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Purpose – The aim of this paper is to describe and apply a new three-step approach to prioritizing service attributes in formulating quality-improvement strategies. In particular, the paper seels to demonstrate the value of impact range-performance analysis (IRPA) and impact-asymmetry analysis (IAA) in prioritizing quality attributes for improvemen...
Contexts in source publication
Context 1
... indices have been proposed by Matzler and Renzl (2007) and by Füller and Matzler (2008). However, rather than utilizing differences, these authors calculated ratios of penalty-indices and reward indices. As a result, index values can theoretically range between positive infinity and negative infinity, which complicates comparisons of calculated indices. In contrast, the values of the IA index proposed here can range from – 1 to +1. Index values can be interpreted as follows: A value of +1 indicates that an attribute is a ‘perfect satisfier’— that is, the attribute has only satisfaction-generating potential. A value of – 1 indicates that an attribute is a ‘perfect dissatisfier’— that is, the attribute has only dissatisfaction-generating potential. A value of 0 indicates that the attribute is a perfect ‘ hy brid’— that is, the attribute has equal satisfaction- and dissatisfaction-generating potentials. The attribute-RIOCS indices, IA indices, and corresponding SGPs and DGPs are shown in Table II. As indicated in the analytical framework, the next step involved an impact range-performance analysis (IRPA). The arithmetic means of attribute-performance scores were depicted along the vertical axis of a two-dimensional grid, and previously calculated RIOCS scores were situated along the horizontal axis. The grand mean of the performance scores (P GM = 3.974) and the grand mean of the RIOCS scores (RIOCS GM = 0.170) were used to divide the grid into four quadrants (as shown in Figure 2). Martilla and James (1977, p. 79) noted tha t the value of IPA “... lies in identifying relative , rather than absolute levels of importance and performance”. Accordingly, when analyzing IRPA results, a good start is to identify attributes which have the highest RIOCSs and lowest performance scores, and to analyze attributes pair- or group-wise. Figure 2 reveals that four attributes —attribute 3 (‘offer of restaurants’), attribute 4 (‘shopping possibilities’), attribute 6 (‘comfort level of the building’) and attribute 8 (‘offer of flights’)— should be paid particular attention because they perform below average, but have RIOCSs close to or above average. Two attributes —attribute 5 (‘cleanliness’) and attribute 7 (‘staff politeness’) should be assigned medium priority because their RIOCS is above average, but so also is their performance. Low priority should be assigned to three attributes —attribute 1 (‘ease of finding your way’), attribute 2 (‘check - in procedure’) and attribute 9 (‘availability of luggage carts’)— because their RIOCS is below average, whereas their performance is above average. As indicated in the analytical framework outlined above, the third step involved an analysis of asymmetric effects in the creation of OCS by conducting an impact-asymmetry analysis (IAA). As shown in Figure 3, another two-dimensional grid was constructed, with RIOCS scores depicted along the horizontal axis and IA scores along the vertical axis. In addition, an iso-impact line was drawn at IA = 0. Attribute-performance scores are shown in brackets. The IAA grid can be interpreted as follows. Attributes in the lower part of the grid (IA less than 0) have a greater potential to create dissatisfaction than satisfaction; these attributes c an be referred to as ‘dissatisfiers’ (‘must - be’ quality elements, basic factors). Conversely, attributes in the upper part of the grid (IA greater than 0) have a greater potential to create satisfaction than dissatisfaction; these attributes can be referre d to as ‘satisfiers’ (attractive quality elements, excitement factors). When attributes are moving towards the iso-impact line, the dominance of either dissatisfaction- or satisfaction-generating potential weakens. Attributes located exactly on the iso-impact line (IA=0) have an equal potential to create satisfaction and dissatisfaction; these can be referred to as perfect ‘ hybrids ’ (or one- dimensional attributes). The attributes were further subdivided into five categories according to the degree of asymme try of their impact on OCS: (i) ‘delighters’ (IAI > 0.4); (ii) ‘satisfiers’ (0.4 ≥ IAI > 0.1); (iii) ‘hybrids’ (0.1 ≥ IAI ≥ –0.1); (iv) ‘dissatisfiers’ (–0.1 > IAI ≥ – 0.4); and (v) ‘frustrators’ (IAI < – 0.4). In addition, to facilitate a distinction between more or less relevant attributes in the creation of OCS, the attributes were also subdivided into three categories according to their RIOCS: (i) ‘high - impact attributes’ (RIOCS > 0.225); (ii) ‘medium - impact’ attributes (0.125 ≤ RIOCS ≤ 0.225); and (iii) ‘low - impact attributes’ (RIOCS < 0.125). As shown in Figure 3, none of the attributes is a ‘delighter’ or a ‘frustrator’. However, three attributes —attribute 1 (‘ease of finding your way’), attribute 3 (‘offer of restaurants’), and attribute 5 (‘cleanliness’)— are categorized as ‘dissatisfiers’; and two attributes— attribute 4 (‘shopping possibilities’) and attribute 7 (‘staff politeness’)— are classified as ‘satisfiers’. These attributes require particular attention when setting improvement priorities because their impact on OCS varies significantly with different levels of attribute- performance. Attribute 1 (‘ease of finding your way’), which is a ‘low - impact dissatisfier’, and attribute 5 (‘cleanliness’) , which i s a ‘m edium- impact dissatisfier’, are not problematic. Because both of these attributes have high performance levels (P 1 = 4.34; P 5 = 4.19), increasing their performance is not likely to result in a significant increase of OCS. However, attribute 3 (‘offer of restaurants’) and attribute 4 (‘shopping possibilities’) have relatively low performance (P 3 = 3.33; P 4 = 3.58) and approximately equal RIOCSs. Because attribute 3 (‘offer of restaurants’) is a ‘medium - impact dissatisfier’, whereas attribute 4 (‘shopping possibilities’) is a ‘medium - impact’ satisfier, the service manager should consider assigning higher priority to the former (in accordance with the rule of decreasing customer dissatisfaction before increasing customer satisfaction). It is noteworthy that the IRPA results provided the same recommendation. Finally, attribute 7 (‘staff politeness’) has relatively high performance (P 7 = 4.24), but is a ‘high - impact satisfier’. This indicates that the attribute has unused potential to increase customer satisfaction. Because only medium priority has been assigned to the attribute through IRPA, the service manager should consider increasing the priority level of its improvement. To formulate effective and efficient service-improvement strategies, service managers need to know which service attributes have a dominant impact on overall customer satisfaction (OCS). However, because the impact of some service attributes on OCS varies according to the current level of attribute performance, traditional measures of the ‘importance’ of quality attributes do not necessarily capture an attribute’s impact on OCS if that attribute’s impact varies with its level of performance. Such analyses might offer misleading recommendations for prioritizing service improvements. The present study therefore makes the following general recommendation s: (i) a revised approach to IPA that uses scores of an attribute’s range of impact on OCS (RIOCS) (rather than attribute-importance); and (ii) an analysis of the asymmetry of attribute-impact on OCS. In applying these general recommendations, the study proposes a new analytical framework to prioritize the improvement of service attributes. This framework involves the following. An impact range- performance analysis (IRPA) to derive an attribute’s level of performance and its range of impact on OCS (RIOCS). This RIOCS measure is independent of the current level of attribute-performance; rather, it takes into account an attribute’s impact on OCS in cases of both extremely high attribute performance and extremely low attribute-performance. Accordingly, the imp rovement priority is determined by an attribute’s general potential to impact OCS, rather than by its current impact (which can significantly vary with different levels of performance). An impact-asymmetry analysis (IAA) to categorize service attributes according to their potential to generate satisfaction or dissatisfaction. This analysis enables attributes to be categorized as ‘delighters’, ‘satisfiers’, ‘hybrids’, ‘dissatisfiers’, or ‘frustrators’). It also enables them to be categorized as ‘high impact’, ‘medium - impact’, and ‘low - impact’ attributes. The applicability and usefulness of this analytical framework has been demonstrated in a case study of passenger satisfaction with airport services. The methodology could also be applied to other service sectors, and thus represents a valuable means for service managers to make decisions about improvement priorities of service attributes. However, it is not possible to generalize the individual findings of this case study to other airports, or to other sectors. An important implication for researchers from the present study is that direct and indirect methods of assessing the ‘importance’ of attributes should not be regarded as alternative approaches to the measurement of the same co ncept. Direct methods assess the ‘importance’ of an attribute as perceived by a customer, whereas indirect methods assess the extent to which an attribute contributes to the customer's global judgment of the performance of a service. These are two different constructs that do not necessarily correspond. Therefore, to avoid potentially misleading conclusions based on attribute-importance data, a clear distinction ...
Context 2
... indices have been proposed by Matzler and Renzl (2007) and by Füller and Matzler (2008). However, rather than utilizing differences, these authors calculated ratios of penalty-indices and reward indices. As a result, index values can theoretically range between positive infinity and negative infinity, which complicates comparisons of calculated indices. In contrast, the values of the IA index proposed here can range from – 1 to +1. Index values can be interpreted as follows: A value of +1 indicates that an attribute is a ‘perfect satisfier’— that is, the attribute has only satisfaction-generating potential. A value of – 1 indicates that an attribute is a ‘perfect dissatisfier’— that is, the attribute has only dissatisfaction-generating potential. A value of 0 indicates that the attribute is a perfect ‘ hy brid’— that is, the attribute has equal satisfaction- and dissatisfaction-generating potentials. The attribute-RIOCS indices, IA indices, and corresponding SGPs and DGPs are shown in Table II. As indicated in the analytical framework, the next step involved an impact range-performance analysis (IRPA). The arithmetic means of attribute-performance scores were depicted along the vertical axis of a two-dimensional grid, and previously calculated RIOCS scores were situated along the horizontal axis. The grand mean of the performance scores (P GM = 3.974) and the grand mean of the RIOCS scores (RIOCS GM = 0.170) were used to divide the grid into four quadrants (as shown in Figure 2). Martilla and James (1977, p. 79) noted tha t the value of IPA “... lies in identifying relative , rather than absolute levels of importance and performance”. Accordingly, when analyzing IRPA results, a good start is to identify attributes which have the highest RIOCSs and lowest performance scores, and to analyze attributes pair- or group-wise. Figure 2 reveals that four attributes —attribute 3 (‘offer of restaurants’), attribute 4 (‘shopping possibilities’), attribute 6 (‘comfort level of the building’) and attribute 8 (‘offer of flights’)— should be paid particular attention because they perform below average, but have RIOCSs close to or above average. Two attributes —attribute 5 (‘cleanliness’) and attribute 7 (‘staff politeness’) should be assigned medium priority because their RIOCS is above average, but so also is their performance. Low priority should be assigned to three attributes —attribute 1 (‘ease of finding your way’), attribute 2 (‘check - in procedure’) and attribute 9 (‘availability of luggage carts’)— because their RIOCS is below average, whereas their performance is above average. As indicated in the analytical framework outlined above, the third step involved an analysis of asymmetric effects in the creation of OCS by conducting an impact-asymmetry analysis (IAA). As shown in Figure 3, another two-dimensional grid was constructed, with RIOCS scores depicted along the horizontal axis and IA scores along the vertical axis. In addition, an iso-impact line was drawn at IA = 0. Attribute-performance scores are shown in brackets. The IAA grid can be interpreted as follows. Attributes in the lower part of the grid (IA less than 0) have a greater potential to create dissatisfaction than satisfaction; these attributes c an be referred to as ‘dissatisfiers’ (‘must - be’ quality elements, basic factors). Conversely, attributes in the upper part of the grid (IA greater than 0) have a greater potential to create satisfaction than dissatisfaction; these attributes can be referre d to as ‘satisfiers’ (attractive quality elements, excitement factors). When attributes are moving towards the iso-impact line, the dominance of either dissatisfaction- or satisfaction-generating potential weakens. Attributes located exactly on the iso-impact line (IA=0) have an equal potential to create satisfaction and dissatisfaction; these can be referred to as perfect ‘ hybrids ’ (or one- dimensional attributes). The attributes were further subdivided into five categories according to the degree of asymme try of their impact on OCS: (i) ‘delighters’ (IAI > 0.4); (ii) ‘satisfiers’ (0.4 ≥ IAI > 0.1); (iii) ‘hybrids’ (0.1 ≥ IAI ≥ –0.1); (iv) ‘dissatisfiers’ (–0.1 > IAI ≥ – 0.4); and (v) ‘frustrators’ (IAI < – 0.4). In addition, to facilitate a distinction between more or less relevant attributes in the creation of OCS, the attributes were also subdivided into three categories according to their RIOCS: (i) ‘high - impact attributes’ (RIOCS > 0.225); (ii) ‘medium - impact’ attributes (0.125 ≤ RIOCS ≤ 0.225); and (iii) ‘low - impact attributes’ (RIOCS < 0.125). As shown in Figure 3, none of the attributes is a ‘delighter’ or a ‘frustrator’. However, three attributes —attribute 1 (‘ease of finding your way’), attribute 3 (‘offer of restaurants’), and attribute 5 (‘cleanliness’)— are categorized as ‘dissatisfiers’; and two attributes— attribute 4 (‘shopping possibilities’) and attribute 7 (‘staff politeness’)— are classified as ‘satisfiers’. These attributes require particular attention when setting improvement priorities because their impact on OCS varies significantly with different levels of attribute- performance. Attribute 1 (‘ease of finding your way’), which is a ‘low - impact dissatisfier’, and attribute 5 (‘cleanliness’) , which i s a ‘m edium- impact dissatisfier’, are not problematic. Because both of these attributes have high performance levels (P 1 = 4.34; P 5 = 4.19), increasing their performance is not likely to result in a significant increase of OCS. However, attribute 3 (‘offer of restaurants’) and attribute 4 (‘shopping possibilities’) have relatively low performance (P 3 = 3.33; P 4 = 3.58) and approximately equal RIOCSs. Because attribute 3 (‘offer of restaurants’) is a ‘medium - impact dissatisfier’, whereas attribute 4 (‘shopping possibilities’) is a ‘medium - impact’ satisfier, the service manager should consider assigning higher priority to the former (in accordance with the rule of decreasing customer dissatisfaction before increasing customer satisfaction). It is noteworthy that the IRPA results provided the same recommendation. Finally, attribute 7 (‘staff politeness’) has relatively high performance (P 7 = 4.24), but is a ‘high - impact satisfier’. This indicates that the attribute has unused potential to increase customer satisfaction. Because only medium priority has been assigned to the attribute through IRPA, the service manager should consider increasing the priority level of its improvement. To formulate effective and efficient service-improvement strategies, service managers need to know which service attributes have a dominant impact on overall customer satisfaction (OCS). However, because the impact of some service attributes on OCS varies according to the current level of attribute performance, traditional measures of the ‘importance’ of quality attributes do not necessarily capture an attribute’s impact on OCS if that attribute’s impact varies with its level of performance. Such analyses might offer misleading recommendations for prioritizing service improvements. The present study therefore makes the following general recommendation s: (i) a revised approach to IPA that uses scores of an attribute’s range of impact on OCS (RIOCS) (rather than attribute-importance); and (ii) an analysis of the asymmetry of attribute-impact on OCS. In applying these general recommendations, the study proposes a new analytical framework to prioritize the improvement of service attributes. This framework involves the following. An impact range- performance analysis (IRPA) to derive an attribute’s level of performance and its range of impact on OCS (RIOCS). This RIOCS measure is independent of the current level of attribute-performance; rather, it takes into account an attribute’s impact on OCS ...
Citations
... Nonetheless, emerging research in the field of customer satisfaction [59]-which includes studies within urban planning-challenges the validity of the linear model [50,53]. The evidence suggests that the relationship between neighborhood attributes and residents' satisfaction may, in fact, be nonlinear, which implies that adherence to a linear model could lead to inaccurate estimations and, consequently, a misunderstanding of the actual relationships. ...
... The evidence suggests that the relationship between neighborhood attributes and residents' satisfaction may, in fact, be nonlinear, which implies that adherence to a linear model could lead to inaccurate estimations and, consequently, a misunderstanding of the actual relationships. Such misestimations could further result in the misallocation of scarce planning resources due to an incorrect assessment of the relative significance of different neighborhood attributes in contributing to residents' satisfaction [59]. Moreover, research into service satisfaction reveals that the relationship between service attributes and satisfaction is asymmetrical [59,60]. ...
... Such misestimations could further result in the misallocation of scarce planning resources due to an incorrect assessment of the relative significance of different neighborhood attributes in contributing to residents' satisfaction [59]. Moreover, research into service satisfaction reveals that the relationship between service attributes and satisfaction is asymmetrical [59,60]. This asymmetry has been substantiated by numerous studies [61][62][63][64], indicating that the impact of an attribute's positive performance on overall satisfaction can differ significantly from the impact of its negative performance, and vice versa. ...
Post-industrial neighborhoods are valued for their historical and cultural significance but often contend with challenges such as physical deterioration, social instability, and cultural decay, which diminish residents’ satisfaction. Leveraging urban renewal as a catalyst, it is essential to boost residents’ satisfaction by enhancing the environmental quality of these areas. This study, drawing on data from Shenyang, China, utilizes the combined strengths of gradient boosting decision trees (GBDTs) and asymmetric impact-performance analysis (AIPA) to systematically identify and prioritize the built-environment attributes that significantly enhance residents’ satisfaction. Our analysis identifies twelve key attributes, strategically prioritized based on their asymmetric impacts on satisfaction and current performance levels. Heritage maintenance, property management, activities, and heritage publicity are marked as requiring immediate improvement, with heritage maintenance identified as the most urgent. Other attributes are categorized based on their potential to enhance satisfaction or their lack of immediate improvement needs, enabling targeted and effective urban revitalization strategies. This research equips urban planners and policymakers with critical insights, supporting informed decisions that markedly improve the quality of life in these distinctive urban settings.
... While the three-factor theory identifie priority of improvement and classifies service factors, it fails to consider the influen factors on overall satisfaction. In 2008, Mikulic and Prebezac proposed IAA [33], w extends penalty-reward contrast analysis (PRCA), evaluates the asymmetric range o fluence on satisfaction by factors and uses a value called IA (impact asymmetry) to q ...
... While the three-factor theory identifies the priority of improvement and classifies service factors, it fails to consider the influence of factors on overall satisfaction. In 2008, Mikulic and Prebezac proposed IAA [33], which extends penalty-reward contrast analysis (PRCA), evaluates the asymmetric range of influence on satisfaction by factors and uses a value called IA (impact asymmetry) to quantify the asymmetric impact of factors on overall satisfaction. Therefore, IAA classifies the different types of factors in the three-factor theory more finely and can prioritize the evaluated factors more accurately [34]. ...
The renovation of public space environments in old residential areas has become the focal point in Chinese urban renewal and promotion of urban sustainable development; thus, an urgent need for research expansion is attached to identifying the environmental factors of public space and their impacts from the perspective of residents’ needs and satisfaction. Taking Hefei, China, as an example, and based on a satisfaction survey of the public space environment in old residential areas, this study discerned critical factors affecting public space satisfaction through gradient-boosting decision trees and impact-asymmetry analysis methods, after which the asymmetric relationship between public space environment factors and resident satisfaction was tested and the priority and goal of public space environment renovation were obtained. The results indicate that the following: (1) Compared with physical environment factors, current social environment factors, including uncivilized behavior, space occupation, and hygiene and cleanliness, exert a greater impact on the overall satisfaction. (2) The hypothesis that there exists a general nonlinear relationship between environmental factors and satisfaction is verified, with all social environment factors proving to be nonlinear and physical environment factors being highly related to social environment creation, such as nursing space for the old and young, reading and learning space, and display and communication space. (3) A priority hierarchy for the improvement of environmental factors should be established, which basically conforms to Maslow’s hierarchy of needs theory. The role of social environment renovation should be highlighted, along with the necessity to bolster community governance and public space management. At the level of the physical environment, more space should be available for the activities of residents, display and communication, and reading and learning. This study can provide a corresponding theoretical basis and planning inspiration for meeting the multiple needs of residents for public space, improving life happiness, and promoting the sustainable development of cities in the renovation of public space environments in old residential areas.
... (F.-H. Lin et al., 2017;Madzík, 2018;Mikulić & Prebežac, 2008, 2011Pugna et al., 2021;Reichenbach et al., 2022;Witell et al., 2013). Those methods have not been comprehensively reviewed and compared, and, more importantly, there is no consensus on the suitability or fit of each method for a particular research situation based on strengths and weaknesses (Violante & Vezzetti, 2017). ...
... Lin et al., 2010;Little et al., 2015;Matzler & Sauerwein, 2002;Slevitch & Oh, 2010;Staus & Becker, 2012;Velikova et al., 2017). In the three-factor literature (Bartikowski & Llosa, 2004;Berger et al., 1993;Busacca & Padula, 2005;Cadotte & Turgeon, 1988;Erto & Vanacore, 2002;Friman & Edvardsson, 2003;Fuchs & Weiermair, 2003;Johnston, 1995;Kondo, 2000; Y. F. Kuo, 2004;Matzler & Hinterhuber, 1998;Matzler & Sauerwein, 2002;Matzler et al., 1996;Matzler et al., 2003Mikulić & Prebežac, 2008, 2011Slevitch et al., 2013;Witell et al., 2013), attribute categories have been variously labeled: ...
... Additionally, the IG method does not reveal the degree of the attribute's impact asymmetry toward satisfaction or dissatisfaction. Although such a measure was proposed by Mikulić and Prebežac (2008), it is not part of the original IG method. Consequently, although the IG method cannot be regarded as a reliable approach for assessing attributes into Kano categories, it can be described as an advanced version of the importance-performance analysis because it overcomes some of the shortcomings of the original IPA method (Bartikowski & Llosa, 2004;Mikulić & Prebežac, 2011;Oh, 2001;Slevitch & Oh, 2010). ...
The Kano model has been widely applied in multiple disciplines, particularly in product or service development and optimization. Although the original categorization framework remains the most applied, modifications and new approaches have recently been developed. Yet, most of the hospitality and tourism studies continue to use the original Kano method when newer and superior methods are available. The new categorization methods also have flaws, which academics and practitioners should be aware of. The present study uses a systematic literature review approach to provide a comprehensive synthesis and critical analysis of the existing Kano model categorization methods structuring them in groups based on categorization procedures and pointing to the strengths, weaknesses, and fit of each method. The manuscript aims to provide the most current systematic roadmap and “how-to” of the existing methods. It also points to the existing knowledge gaps and directions for future research.
... Then regression analysis was conducted to investigate the symmetric influence of hotel service quality on CE. Asymmetric impact-performance analysis (AIPA), an extended version of Mikulić and Prebežac's (2008) impact-asymmetry analysis, was used to classify service quality attributes according to their asymmetric influences on CE. AIPA is a twodimensional matrix that consists of impact asymmetry and performance of the attributes. ...
... The performance score and impact asymmetry (IA) value for each service attribute should be calculated to construct the AIPA matrix. While the arithmetic means of service attributes were used as the performance score, the IA value was calculated in four steps, as Mikulić and Prebežac (2008) suggested. First, the penalty indices (PI) and reward indices (RI) were obtained by employing the penalty-reward contrast analysis. ...
... In the following step, each service attribute was placed on the AIPA matrix by using its mean performance and IA value as the x and y coordinates, respectively. The matrix was further divided into three parts by two parallel lines that were drawn from the −0.1 and + 0.1 points of the IA axis (see Figure 2), as suggested by Mikulić and Prebežac (2008). Based on the IA scores, service attributes were classified as follows: attributes with IA scores between −1.0 and −0.1 as basic factors, those between + 1.0 and 0.1 as excitement factors, and those between −0.1 and 0.1 as performance factors. ...
... The symmetric assumption of attribute performance-satisfaction effect; however, has been challenged by the exploration of asymmetries. Many studies (Mersha and Adhlaka, 1992;Mikulic and Prebezac, 2008;Slevitch and Oh, 2010) indicate that the relationship between attribute performance and customer satisfaction is nonlinear or asymmetric. Mersha and Adhlaka (1992), for example, find that staff indifference is ranked very high as an attribute of poor quality; however, staff enthusiasm or helpfulness is ranked low as an attribute of good quality. ...
... Matzler and Sauerwein (2002), for example, propose basic factors (i.e., must-be attributes), performance factors (one-dimensional attributes) and excitement factors (i.e., attractive attributes). This approach has typically been adopted and adapted (Albayrak, 2018;Matzler, 2008, Mikulic andPrebezac, 2008). Fuller and Matzler (2008), for example, refer to basic factors, performance factors and excitement factors as dissatisfiers, hybrids and satisfiers, respectively. ...
... Fuller and Matzler (2008), for example, refer to basic factors, performance factors and excitement factors as dissatisfiers, hybrids and satisfiers, respectively. Mikulic and Prebezac (2008) similarly refer to performance factors as hybrids and notably further subdivide basic factors into dissatisfiers and frustrators, and excitement factors into satisfiers and delighters. Dissatisfiers, frustrators, hybrids, satisfiers and delighters are demonstrated based on the studies by Mikulic and Prebezac (2008) and Fuller and Matzler (2008) as follows. ...
All-inclusive holidays have regained popularity among budget-conscious holidaymakers amidst inflation and living cost crisis. Following the pandemic, tourists are now staying at the hotel more and venture out less, which further makes all-inclusive holidays more popular than ever. With the increasing popularity of all-inclusive holidays, knowledge expansion of this tourism segment is necessary. This study extends the under-developed all-inclusive holiday literature by exploring service quality attributes of all-inclusive holidays and evaluating their asymmetric effect on tourist satisfaction. Suggestions on prioritizing service quality attributes for improvement are expected to support industry practitioners in managing service quality and satisfaction strategically.
... The AICA is grounded in the three-factor theory of customer satisfaction, which classifies the service attributes as the basic, performance and excitement factors based on their varying impact on overall satisfaction at their high and low performance levels (Mikuli c and Prebežac, 2008). The basic factors refer to the attributes that would not lead to satisfaction, but failure to offer these attributes according to customer expectations would result in dissatisfaction. ...
Purpose
By conducting a longitudinal study, this study aims to investigate how the role of hotel attributes in destination competitiveness changed through the stages of pre-, amid and recovery from the crisis.
Design/methodology/approach
First, the latent Dirichlet allocation method was used to identify hotel attributes from 15,137 online reviews, and then a sentiment analysis was performed to determine tourist satisfaction with the subject attributes. Second, separate asymmetric impact competitor analyses were conducted for the three stages of the crisis, and their results were compared with understand how the role of the hotel attributes changed throughout the crisis.
Findings
The results revealed that the impacts of hotel attributes on tourist satisfaction and destination competitiveness differed significantly at each stage of the crisis.
Research limitations/implications
This research expands the existing literature by offering valuable insights by elucidating the changing characteristics of hotel attributes at each crisis stage. The results extend the body of knowledge in destination management by providing evidence on the validity of asymmetric impact competitor analysis.
Originality/value
To fully understand the impact of a crisis (e.g. COVID-19) on destination competitiveness with a focus on the hotel sector, this research conducted a longitudinal study that covers three stages of the crisis (i.e. pre-, amid and post-crisis). Moreover, unlike previous studies, this research considers the asymmetric relationships between service attributes and overall tourist satisfaction, as well as competitors’ information.
... Through regression analysis using these dummy variables, the structure of factors can be discerned, aided by two coefficients indicating each attribute's penalty index (PI) and reward index (RI). Mikulić and Prebežac extended this approach [55], introducing the concept of impact-asymmetry analysis (IAA), which is a further development of the three-factor theory. As shown in Figure 4, IAA distinguishes five factors: frustrators, dissatisfiers, hybrid, satisfiers, and delighters. ...
... Throu gression analysis using these dummy variables, the structure of factors can be dis aided by two coefficients indicating each attribute's penalty index (PI) and reward (RI). Mikulić and Prebežac extended this approach [55], introducing the concept pact-asymmetry analysis (IAA), which is a further development of the three-factor As shown in Figure 4, IAA distinguishes five factors: frustrators, dissatisfiers, hybr isfiers, and delighters. This finer categorization is based on the degree of asymmetr frustrators and delighters being more asymmetric compared to dissatisfiers and sa [17]. ...
Subway station entrances serve as crucial links between urban environments and underground transit systems and are particularly vital in cities with cold climates. Specialized design strategies are essential to address user needs and promote safety and comfort, thereby encouraging sustainable travel in harsh winter conditions. This research utilizes data from Harbin and Shenyang, two winter cities in China, to explore the nonlinear influences of subway entrance attributes on passenger satisfaction through the combined use of gradient-boosting decision trees and asymmetric impact-performance analysis. The findings indicate that most key attributes of subway entrances impact passenger satisfaction asymmetrically, highlighting the significance of their hierarchical importance in generating satisfaction. These attributes are categorized into frustrators, dissatisfiers, hybrids, satisfiers, and delighters, based on their asymmetry levels. Considering the current performance of these attributes, the study identifies priority for improvement at Harbin and Shenyang’s subway entrances. This aids urban designers and city managers in making informed decisions for urban development and enhancing the overall commuter experience in winter cities.
... (Brown, 2015). (Mikulić and Prebežac, 2008). (Mikulić and Prebežac, 2008). ...
... (Mikulić and Prebežac, 2008). (Mikulić and Prebežac, 2008). ...
The right of mobility is a fundamental entitlement protected by Korean law for individuals facing transportation disadvantages. However, practical implementation often falls short, resulting in unjust constraints on daily life and societal engagement. Among those with transportation disadvantages, dedicated efforts are crucial to secure the rights of individuals with disabilities, considering their physical and/or mental conditions. Wheelchair users, in particular, require tailored solutions due to their reliance on mobility aids. This study seeks to furnish evidence for establishing priorities and enhancements to improve the accessibility of urban railway services, a key mode of public transportation for people with disabilities. To achieve this, the research focused on evaluating the convenience of using urban railway stations in Busan, Korea, through a survey targeting wheelchair users. Employing asymmetric impact-performance analysis, a theoretical and methodological enhancement over traditional importance-performance analysis, the study aimed to derive reliable results. The findings underscore the pressing need to enhance station elevators as the foremost priority for improving the convenience of urban railway stations for wheelchair users. Additionally, suggested areas for improvement, in order of priority, include wheelchair lifts, platform accessibility, and information signage and service staff assistance.
... The first type of method divides attributes into different strategy intervals in terms of attribute performance, impact asymmetry (IA), and importance. Such methods include IPA, AIPA, AISPA, IA analysis (IAA) (Mikulić and Prebežac, 2008), and AICA. IPA is one of the common methods to determine attribute priority. ...
... The Kano model is an approach that is often used to design or improve products and services [11][12][13][14][15][16]. The various terms "needs", "wants", "features", and "requirements" may be interchangeably used in engineering, marketing, and industrial design literature. ...
... Constraints (10)- (12) indicate that the product flow is always positive. Constraint (13) shows the equality and balance between the motivational level of the set of customer's needs regarding the product and the score of the carrier's first (motivational) criterion. ...
Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano's conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model ("must-be", "one-dimensional", "attractive" and "indifferent") can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.
