generation of consumers following the generation of the Baby Boomers. These generational segments represent two of the most affluent consumer groups in the market place. Many retailers and academic researchers are interested in these segments, and although each segment has received some review, limited academic research has examined their apparel shopping behavior. Studies tend to be focused on one generation but not on the comparison of the two generations and their similar or different shopping activities. The purpose of this study was to examine the influences of generational consumer segments, shopping orientation, and specific product categories on the shopping process variables. Data collection resulted in 355 usable responses from Echo Boomers (ages 18-24) and 180 responses from Baby Boomers (ages 46-59). The respondents, for both generations, included three-fourths female and one-fourth male consumers. The primary occupation for Baby Boomers was listed as professionals (53%), while the second most common occupation was listed as homemaker (16%). More than 98% of the Echo Boomers were full time students. Exploratory factor analysis resulted in two shopping orientation variables (i.e., fashionista and experiential). Multiple regression analyses showed that these two orientation factors significantly explained both segments’ shopping process activities (i.e., wait time and try on). In contrast, the generational segment variable showed no significant differences for the shopping process activities. Findings from this study support the previous work place literature that notes similarities between the segments. In this study, age (i.e., generational segments) was not a significant factor in explaining selection activities (i.e., try on and wait time). This finding refutes previous studies that proclaim the differences between the Baby Boomers and the Echo Boomers and provides support for the similarities, not differences, between the two generational segments. With the similarities between generational segments being identified, the differences found with other variables are further discussed. The shopping orientation variable provided more information in explaining consumers’ selection activities than the generational segments. Regardless of age (i.e., generational segment), both Echo Boomers and Baby Boomers in this study, who scored high on the fashionista shopping orientation factor, placed less importance on try-on activities and were less willing to wait for products. The try-on activities variable was also explained by the experiential shopping orientation in comparison to its lack of differentiation with the generational segments variable. Consumers, regardless of age, who rated experiential activities as more important when shopping were the consumers who wanted to try on the products. Denim was the one product category variable, in the conceptual model, that explained try-on and wait time activities. Consumers who placed more importance on denim, specifically the fit, color and styling of blue jeans, were the consumers who were more willing to wait for products to be delivered. This research has a number of implications for practitioners and for academicians. Previous research studies in several fields have noted that consumers may react differently to various situations according to their generational segment (i.e., age grouping).

EMOTION, COMPENSATION AND CUSTOMER ENGAGEMENT: EVIDENCE FROM LUXURY HOTELS IN CHINA

Doris Chenguang Wu,Namho Chung,Zhaohan Hua,Hee Chung Chung 글로벌지식마케팅경영학회 2018 Global Marketing Conference Vol.2018 No.07

Introduction Although hotel employees are trained to deliver the best service, service failures may happen at any time because service is delivered by people to people (Susskind, 2002). Moreover, customers are more impressed by failed services than good services (Titz, 2001). According to the recovery paradox, customers have higher satisfaction level after experiencing a service failure if they receive satisfactory service recovery or compensation (McCollough & Bharadwaj, 1992). With the development of information communication technology and mobile device, customers can receive personalized services in recent days (Migacz, Zou, & Petrick, 2018). They also can easily share their experience on the online review platforms such as TripAdvisor, as well as select hotels based on shared online reviews (Liu & Park, 2015; Nieto-Gara?a, Mu?oz-Gallego, & Gonz?lez-Benito, 2017). Therefore, it is important for hotel managers to understand the mechanisms for service failure and recovery strategy. Thus, this study aims to examine the relationship between different emotion, customer engagement and brand loyalty under the context from the luxury hotels in China that different service failure compensation strategies are adopted. Particularly, the following two research questions are aimed to be addressed: First, do emotions (anger, regret and helplessness) significantly affect hotel brand loyalty through customer engagement? Second, does compensation type (immediate vs. delayed) significantly affect customer engagement and hotel brand loyalty based on customers’ emotions? The results of this study will benefit industry practitioners for formulating effective service failure recovery strategies. Theoretical frameworks and hypotheses development Stimulus-Organism-Response framework Stimulus-Organism-Response (S-O-R) framework is a commonly used form of behavioral research in which events or occurrences are said to be the result of certain stimulus leading to a certain response, following a set of organism processes (Kim & Lennon, 2013; Mehrabian & Russell, 1974). In behavioral research, the S-O-R theory explains “how” something happens and a variance theory describes “why” (Chiles, 2003). We adopted the S-O-R framework in an attempt to explain the effect of the compensation types (immediate vs. delayed) on hotel brand loyalty. In our research model, customer engagement is used an intervening construct on the causal relationship between emotions of customer (anger and regret as a retrospective emotions, helplessness as a prospective emotion) (Gelbrich, 2010) and hotel brand loyalty. Customer engagement is composed of multidimensional concepts of identification, enthusiasm, attention, absorption, and interaction (So, King, & Spark, 2014). Our model thus explains four basic processes of relationship impact on service failure as “stimulus”, emotions and customer engagement as “organism”, and hotel brand loyalty as “response”. This study also emphasizes compensation type as “moderator”. The model shows how to enhance the understanding of emotions that affect hotel brand loyalty through customer engagement based on the moderating effect of compensations type. Customer engagement It is important for a firm to manage customers to improve a firm’s performance. Customer management has transformed from customer transactions, to relationship marketing, and then engaging customers (Pansari and Kumar 2017). There are different definition about customer engagement and most of them define customer engagement as the activity of the customer toward the firm. For example, Pansari and Kumar (2017) define customer engagement as how customer contributes to the firm by “the mechanics of a customer’s value addition to the firm, either through direct or/and indirect contribution.” Vivek et al. (2012) define customer engagement as “the intensity of an individual’s offerings or organizational activities, which either the customer or the organization initiates” (p.127). It has been discussed that customer engagement has been affected by customer emotion and also has significant impact on behaviour intention and brand loyalty. However it has not been discussed under service failure context and when different types of compensation strategies are employed. This study therefore aims to explore this mechanics. Under hospitality context, So, King and Sparks (2014) develop five factors to measure customer engagement: identification, enthusiasm, attention, absorption, and interaction. Since this study also examine hotel guest customers, we adopt the scale of So et al. (2014) due to its comprehensiveness and consistent context. Service failure and emotion Customer emotion is an important antecedent of customer engagement. Currently firms have been shifted their focus from selling products to emotional connection with their customers (Pansari and Kumar 2017). Positive emotion may enhance customer engagement and thereby improve customer loyalty. But when service failure occurs, customers have different negative emotions including anger, frustration, helplessness, regret amongst others. These negative emotions of customers disappoint customers themselves and reduce customer loyalty. Different emotions may have different impact on customer engagement. Anger often refers to the attributes of others such as the service providers (Weiner, 1985) whereas regret often refers to the service failure locus of customer himself/herself such as the customer is regret to choose this service provider (Roseman, 1991). Both anger and regret refer to retrospective emotions and when customer would like to solve questions they may also negative emotion of helplessness which is called prospective emotions (Davidow, 2003; Gelbrich, 2010). This study aims to examine and differentiate the impact of two retrospective emotions of anger and regret and one prospective emotions of helplessness. The first hypothesis is therefore proposed: H1: Anger has negative impact on customer engagement. H2: Regret has negative impact on customer engagement. H3: Helplessness has negative impact on customer engagement. Service failure compensation Though service providers aim to deliver zero fault service, it is inevitable service failure may occur that may bring customers anger and dissatisfaction and damage the customer loyalty thereby. It is found that compensation is an effective way to comfort and delight the dissatisfied customers. Therefore, it is important to formulate effective compensation strategy when service failure occurs. Different compensation strategies such as monetary or nonmonetary (Fu et al. 2015), immediate or delayed compensation (Boshoff, 1997; Davidow, 2003), may be suitable to different contexts/situations. According to prospect theory, a customer is risk-reverse in case of gains. A customer may value products available now more than products obtained in the future due to the higher certainty of the former. Similarly, immediate compensation has less uncertainty than delayed compensation, and therefore is supposed to have higher value. Therefore customers with anger are assumed to have higher customer engagement when immediately compensated. On the other hand, regret customers attribute failure to himself/herself and therefore less expect compensation. The immediate compensation may lead to unfair and thereby less effect than delayed compensation. Therefore immediate compensation may not always be superior over the delayed one under different contexts. We therefore propose the second hypothesis: H1a: Compensation type (immediate vs. delayed) moderates the relationship between anger and customer engagement. H2a: Compensation type (immediate vs. delayed) moderates the relationship between regret and customer engagement. H3a: Compensation type (immediate vs. delayed) moderates the relationship between helplessness and customer engagement. Brand loyalty Brand loyalty refers to the loyalty of a customer toward the brand both behaviourally and attitudinally (Dick and Basu 1994; Li and Petrick 2008; So, King, Sparks, and Wang 2013). It is a key goal of marketing activities, and its antecedents have been extensively examined such as satisfaction, perceived quality, received value, and brand trust, amongst others. Customer engagement, as the activity of a customer toward to a firm, is naturally viewed to influence brand loyalty. This study therefore adopts brand loyalty as the consequence of customer engagement. Furthermore, we would like to examine if compensation types have moderating effect between customer engagement and brand loyalty. We therefore propose below two hypotheses: H4: customer engagement has positive impact on brand loyalty. H4a: Compensation type (immediate vs. delayed) moderates the relationship between customer engagement and brand loyalty. The research model is shown in Figure 1 where all hypotheses are demonstrated. Our research model is developed based on the S-O-R framework in which emotions are antecedent of customer engagement, and customer engagement impacts hotel brand loyalty. This research model also shows the moderating effects of compensation types has on causal relationships between the aforementioned constructs. Methodology Scenario design Scenario based questionnaire is designed to obtain quantitative data for analysis. Based on the interview with hotel managers/operators, one service failure scenario and two compensation scenarios (immediate and delayed) are designed. In-depth interviews with a couple of hotel managers and guests were conducted to verify the realisation of the scenarios formulated. The questionnaire begins with a screening question: in the previous 12 months have you ever had experience staying in a four- or five-star hotel? The survey would only continue if the answer is “yes”. Then the participant is asked to write down the name of this hotel and read the below service failure scenario thereby. Service failure scenario: Imagine you have checked into this hotel again. During your stay in hotel, you send your coat for laundry. It is a nice coat and you bought it a year ago with the price of 1000RMB. However when you collect the cleaned coat, you notice that there is a damage on your coat which makes you cannot dress this coat anymore. You therefore call the service counter for complain. Immediate and delayed compensation scenarios were designed as follows: Immediate compensation scenario: after 15 minutes, the duty manager of the hotel went to our hotel and expressed his sincere apology. You showed him about the damage and informed him the original price of your coat. The manager offered you the cash compensation with the original price of your coat and you agree with this. After half an hour you received 1000RMB cash as the compensation. Delayed compensation scenario: after 15 minutes, the duty manager of the hotel went to your room and expressed his sincere apology. You showed him about the damage and informed him the original price of your coat. The manager said according to the hotel policy, they need to check how this happened and confirm the price of your coat first before making the compensation for you. After two weeks you left the hotel, you received 1000RMB compensation which is transferred into your bank account directly. Participant emotion is measured after the participants read the service failure scenario and before they read the compensation scenario. Each participant is randomly assigned to be involved in one compensation scenario only. Customer engagement and hotel brand loyalty are measured after the compensation happened. Variable measurement Customer engagement is measured using 25-item scale developed by So et al. (2014) in which five factors are involved: identification, enthusiasm, attention, absorption, and interaction. Particularly, identification is measured by four attributes: “When someone criticizes this brand, it feels like a personal insult”, “When I talk about this brand, I usually say we rather than they”. “This brand’s successes are my successes”. “When someone praises this brand, it feels like a personal compliment”. Enthusiasm is measured by five attributes: “I am heavily into this brand”. “I am passionate about this brand” “I am enthusiastic about this brand” “I feel excited about this brand” “I love this brand”. Attention is measured by five attributes: “I like to learn more about this brand” “I pay a lot of attention to anything about this brand” “Anything related to this brand grabs my attention” “I concentrate a lot on this brand” “I like learning more about this brand” . Absorption is measured by five attributes: “When I am interacting with the brand, I forget everything else around me” “Time flies when I am interacting with the brand” “When I am interacting with brand, I get carried away” “When interacting with the brand, it is difficult to detach myself” “In my interaction with the brand, I am immersed” “When interacting with the brand intensely, I feel happy”. Interaction is measure by five attributes: “In general, I like to get involved in brand community discussions” “I am someone who enjoys interacting with likeminded others in the brand community” “I am someone who likes actively participating in brand community discussions” “In general, I thoroughly enjoy exchanging ideas with other people in the brand community” “I often participate in activities of the brand community”. Three emotion of anger, regret and helplessness are included as the measurement of emotion. Particularly, according to Gelbrich (2010), three attributes are adopted to measure anger “I would feel angry with the hotel/hotel employees”, “I would feel mad with the hotel/hotel employees”, and “I would feel furious about the hotel/hotel employees”. Three statements are employed to measure regret (Tsiros & Mittal 2000): “I would feel sorry for choosing this hotel”, “I regretted choosing this hotel”, and “I should have chosen another hotel”. Four statements are used to measure helplessness (Gelbrich 2010): “I would feel helpless”, “I would feel lost”, “I would feel defenceless”, and “I would feel stranded.” Five statements are used to measure brand loyalty (So, King, Sparks, & Wang 2013): “I would say positive things about this brand to other people.” “I would recommend this brand to someone who seeks my advice.” “I would encourage friends and relatives to do business with this brand.” “I would consider this brand my first choice to buy services.” “I would do more business with this brand in the next few years.” A seven-point Likert scale ranging from 1 (=disagree strongly) to 7 (=agree strongly) is adopted for all measurement. Data collection and analysis method In-depth interview with managers from upscale hotels and customers will be used to finalize scenarios. Opinions of academic experts will be used to revise variable measurements and questionnaires. Convenience sampling method will be adopted to obtain about 400 respondents who has experience of staying at four- or five-stars hotels in China in the previous year. Regarding with data analysis, Partial least square structural equation modelling (PLS-SEM) is used to test the hypotheses proposed. Expected results The manipulation check has been conducted to verify the scenarios designed. The negative relationship between emotions and customer engagement are expected and compensation timing (delayed or immediate) may moderate this relationship. Most importantly, it is expected that this moderating effect varies when different emotions and customer engagement are examined. Contributions The theoretical contributions have three folders. Firstly, this study first considers compensation timing into the examination of relationship between different negative emotions and customer engagement, after service failure occurs. Secondly, this study adopts stimulus-organism-response theory to explore the mechanism how service failure could be well recovered by relationships of different negative emotions, effective compensation type, customer engagement, and brand loyalty. Thirdly, this study applies second order factor for the measurement of customer engagement and also divides negative emotions into retrospective and prospective ones to shed light on customer engagement in the context of service failure and compensation. The practical implication of this study will benefit industry practitioners for their formulation of compensation strategies. Especially as the development of big data, hotel industry is able to adopt different strategies for individuals to maximize customer experience. The findings of this study could propose different strategies for different situations/individuals thereby.

Disciplinary Session 9 / Sport Biomechanics / Development and Method in Sport Biomechanics Study : 코치 업무에 있어서 컴퓨터의 새로운 역할

( Doris I . Miller ) 한국체육학회 1998 국제스포츠과학 학술대회 Vol.1998 No.2

Effect of Fiber Orientation on the Fatigue Behavior of Steel Fiber-Reinforced Concrete Specimens by Performing Wedge Splitting Tests and Computed Tomography Scanning

Dorys C. González,Álvaro Mena-Alonso,Jesús Mínguez,José A. Martínez,Miguel A. Vicente 한국콘크리트학회 2024 International Journal of Concrete Structures and M Vol.18 No.2

This paper shows the relationship, in steel fiber-reinforced concrete, between fiber orientation and fatigue response through the combined use of computed tomography (CT), digital image processing (DIP) software and wedge splitting test (WST). The WST cubes were extracted from conventional 150 × 150× 600 concrete prisms and a groove and notch were carved on different faces in such a way that in half of the test specimens the fibers are oriented mostly perpendicular to the breaking surface and, in the other half, the fibers are mostly oriented parallel to the breaking surface. Fiber orientation was obtained using a CT device and DIP software from a miniprism extracted from the previously mentioned concrete prisms. The results show that there is a strong correlation between the crack-sewing fiber orientation on the one hand and fatigue life and crack opening rate per cycle on the other hand. Cubes with a higher percentage of fibers perpendicular to the crack surface (i.e., with a higher efficiency index) show a longer fatigue life and a lower crack opening rate per cycle, while cubes with a lower efficiency index show a shorter fatigue life and a higher crack opening rate per cycle.

The Panorama of Studies on the Brazilian Housing Program Called “Minha Casa Minha Vida”

Doris C.C.K. Kowaltowski,Ariovaldo Denis Granja,Daniel de Carvalho Moreira,Silvia A. Mikami G. Pina,Carolina Asensio Oliva,Mariana Rios Castro 한국주거학회 2015 한국주거학회 국제학술대회논문집 Vol.2015 No.4

Labor Unions, Unemployment, and Trade and Capital Liberalization

( Doris Geide Stevenson ) 세종대학교 경제통합연구소 (구 세종대학교 국제경제연구소) 2000 Journal of Economic Integration Vol.15 No.1

This paper utilizes a specific factor model where rewards to labor and capital, and employment are determined by efficient bargaining between entrepreneurs and workers in each sector. Union threat points arise endogenously since workers` outside opportunities in one sector depend on the bargain struck in the other sector. This fully unionized economy will generally be characterized by unemployment and inter- industry wage differentials. Both trade and capital liberalization may lead to an increase in overall employment. (JEL Classifications: F10, F15, F20, J51, J64)