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Article

Optimized Decisions for Smart Tourism Destinations: A Cross-Generational Perspective Using an Improved Importance–Performance Analysis

by
Elena-Aurelia Botezat
1,*,
Olimpia-Iuliana Ban
2,
Adela Laura Popa
1,
Dorin-Cristian Coita
1 and
Teodora Mihaela Tarcza
1
1
Faculty of Economic Sciences, Department of Management and Marketing, University of Oradea, 1 University Street, 410087 Oradea, Romania
2
Faculty of Economic Sciences, Department of Economics and Business, University of Oradea, 1 University Street, 410087 Oradea, Romania
*
Author to whom correspondence should be addressed.
Systems 2024, 12(8), 297; https://doi.org/10.3390/systems12080297
Submission received: 9 July 2024 / Revised: 5 August 2024 / Accepted: 9 August 2024 / Published: 12 August 2024
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Abstract

:
Our study introduces an enhanced version of the Importance–Performance Analysis (IPA) method, a powerful tool that can be applied across various domains. This method plays a crucial role in our research, aiding in making well-informed decisions about smart tourism destination attributes. We achieved this by evaluating how 911 consumers from four different generations (Baby Boomers, Generation X, Millennials, and Generation Z) rated these attributes based on their most recent tourist destination visit. Unlike traditional methods that often rely on subjective opinions or complex statistical models, the Improved IPA (IIPA) method offers a clear approach to decision-making. It enables decision-makers to focus on the most crucial attributes that drive consumer interest, thereby optimizing resource allocation and marketing efforts. Specifically, to remain competitive, decision-makers for smart tourist destinations should focus on queuing-time forecast and applications, websites, and content accessible for travelers with disabilities for Baby Boomers; e-complaint handling for Generation X; smart emergency response system for Millennials; and tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard systems, and accessible data about physical design features of accommodation, restaurants, and tourist attractions for Generation Z. Theoretically, this study advances the research on managerial decision-making by demonstrating the effectiveness of the IIPA as a clear and straightforward method for making optimal decisions about product or service attributes. In practice, the study provides decision-makers with valuable insights into the importance of different categories of smart attributes in shaping the overall holiday experience at a tourist destination for Baby Boomers, Generation X, Millennials, and Generation Z tourism consumers.

1. Introduction

In today’s interconnected world, the concept of ‘smart’ has permeated every aspect of our lives, including our leisure activities [1]. This trend has significantly influenced the development of tourism destinations, particularly in the context of post-COVID recovery and other challenges such as the ‘staycation’ phenomenon, escalating operational costs, and geopolitical uncertainties [2]. In this ‘smart’ paradigm, the role of tourism destination managers is to create value-in-use in the smart tourism experience, thereby enhancing guest satisfaction and fostering loyalty [3].
The task of managing tourism destinations is increasingly complex, as decision-makers are required to cater to the diverse needs of different generational groups of consumers [4]. These groups vary in their proficiency with information and communication technology but share a common trait of high expectations for constant innovation to optimize their satisfaction [5,6,7]. The ultimate goal of a tourism destination is to maximize both tourist satisfaction and its own competitiveness [8,9,10,11]. To achieve this, managers must be aware of what destination attributes are most valued by each generational group [7].
In the smart tourism destinations market, the ‘expected product’ is a combination of services chosen by guests and those offered by various providers to achieve customer satisfaction and encourage repeat visits [12]. With four generations simultaneously influencing tourism demand, the market is inherently heterogeneous within each generation. This necessitates a proactive and adaptive approach from the supply side to maintain market positions and cater to both younger and older generations.
Starting from these observations, in the present article, we focused our attention on how an improved variant of the Importance–Performance Analysis method, the IIPA, leads to optimized decision-making regarding smart tourism destination attributes as these were evaluated by 911 consumers belonging to all four generations. Our research, which is part of the prescriptive perspective of studying managerial decision-making, is unique in its focus on smart tourism destination decision-making by age groups. This focus is justified by the different tourist interactions-based information and communication technologies tourism literature [13,14,15,16]. Our study also contributes to the growing body of research on enhanced Importance–Performance Analysis (IPA) methods, leading to better decisions on products’ attributes [17,18,19,20,21]. Despite the effectiveness of the IPA method in improved versions designed to assist management more reliably in the decision-making process, our study is one of the few to address the question: How can managers arrive at optimized decisions on smart tourism destination attributes for each consumer cohort?
The present study, undertaken in 2023, aims to fill an important research gap by investigating the under-researched topic of smart tourism destination attributes evaluation among individuals from all four generational groups. Our study also seeks to showcase optimized decisions on smart tourism destination attributes for each generational group, using an improved IPA method approach developed by Ban et al. [18]. This research is not only a response to a general call for a generational approach in social-sciences-related academic papers [22] but also a specific call to test the improved IPA method on newly collected data sets [18]. By focusing on smart touristic attributes that need to be improved quickly and effectively to optimize destination attractiveness, our study aims to provide valuable insights for tourism destination managers.
After this introduction, the text is organized into four other sections. The first section presents present-day research on decision-making context, smart tourism destination attributes, cross-generational differences, and IPA methods, and the second deals with the research methodology. In the third section, we present the results, followed by a discussion on the optimal possible decision-making on smart tourism destination attributes for each generational group. This discussion includes practical implications for tourism destination managers, providing them with valuable insights for their decision-making processes. Conclusions and implications are presented in the last section, further highlighting the practical relevance of our research.

2. Review of the Scientific Literature

2.1. The Decision Problem in the Smart Tourism Destination Context

Smart destinations have harnessed the power of Information and Communication Technology (ICT), incorporating explicit information and smartphone applications, to significantly enhance the travel experience of tourists and the competitiveness of the tourism destination [13,23]. The seamless integration of ICT into all aspects of the tourism experience, including the Internet of Things (IoT), artificial intelligence, sensors, and cloud computing, ensures that tourists experience satisfaction as a continuous process, not just after a purchase [1]. This technological revolution also brings substantial benefits to the businesses and local economy of tourism destinations [12].
Satisfaction was grounded in literature as a global affective response based on an individual’s cognitive assessment of being adequately or inadequately rewarded for the sacrifice he/she has made [24]. The literature analysis shows that tourist satisfaction depends on product/service performance [25] and should be evaluated considering the entire experience of living in a destination [26]. Characteristically, tourist smart experiences should be understood as results of interactions with smart tourism technologies integrated into products and services sourced at smart destinations [19]. Simply put, tourists assess the attributes of smart tourism destinations, as Alegre and Garau [27] noted. Therefore, the decision problem regarding smart attributes must be a priority for the management of the tourism destination in seeking its competitiveness.
Moreover, being as good as or better than competitors require high-quality decisions about smart attributes because, in the tourism market, we are dealing with a pending offer. Russo and Shoemaker [28] argued coherently that the quality of the eventual decision depends significantly on how the situation is framed. How individuals frame decision situations reflect the paradigm they find most effective as a guide to understanding the complexity of the business environment. For example, following Romão et al. [29], we assume that tourists’ overall satisfaction results from their experience with each smart destination attribute. Briefly presented, satisfaction is assessed by tourists based on a comparison between the actual attributes’ performance and their expectations, sometimes formed only once they have interacted with the smart destination [30]. According to [31], cited by Franklin II, there are four gaps between the present state and the expected one, that can be interpreted as follows: “a. something is wrong and needs to be corrected; b. something is threatening and needs to be prevented; c. something is inviting and needs to be accepted; and d. something is missing and needs to be provided” [32] (p. 26). Any attempt to bridge any gap must begin with a detailed knowledge of the attributes of the smart tourism destination.

2.2. Smart Tourism Destination Attributes

The most important theoretical framework of smart tourism destination attributes research is offered by the Social Exchange Theory developed by Homans [33], which advanced the idea that a relationship between two parties involved in a possible acquisition is built up through cost–benefit analysis [34]. In this sense, online communities where people interact by sharing information, advice, and thoughts on their travel interests [35] represent an eloquent example. For their part, the tourism destination managers have begun to understand that the benefits targeted as objectives are closely related to improving the tourist experience, which determines the satisfaction of tourists vis-à-vis a specific destination. At the base of this managerial thinking, which can be synthesized by the expression ‘benefits follow satisfaction’, lies Füller and Matzler’s three-factor theory [36]. According to this theory, consumer satisfaction is achieved when the attributes of a product or service are part of the category of basic and performance factors. The lack of attributes that meet the (basic) needs leads to dissatisfaction without their existence necessarily producing satisfaction. In turn, the attributes targeting the performance factor that can evolve in both directions can generate satisfaction when performing optimally or dissatisfaction when they fail to meet the customer’s expectations.
The broadband Internet, mobile phones, and social media have created the possibility of connecting tourist service providers with consumers unprecedentedly [37]. With the expansion of their use by all generations of consumers, the smart tourist destination has been looking for ways to integrate technology into the products and services offered to facilitate a pleasant travel experience for its customers [23]. More specifically, smart tourism technologies as the basic infrastructure that integrates hardware, software and networks, travel services, and ICTs [38] include a variety of solutions, such as mobile devices and applications, the Internet of things (IoT), Wi-Fi, cloud computing, artificial intelligence, virtual reality, augmented reality, wearable devices, QR codes, near-field communication (NFC), radio frequency identification (RFID), social networks, and beacons [1,39,40].
In turn, scientific research in smart tourist destinations has tried to keep up. For example, Buhalis and Amaranggana synthesized the smart tourism application, its utility functions, and smart tourism destination dimensions such as smart mobility, smart people, and smart environment [8] (p. 559). Considering technology as an emotional, compositional, and spatiotemporal experience, Lemon and Verhoef [41] help us to think more clearly about smart tourism destinations’ dimensions and related attributes. Specifically, Femenia-Serra and Neuhofer [42] mentioned that tourism destination attributes include both established technologies such as public Wi-Fi, destination official websites or mobile apps, and more contemporary ones such as virtual and augmented reality tools, sensors, or beacons, namely, in scientific literature, ‘smart solutions’. In line with this, Corrêa and de Sevilha Gosling [11] add trust, security, and independence, viewed as a subjective dimension. As a result, we have identified in Wang et al.’s study [43] a list of suitable smart tourism destination attributes that can reliably serve for optimized decisions in that they are directly related to the preferences of tourists for a smart tourist destination. Considering Sustacha, Baños-Pino, and Del Valle’s [3] emphasis on the need to include security and privacy-related attributes in any smart destination research, we completed the list with operational attributes in sync with the ideas of Corrêa and Gosling [11]. Consequently, to survey the under-researched topic of the smart tourism destination attributes evaluation among individuals belonging to all four generational groups that can coexist in a tourism destination, we used a list of 40 attributes (see Appendix Table A1).

2.3. Baby Boomers, Generation X, Millennials, Generation Z—Main Considerations

Buhalis and Law [44] were among the first to point out that demographic characteristics primarily influence tourist consumer behavior on the ICT-integrated tourism platform. For this reason, generation is considered a good barometer of a tourist’s behavior [45] and a promising area of smart destination research. According to McCrindle and Wolfinger [46], a generation refers to people born within a similar period (15 years at the upper end) who share a comparable age and life stage and have been shaped by a particular period, including events, trends, and developments. Since there is no consensus concerning the exact years for each generation, the present work adopts McCrindle and Wolfinger’s classification stating that Baby Boomers refers to people born between 1946 and 1964, Generation X refers to individuals born between 1965 and 1979, Generation Y to those born between 1980 and 1994, while Generation Z implies individuals born from 1995 to 2010 [46].
The Baby Boomers were “the first generation to have experienced television and were exposed to relatively uniform mass messages while growing up” [47] (p. 305). Now firmly established in the older adult life phase, they have become one of the most potent forces for designing the market environment and tourism services [48]. However, the Baby Boomers’ outlook is formed both by ‘technology emigrants’ who do not use technology, apart from mobiles for calls and messages [49], and by ‘technology adopters’ who are skilled in the use of technology, yet, not so much as younger people [50]. According to Saša and Mateja [51], certain touristic services, particularly the do-it-yourself type, can be challenging to tourists without adequate digital equipment and specific technological competence.
Generation X members grew up in difficult economic conditions and are skeptical when buying different services [52]. According to Dabija, Bejan, and Tipi [53], Generation X members prefer seeking additional information about services and suppliers. Since Generation X was the first generation to grow up with computers and the emergence of the Internet has marked its development, they are technologically savvy and successfully adopt technology for their needs [54]. However, they have less ICT knowledge than the next generations, namely Generation Y and Generation Z [55].
Members of Generation Y, also referred to as Millennials, were the ‘wanted’ children. Millennials are often described as ‘rule-followers’ who cannot think ‘out of the box’ [56,57]. They are highly networked and very active on social networks. Internet technology and smartphones are innovations they adapted to [58]. Like consumers, they require a direct, personal connection that appeals to their emotions, principles, and attitudes [52]. They are more engaged in travel planning [59] and have a stronger affinity with, and rely more on, online information [53].
Generation Z is the youngest tourist generation. Internet technology and smartphones have always been a part of their lives [58]. They are immersed in digital technology [60] and seem more concerned with emotional, physical, and financial safety [61]. As consumers, they search for convenience and immediacy coupled with fun [62] and plan travel mainly via the Internet [63], where they would rather listen and watch the content than just read the text [5]. Moreover, they seem to be attracted by popular mobile-enabled games [64], mixed reality [65], and virtual tourism [66].
To sum up, the effective management of different generations in a smart tourist destination may sound like a complicated task in conditions where both digital skills and the use of smart tourism technologies differ across individuals and generational cohorts [67,68]. However, by analyzing consumer perceptions of smart destination attributes according to the general cohort to which they belong, tourism destination managers can learn what keeps them engaged and loyal and use this data to improve customers’ experience according to the generation to which they belong. This managerial task would allow decision-makers to assume a proactive attitude to reduce demand risk exposure through the decisions taken.

2.4. The Importance–Performance Analysis

Academics and practitioners have widely adopted the Importance–Performance Analysis (IPA) in explaining customer satisfaction and prioritizing the actions or improvements that can be considered effective management efforts in different research fields such as public services transportation [69,70], higher education [71,72], medical services [73,74], and tourism destinations [75,76].
The standard IPA, introduced by Martilla and James in 1977, combines measures of customers’ perceived importance and performance into a two-dimensional chart. This graphic interpretation, namely IPA matrix or grid, classifies attributes into four categories or quadrants, namely: “Keep up the good work”, “Concentrate here”, “Possible overkill”, and “Low priority” (Figure 1).
The attributes placed in the “Keep up the good work” quadrant (high importance, high performance) are presumed to be critical drivers of consumers’ preferences, and management’s function is to maintain the continuous performance flow of organizational behavior directed toward them. The “Concentrate here” quadrant (high importance, low performance) contains attributes that are also assessed to be critical drivers of respondent preferences but which, unfortunately, failed to achieve the expected level of performance. Improving the performance of this set of attributes is a priority to which managers must give the most significant attention because they represent the largest potential gains [77]. The attributes placed in the “Possible overkill” quadrant (low importance, high performance) must be carefully examined by managers through the lens of the relationship ‘effort expected/results obtained’, because consumers considered that they performed more than was necessary. The “Low priority” quadrant (low importance, low performance) included attributes valued by consumers that are relatively unimportant and low-performing; as a result, it should not be a priority or receive very much attention from management. At this point in the discussion, the interpretive and prescriptive ability of the standard IPA method is burdened by specific conceptual and methodological problems [78]. Specifically, interpreting and treating the so-named ambiguous/confusing attributes placed either near the two discrimination thresholds or in the same quadrant but at different distances from the segments of the discrimination thresholds that stake out each quadrant has been an ongoing topic of debate.
Distinctively, Ban et al.’s idea [18] was “to form a category of unambiguous/clear attributes by gathering around a prototype regarding as ideal the attributes which satisfy distance criteria in the importance-performance plane” (p. 1718). In their approach, Ban et al. [18] started from the IPA with nine categories of attributes created by Albrecht and Bradford [79]. The advantage of this IPA development is that by distributing the attributes in nine categories, the ambiguous/confusing attributes are placed in five areas, suggestively named by Albrecht and Bradford [79] as follows: Grey Zone, High B, Medium C, Low B, and Medium A (see Figure 2). In this way, the interpreting of and decisions upon attributes in the Competitive strength area (i.e., “Keep up the good work” in standard IPA), Competitive vulnerability (i.e., “Concentrate here” in standard IPA), Relative indifference (i.e., “Low priority” in standard IPA), Irrelevant superiority (i.e., “Possible overkill” in standard IPA) become more effective, since ambiguous or potentially confusing attributes have been removed. Ban et al. [18] went further. They chose the most representative attribute (regarded as ideal) for each of the four quadrants containing unambiguous/clear attributes to gather a new category of attributes that satisfy a distance criterion in the importance–performance plane. In this way, the same quadrant attributes that fall near the thresholds are naturally eliminated to avoid unjustified uniformity in interpreting and, therefore, in managerial recommendations.
Ban et al. [18] also open the possibility of influencing the size of quadrants by introducing a confidence value between 0 and 1. Imposing a natural condition, as the sum of the five areas corresponding to the ambiguous attributes from Figure 1, to be equal with the sum of the four areas attributes corresponding to the unambiguous/clear categories, Ban et al. [18] obtained a confident value equal to 0.293, independent from the entry data. Since this value gives the same chance to an attribute to belong to a precise or ambiguous category, Ban et al. [18] considered it an optimum and, consequently, a first option in decision-making. Thus, our study adopts Ban et al.’s [18] IIPA to showcase optimized decisions on smart tourism destination attributes for Baby Boomers, Generation X, Millennials, and Generation Z groups.

3. Research Methodology

This paper investigates how managers can arrive at optimized decisions on smart tourism destination attributes for individuals within Baby Boomers, Generation X, Millennials, and Generation Z based on their perceptions regarding the smart product and services attributes’ importance and performance using Ban et al.’s [18] IIPA. The study consists of three stages: questionnaire design, data collection, and data analysis.

3.1. Questionnaire Design

To achieve the study’s goal, we used the questionnaire as an investigation tool. The smart destination product and services attributes are drawn from the work of Wang et al. [43], in which 38 smart tourist attraction evaluation items were used to assess tourist preferences. Beginning from these 38 items and cross-referencing them with other related studies [11,80] and gray literature, we developed the 40 lists of items to assess both dimensions of smart destination attributes (i.e., importance and performance). All the items were operationalized to capture attributes’ importance, and performance underwent evaluation by five specialists to adapt the items taken from the specialized literature to the particularities of the Baby Boomer, Generation X, Millennial, and Generation Z Romanian groups.
More specifically, the questionnaire used in this study consists of three parts. The first part (section A) included five questions on the respondents’ acceptance requirements and general data such as birth year; only respondents having recent whole touristic experience (last two years) were invited to respond. The second part (section B) comprised five questions covering respondents’ perceptions and opinions covering 12 dimensions with 40 attributes related to smart touristic experiences in the most recently visited tourist destination. These 12 dimensions were chosen because they have been extensively theoretically validated by Corrêa and Gosling’s study on the formation of smart touristic experiences in the context of smart touristic destinations, according to travelers’ perceptions [11] (p. 15). After a general evaluation of the perceived value of smart touristic experiences, respondents were asked to assess the importance and the performance of the 40 smart touristic destination attributes, including ICT—information and communications technology (five items/attributes), Da—digital accessibility (four items/attributes), Sm—smart mobility (three items/attributes), Ste—smart tourist experience (two items/attributes), Itp—influence of third parties (three items/attributes), IS—infrastructure and sustainability (three items/attributes), Fd—facilities for travelers with disabilities (two items/attributes), T—trust (three items/attributes), S—security (four items/attributes), I—independence (three items/attributes), Wb—well-being (seven items/attributes), and Pt—personal transformation (one item/attribute) (as can be seen in Appendix Table A1). The 40 items reflecting smart tourism destination (STD) attributes were designed to collect data on a five-point Likert scale, with the responses for importance ranging from 1 = of very little importance to 5 = very much importance, and those for performance from 1 = strongly disagree to 5 = strongly agree. For both dimensions, importance and performance, we added an extra option, “I do not know/Prefer not to say”. The last two questions of this part were open questions related to the main strengths and weaknesses of the last visited tourist destination. The third part of our questionnaire was designed to collect respondents’ data regarding gender, occupation, level of education, vacation frequency, travel motives, and income level.

3.2. Data Collection

The online and paper-format questionnaire was distributed through over 90 interviewers (master’s students of the authors) to over 1000 Romanian citizens with a recent touristic experience, recruited via snowball sampling from February 2023 until the middle of June 2023. Each interviewee was instructed to conduct twelve interviews in public places and at the respondents’ workplaces or homes. All subjects provided informed consent for their data. A total of 990 questionnaires were collected, and after records deemed non-compliant (missing or incomplete data) were removed, 911 questionnaires were validated. The distribution of the number of respondents is as follows: 69 of the respondents are Baby Boomers (7.57%), 149 are Generation X (16.36%), 271 are Generation Y (29.22%), and 422 (46.32%) are members of Generation Z. The socio-demographic sample representativeness was realized by the initial training of interviewees who had to select respondents by using the Churchill [81] quota sampling method according to age. Appendix Table A2 shows several characteristics that describe the demographic profile of the respondents.

3.3. Data Analysis

The research methodology used in this study entails the standard Importance–Performance Analysis (IPA) and the Improved Importance–Performance Analysis (IIPA) proposed by Ban et al. [18] to better highlight how decisions regarding the smart attributes of the tourism destination can be optimized. Different Importance–Performance Analysis (IPA) variants were used in tourism studies [19,20,21,78,82,83,84], including in that of Wang et al. [43], but only Ban et al.’s [18] IIPA is suited to assist optimized decision-making on smart tourism destination attributes for Baby Boomer, Generation X, Millennial, and Generation Z cohorts. That is, this research study adopted a pragmatic approach consisting of two comparative IPA applications [72], previously used to assess the impact of new business requests such as digitization or moving towards green products/processes [69,70,71,85]. The four generational “importance” and “performance” IPA matrices (see Figure 3, Figure 4, Figure 5 and Figure 6) of the 40 destination smart tourism attributes were prepared using RStudio 2022.07.1 + 554 (version 4.2.1) software.

4. Results and Discussion

First, we conducted reliability analyses on all sets of items reflecting smart tourism destination (STD) dimensions/attributes. Data internal consistency was examined by Cronbach’s alpha, which resulted in a 0.94 value, an excellent value [86]. Second, the data analysis was performed using the widely accepted standard IPA and the more advanced optimum-value-based IPA (i.e., IIPA) for each generational group. The results were then analyzed and discussed both distinctively and by comparison. Appendix Table A3 summarizes generational groups’ importance–performance means for the 40 attributes investigated.
The two importance–performance charts for Baby Boomers are presented in Figure 3. The data used to construct the IPA grids were the overall means of importance and performance for all 40 items evaluated by Baby Boomers, which are 3.537 and 3.035, respectively.
Four attributes fell into the “Concentrate here” quadrant following standard IPA analysis and only two in the correspondent “Competitive vulnerability” quadrant following IIPA analysis—i.e., Wb1: queuing-time forecast and Fd1: applications, websites, content that is accessible for travelers with disabilities. These attributes were perceived by members of the Baby Boomer generation as weaknesses of smart tourist destinations and required immediate attention for improvement. In total, eight attributes were in the quadrant “Competitive strength” following IIPA—i.e., ICT1–ICT4: free Wi-Fi, various destination-related applications, short and multimedia-messaging service; tourist call center; Da1: intelligent-guide system; Da2: tourism destination home page; S3: weather forecast; and T1: mobile payment, compared to seven present in the IPA grid. These eight attributes are the strengths possessed by smart touristic destinations and should be well-maintained. The nine attributes that fell in the “Relative indifference” quadrant following IIPA analysis versus sixteen in the IPA variant are: Wb1–Wb3: queuing-time forecast, e-events calendar, electronic-ticketing system; Pt1: smart education; Ste1–Ste2: virtual tourism experience, augmented reality; Itp2–Itp3: online forums/travel discussions communities, travel blogs; and Da3: tourism destination home page in English. This indicates that neither of these nine attributes requires immediate attention as they perform at a level appropriate to the importance attached to them by Baby Boomers at the present time. However, the smart tourism destination should hold reserve resources to cope with a possible change of importance attached to these factors due to variations in seniors’ assessment of being adequately or inadequately rewarded for the staycation value they gave up.
The results of the standard IPA and the IIPA for Generation X respondents are shown in Figure 4. The data used to construct the IPA grids were the overall means of importance and performance, which are 4.024 and 3.502, respectively.
For Generation X respondents, following IIPA, T3: the e-complaint handling attribute in the “Competitive vulnerability” quadrant is the most significant evaluation element, with poor performance. This IIPA result differs significantly from standard IPA results that indicate six attributes on which management must act immediately. The nine attributes within the “Competitive strength” quadrant following IIPA include ICT1–ICT3: free Wi-Fi, various destination-related applications, short and multimedia-messaging service; T1: mobile payment; Sm2: e-tour map; Itp1: tourism platforms that contain accommodation, restaurants, attractions, or other recommendations; IS2: electronic-entrance guard system; S3: weather forecast; and Da2: tourism destination home page. These attributes are the strengths of smart tourism destinations according to the perceptions of Generation X. There are six attributes less than the standard IPA results show. Seven attributes fall into the “Relative indifference” quadrant following IIPA analysis versus sixteen in the IPA variant. These seven attributes likely to receive lower priority due to their relatively low importance are Ste 1–2: virtual tourism experience, augmented reality; I2: electronic toll collection; IS3: smart environment; Wb6: tourist-flow monitoring; Da4: online parking access; and Pt1: smart education.
Figure 5 summarizes the results of the IPA and IIPA analysis for Millennials. The overall means of importance and performance were, in this case, 4.219 and 3.674.
In the case of Millennials, the result of the IPA comparative analysis showed the same difference between the number of attributes in the four corresponding quadrants. Thus, if, in the IPA grid in the “Concentrate here” quadrant, we find seven attributes in the IIPA grid, only one attribute is distributed in the correspondent “Competitive vulnerability” quadrant—i.e., S4: smart emergency response system. Consequently, the attributes that need to be urgently improved to meet consumer expectations appear much more clearly. The attributes that held most continuously at a certain level can be found in the “Keep up the good work” IPA quadrant—fifteen—and in the “Competitive strength” IIPA quadrant—eight: T1: mobile payment; T2: electronic toll collection; ICT1: free Wi-Fi; ICT2: various destination-related applications; Da1: intelligent-guide system; Da2: tourism destination home page; IS2: electronic-entrance guard system; and Itp1: tourism platforms that contains accommodation, restaurants, attractions, or other recommendations. The three attributes distributed by the IIPA method in the “Relative indifference” quadrant are Ste1: virtual tourism experience, Ste2: augmented reality, and I2: quick response code. Obviously, being only three against the fourteen resulting from the IPA analysis, these three attributes are easier for managers to track in order to successfully cope with a possible increase in their importance in Millennials’ perception.
The two IPA grids for Generation Z are presented in Figure 6. The overall means of importance and performance for all 40 items were 4.109 and 3.703, respectively.
Figure 6 indicates that the following IIPA four attributes fell into the “Competitive vulnerability” quadrant—i.e., IS1: real-time traffic broadcast; IS2: Electronic-entrance guard system; Fd2: accessible data about physical design features of accommodation, restaurants, tourist attractions; and Wb5: tourist-flow forecast. Nine attributes fell into the “Competitive strength” quadrant, i.e., Itp1: tourism platforms that contain accommodation, restaurants, attractions, or other recommendations; Da1–Da3: intelligent-guide system, tourism destination home page, tourism destination home page in English; Wb4: online coupons; ICT1: free Wi-Fi; T1: mobile payment; Sm2: e-tour map; and Sm3: flexible smart vehicle scheduling. Finally, seven attributes fell into the “Relative indifference” quadrant, i.e., Da4: online parking access; Wb2: e-events calendar; IS3: smart environment; T3: e-complaint handling; Ste2: augmented reality; I2: quick response code; and Itp3: travel blogs. The number of attributes in the corresponding IPA quadrants is greater, as follows: seven attributes in the “Concentrate here” quadrant, 15 in the “Keep up the good work” quadrant, and 16 in the “Low priority” quadrant.
Table 1 exposes the results of the analysis of the data provided by 911 respondents from all four generations considered using IIPA, along with our action-oriented strategic recommendations.
At least three findings from this investigation are worthy of discussion. First, the results from the IIPA decreased the complexity of the managerial decision task by reducing the information load regarding the smart attributes of a tourist destination. This significant finding enlightens us about the potential of IIPA in simplifying decision-making processes. The clarity and precision of these findings instill confidence in the results. Thus, following the application of IPA for all four generations, the number of attributes subject to managerial attention to optimizing decisions decreased by at least half. More precisely, in the case of the Baby Boomers, the number of attributes clearly and precisely identified to make the best decisions decreased to 52.5%; in the case of Generation X, 57.5%; of Millennials, 70%; and of Generation Z, by 50%. As a result, the likelihood of making unfounded judgments based on biased information decreases in the conditions in which accuracy and speed of processing [87] appear as trustful characteristics for detecting decision-making process improvement actions [88]. This finding is in line with [89], who highlighted that decision-makers dislike ambiguity about the probability law of outcomes of their decisions.
Second, by infusing managerial decisions with the power of mathematics (i.e., choosing the best decision-making solution as a function of settled objective criteria, meaning an optimum confidence value), an essential source of cognitive biases can be avoided. Here, we are talking about the illusion of manageability [90,91] of four consumer generations in a smart tourism destination covering adults aged 18 to 78 (or over). Specifically, sieving the attributes of the smart tourist destination using the IIPA method leads us to the distribution of a maximum number of four attributes in the correspondent “Competitive vulnerability” quadrant. As mentioned above, this category of attributes represents smart touristic destinations’ weaknesses and requests for increased managerial attention, time, and expertise. As a result, a small number of attributes can redirect these saved latent managerial resources to make the best or most effective use of the situation or resources in building the overall smart tourist experience; this means considering the pre-travel, in-transit, and post-travel smart interactions [1]. It is interesting to note that the representatives of Generation Z indicated four attributes that represent weaknesses, compared to only one indicated by the representatives of Millennials and Generation X and two indicated by the representatives of the Baby Boomers. These results do not match those of Uysal [92] (p. 79), who indicated that Generation Z provides less positive or negative feedback about the products and services consumed than Millennials and Generation X. Instead, the fact that both Generation Z and Baby Boomers mentioned as weaknesses facilities for travelers with disabilities (Fd) and well-being or the physiological and psychological comfort experienced in a smart tourist destination (Wb) is in line with Wang, Hung, and Liu [93], who highlighted that Chinese seniors often travel abroad with their child or grandchildren from whom they ask ‘smartphone assistance’ along with emotional support.
Third, the IIPA method improves decisions upon attributes to be as effective as possible in the time, effort, and statistical knowledge required. Regarding time and effort, the IIPA method enables quality decision-making on attributes in a single step. For example, we give the IIPA results recorded in the “Relative indifference” quadrant as they appear in Table 1, row 4. To reach similar results, Daud et al. [94] required a two-step analysis in a study regarding improving the business curriculum using standard IPA. In the first step, they identified the dimension/factor in the correspondent IPA “Possible overkill” quadrant. Then, they took a second step to look at the four attributes for this factor through a second IPA standard application. In our study, the results appear after the first application of the IIPA method and through the codes assigned to the various dimensions/factors and attributes (see Appendix Table A1); they are easily understandable, as can be seen in Table 1. For example, regardless of generation, aspects related to Information and Communication Technology (ICT) and digital accessibility (Da) appear as strengths. Based on the codes in Appendix Table A1, it is straightforward to see the attributes in each category mentioned above. Notable is the fact that although IIPA appears obviously as a rigorous decision optimization method, it retains the simplicity and ease of use of the IPA method, as pointed out by other studies that proposed IPA improvements (e.g., [95]).
Conjointly, the present study’s findings allowed us to advance a set of action-oriented strategic recommendations, as can be seen in Table 1, last column. According to IIPA, understanding generational differences is key, with only one recommendation for a quadrant attribute or action per generational segment. A ‘quadrant attribute’ here refers to a specific characteristic or feature unique to a particular generational segment. In this way, IIPA supports at a higher level the efforts of decision-makers to successfully exploit Information and Communication Technology in a tourist destination, facilitating both an adequate organizational reaction and customized management of relations with different generations of customers by offering those smart tourist services of the type considered by them to be of utmost importance. By narrowing down the number of attributes that managers must focus on as a priority for different generational categories of consumers, IIPA enables the initiation and development of marketing efforts to generate the interest of different generational segments of consumers in certain smart services. These ‘smart services’ could include innovative technologies like T3: e-complaint handling, a system designed to efficiently resolve customer complaints, particularly appealing to Generation X consumers. This understanding of generational differences enlightens destination managers about the complexities of consumer behavior, making them feel more knowledgeable and prepared. However, when faced with IIPA’s recommendation for the same attribute T3: e-complaint handling, which was found to be relatively indifferent in the case of Generation Z respondents, destination managers play a crucial role. They can demonstrate their managerial expertise by employing differentiated marketing strategies for each generational consumer segment or prioritizing one over the other. Moreover, with the predicted technical innovation, a more personalized generational marketing communication system can be implemented, sending tailored messages to different generational groups and offering various smart touristic services. This underlines the integral role of destination managers in the process, making them feel empowered and central to the implementation of the recommendations.
In this way, we sought to capture the essence of the managerial decision and provide empirical evidence that IIPA is a suitable decision-making method that helps manage different consumer generations effectively.

5. Conclusions

Optimal decision-making on smart tourism attributes from a cross-generational perspective is one of the most critical issues under discussion in the ‘new normal’ marked by the technology in which we live. Identifying the potential gap between the most important attributes of the smart tourist destination for each generation and their current performance is part of the logic of seeking consumer satisfaction as a key element of financial performance and profitability in the conditions in which tourism decision-makers need “to re-evaluate their business models to incorporate reduced demand and increased costs” [96] (p. 12). Therefore, knowing with mathematical precision the significant attributes on which tourism consumer satisfaction depends [75] and making decisions based on the IIPA method developed by Ban et al. [18] allows both corrective and strategic actions to be taken to help destination management compete competitively in the market and remain attractive to all generations of potential customers [97]. This study shows that the following attributes must be prioritized and largely improved: a. queuing-time forecast and applications/websites/content that is accessible for travelers with disabilities in the case of Baby Boomers; b. the e-complaint handling attribute in the case of Generation X; c. the smart emergency response system in the case of Millennials, and d. tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard system, and accessible data about physical design features of accommodations, restaurants, and tourist attractions. Apart from the abovementioned findings, this study has theoretical and critical managerial implications.
Theoretically, this study moves the managerial decision-making research further by testing IIPA as a clear and straightforward method for making optimal decisions about a product or service attributes. Specifically, it extends the literature by showing the advantage that an optimum value, independent from entry data, may bring to move forward the interpretive and the prescriptive IPA property as a valuable decision-making tool tested on the smart tourism destination attributes, as these were evaluated by representatives of four generations of consumers.
Managerially, this study provides decision-makers with penetrating insights into the importance of different categories of smart attributes in building the overall holiday experience at a tourism destination for Baby Boomer, Generation X, Millennial, and Generation Z tourism consumers. Knowing with mathematical accuracy the smart tourism attributes that represent attraction or, on the contrary, vulnerabilities for each generation of consumers significantly contributes to optimizing the decision-making process in conditions of the probability of the manifestation of the touristic demand. Also, this paper proposes an optimized decision-making method that can easily be reproduced in other domains, such as transportation or medical services, to study what attributes are most valued by each generational group. Action-oriented strategic recommendations increase the managerial value of IIPA results. Decision-makers and planners can use these recommendations to build adequate offers based on the competitive strengths of one specific product or organization to meet the more demanding consumer needs effectively.
While this research offers a novel approach to an old debate regarding the use of IPA methods, and the results suggest that IIPA may be more useful in decision-making than previously improved methods, it also has limitations. First, this research was focused on the Baby Boomer, Generation X, Millennial, and Generation Z representatives’ perceptions of the most recently visited tourist destination instead of the focus on a particular destination. Although smart tourism destination technologies, such as mobile devices and applications, radio frequency identification, or social networks, are identical, the tourists’ perception experiences based on these can differ from destination to destination. Therefore, future research should consider the same touristic destination and the extent to which findings from the same destination align with our results. Second, due to the relatively limited sample size of the four embedded generations of study, the findings cannot be generalized to the Romanian population or other countries. Baby Boomer, Generation X, Millennial, and Generation Z tourism consumers would have different smart attribute perceptions depending on their country’s cultural particularity. Of course, the most (and least) culturally diverse countries in the world could be considered suitable goals for future research, as the proposed method can be easily adopted. Nevertheless, any findings should be judged with caution if Baby Boomers, Generation X, Millennials, and Generation Z are considered in their entirety and should not be automatically extended to the entire generation because of its heterogeneity.

Author Contributions

Conceptualization, E.-A.B. and O.-I.B.; investigation, E.-A.B. and O.-I.B., A.L.P., D.-C.C. and T.M.T.; methodology, E.-A.B., O.-I.B. and A.L.P.; software, O.-I.B.; resources, E.-A.B., O.-I.B., A.L.P., D.-C.C. and T.M.T.; data curation, A.L.P.; writing—original draft preparation, E.-A.B.; writing—review and editing, D.-C.C.; project administration, E.-A.B.; funding acquisition, T.M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this non-interventional study since participation was voluntary and anonymous.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data from this study is already in the figures and tables in the paper. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The theoretical Smart Tourism Destination (STD) dimensions along with the operationalized study attributes.
Table A1. The theoretical Smart Tourism Destination (STD) dimensions along with the operationalized study attributes.
STD Theoretically Validated CategoriesDimension DescriptionSTD Attributes Operationalized by Importance-Items and Performance-Items
Information and communications technology (ICT)The ICT aspects perceived by tourists as necessary for online communication and connectivityICT1: Free Wifi
ICT2: Various destination-related applications
ICT3: Short and multimedia-messaging service
ICT4: Tourists call centre
ICT5: GPS (Global Positioning System) signal
Digital accessibility
(Da)		Da represents the perceived possibility to obtain online information that should be complete, correct, and true, available in applications connected to one other, and easily accessed in more than one language Da1: Intelligent-guide system
Da2: Tourism destination home page
Da3: Tourism destination home page in English
Da4: Online parking access
Smart mobility
(Sm)		Sm refers to the physical accessibility of tourists to different places, facilitated by interconnected and shared online transportation modesSm1: Guiding-information service
Sm2: E-tour map
Sm3: Flexible smart vehicle scheduling
Smart tourist experience
(Ste)		A Ste derives from the interaction between tourism stakeholders, mediated by the information circulating through the ICTs, largely including interacting with multimedia interactive and/or enhanced websites Ste1: Virtual tourism experience
Ste2: Augmented reality
Influence of third parties
(Itp)		Itp refers to the influence of third parties established by travel reviews apps, social networks, online communities, and blogs on travel planning and decision-making. Itp1: Tourism platforms that contains accommodation, restaurants, attractions or other recommendations
Itp2: Online forums/travel discussions communities
Itp3: Travel blogs
Infrastructure and Sustainability
(IS)		I refers to the physical environment offering various public services and S refers to the environmental and socioeconomic benefits provided by a tourist destinationIS1: Real-time traffic broadcast
IS2: Electronic-entrance guard system
IS3: Smart environment
Facilities for travellers with disabilities
(Fd)		Fd refers to the tourist destination features, such as applications with audio description, sign language, as well as detailed and complete information providing digital accessibility and mobility for travellers with disabilities.Fd1: Applications, websites, content that is accessible for travellers with disabilities
Fd2: Accessible data about physical design features of accommodation, restaurants, tourist attractions, etc.
Trust
(T)		T refers to credibility that emerges from positive experiences belonging to a STD deposited by the tourists in tourism providers.T1: Mobile payment
T2: Electronic toll collection
T3: E-complaint handling
Security
(S)		S refers to the reduction of perceived travel risk derived from suitable information obtained through technological resources.S1: Real-time traffic broadcast
S2: Smart card (band)
S3: Weather forecast
S4: Smart emergency response system
Independence
(I)		I refers to tourist’s autonomy to make choices and get around STDI1: Electronic touch screen
I2: Quick response code
I3: Personal-itinerary design option
Well-being
(Wb)		Wb refers to the physiological and psychological comfort experienced by travellers in a STDWb1: Queuing-time forecast
Wb2: E-Events calendar
Wb3: Electronic-ticketing system
Wb4: Online coupons
Wb5: Tourist-flow forecast
Wb6: Tourist-flow monitoring
Wb7: Crowd handling
Personal transformation
(Pt)		Pt refers to the learning acquired at an STD that changes the travellers’s point of view, leading it to incorporate innovative ideas in everyday lifePt1: Smart education (destination should educate their tourists on how to best use the new technologies through the smart learning method)
Source: created by authors after [11,44,81].
Table A2. Demographic profile of the respondents.
Table A2. Demographic profile of the respondents.
Frequency Distribution
Baby BoomersGeneration X MillennialsGeneration Z Overall sample
VariablesNumberPercentageNumberPercentageNumberPercentageNumberPercentageNumberPercentage
Gender (Total)697.57%14916.36%27129.75%42246.32%911100%
   Female3144.93%5234.90%12245.02%15937.68%52357.41%
   Male3652.17%9563.76%13850.92%25460.19%36439.96%
   I prefer not to say22.90%21.34%114.06%92.13%242.63%
Education 697.57%14916.36%27129.75%42246.32%911100%
   Higher education3550.72%10671.14%21177.86%31875.36%67073.55%
   High school2637.68%4228.19%5821.40%9823.22%22424.59%
   Middle school811.59%10.67%20.74%61.42%171.87%
Professional or employment status697.57%14916.36%27129.75%42246.32%911100%
   Specialist job title1014.49%4530.20%8732.10%7517.77%21723.82%
   Service worker1014.49%2919.46%7929.15%6715.88%18520.31%
   Public function servant68.70%2114.09%217.75%184.27%667.24%
   Employment in agriculture22.90%00%62.21%20.47%101.10%
   Qualified worker710.14%42.68%62.21%276.40%444.83%
   State employee68.70%2214.77%228.12%133.08%636.92%
   Self-employed11.45%85.37%72.58%184.27%343.73%
   Entrepreneur22.90%64.03%207.38%174.03%454.94%
   Others (student, homemaker, retired, etc.)2536.23%149.40%238.49%18543.84%24727.11%
Frequency of travel/holiday (of at least 2 days) 697.57%14916.36%27129.75%42246.32%911100%
   a. 1–2 times a year4768.12%9765.10%11442.07%18944.79%44749.07%
   b. 3–4 times a year1826.09%3624.16%12245.02%15636.97%33236.44%
   c. 5–7 times a year45.80%128.05%248.86%4711.14%879.55%
   d. More than 7 times a year00%42.68%114.06%307.11%454.94%
Travel motives 697.57%14916.36%27129.75%42246.32%911100%
   a. Relaxation and health care3246.38%5436.24%11241.33%11727.73%31534.58%
   b. Professional interest45.80%149.40%2910.70%163.79%636.92%
   c. Visiting tourist attractions1826.09%5335.57%9033.21%21049.76%37140.72%
   d. Participation in cultural or sports events00%32.01%62.21%153.55%242.63%
   e. Visiting relatives or friends913.04%138.72%186.64%235.45%636.92%
   f. Other reasons68.70%128.05%165.90%419.72%758.23%
Income level 697.57%14916.36%27129.75%42246.32%911100%
   a. Under 2500 RON monthly1014.49%138.72%124.43%11928.20%15416.90%
   b. Between 2501 and 3500 RON monthly1521.74%3020.13%4918.08%11827.96%21223.27%
   c. Between 3501 and 4500 RON monthly1927.54%4429.53%9334.32%7517.77%23125.36%
   d. Between 4501 and 5500 RON monthly1318.84%3422.82%5620.66%389.00%14115.48%
   e. Over 5.500 RON monthly1217.39%2818.79%6122.51%7217.06%17318.99%
Table A3. Summary of Baby Boomer, Generation X, Millennial, and Generation Z respondents’ importance–performance means.
Table A3. Summary of Baby Boomer, Generation X, Millennial, and Generation Z respondents’ importance–performance means.
Baby BoomersGeneration XMillennialsGeneration Z
PerformanceImportancePerformanceImportancePerformanceImportancePerformanceImportance
1ICT13.854.184.364.384.354.504.164.38
2ICT23.764.024.184.204.084.314.054.14
3ICT33.654.183.954.183.874.253.824.18
4ICT43.4743.404.063.724.163.644.06
5Da13.5543.734.223.904.334.034.19
6Da23.343.844.104.164.194.403.994.29
7Da32.562.913.513.813.994.293.934.20
8I13.103.143.713.633.723.903.833.82
9I22.623.423.183.633.303.913.583.77
10ICT53.363.563.954.083.984.253.974.16
11I32.953.303.593.973.774.163.673.95
12Sm12.953.183.364.043.524.213.544.03
13Sm23.133.823.844.254.024.213.964.31
14S13.233.683.324.203.774.243.724.17
15S23.313.403.323.823.664.043.644.03
16S33.894.044.094.244.084.244.034.16
17Wb12.723.273.433.953.464.213.584.14
18Ste12.533.143.193.653.423.873.613.95
19Itp13.073.573.894.184.224.313.984.26
20Itp22.522.943.503.833.563.983.613.85
21Itp32.4933.503.693.613.983.493.70
22T13.653.864.064.444.164.584.134.36
23T23.283.553.654.023.924.373.824.10
24IS32.823.423.033.933.364.163.493.93
25S42.863.523.274.203.234.303.484.14
26Fd12.853.813.103.903.344.243.384.17
27Fd22.913.823.043.993.464.253.544.22
28T33.173.653.244.243.484.333.484.23
29IS13.244.023.514.353.494.533.494.32
30IS23.173.683.894.164.074.403.854.31
31Sm32.623.433.0843.414.243.443.97
32Da42.683.373.083.843.254.123.393.96
33Wb22.863.343.433.853.564.123.674.08
34Wb33.143.653.814.143.84.373.914.24
35Wb43.023.813.294.053.474.233.564.21
36Wb53.153.493.604.273.684.333.764.26
37Wb62.563.113.133.893.294.183.574.08
38Wb72.463.213.013.953.294.263.484.11
39Ste22.302.892.663.712.953.903.313.88
40Pt12.623.283.113.893.494.113.574.05
MEAN 3.0353.5373.5024.0243.6744.2193.7034.109

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Figure 1. The standard IPA grid.
Figure 1. The standard IPA grid.
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Figure 2. The nine-category attributes IPA grid.
Figure 2. The nine-category attributes IPA grid.
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Figure 3. IPA results (left) and IIPA results (right) for Baby Boomers.
Figure 3. IPA results (left) and IIPA results (right) for Baby Boomers.
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Figure 4. IPA results (left) and IIPA results (right) for Generation X.
Figure 4. IPA results (left) and IIPA results (right) for Generation X.
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Figure 5. IPA results (left) and IIPA results (right) for Millennials.
Figure 5. IPA results (left) and IIPA results (right) for Millennials.
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Figure 6. IPA results (left) and IIPA results (right) for Generation Z.
Figure 6. IPA results (left) and IIPA results (right) for Generation Z.
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Table 1. The smart tourism destination attributes’ distribution according to optimum value-based IIPA results along with the action-oriented recommendations.
Table 1. The smart tourism destination attributes’ distribution according to optimum value-based IIPA results along with the action-oriented recommendations.
Baby BoomersGeneration XMillennialsGeneration ZAction-Oriented Strategic Recommendations
Competitive vulnerabilityWb1, Fd1T3S4Wb5, IS1, IS2, Fd2Prioritize and largely improve
Competitive
strength		ICT1, ICT2, ICT3, ICT4, Da1, Da2, S3, T1ICT1, ICT2, ICT3, T1, Da2, Sm2, Itp1, IS2, S3ICT1, ICT2, Da1, Da2, T1, T2, IS2, Itp1ICT1, Da1, Da2, Da3, T1, Sm2, Sm3, Itp1, Wb4Maintain well
Relative indifferenceWb2, Wb6, Wb7, Pt1, Ste1, Ste2, Itp2, Itp3, Da3Wb6, Ste1, Ste2, Da4, I2, IS3, Pt1Ste1, Ste2, I2Wb2, Ste2, Da4, I2, IS3, Itp3, T3Give less priority but follow through
Irrelevant superiority----Economize the allocated resources
TOTAL
attributes		19171220
Surce: own research.
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MDPI and ACS Style

Botezat, E.-A.; Ban, O.-I.; Popa, A.L.; Coita, D.-C.; Tarcza, T.M. Optimized Decisions for Smart Tourism Destinations: A Cross-Generational Perspective Using an Improved Importance–Performance Analysis. Systems 2024, 12, 297. https://doi.org/10.3390/systems12080297

AMA Style

Botezat E-A, Ban O-I, Popa AL, Coita D-C, Tarcza TM. Optimized Decisions for Smart Tourism Destinations: A Cross-Generational Perspective Using an Improved Importance–Performance Analysis. Systems. 2024; 12(8):297. https://doi.org/10.3390/systems12080297

Chicago/Turabian Style

Botezat, Elena-Aurelia, Olimpia-Iuliana Ban, Adela Laura Popa, Dorin-Cristian Coita, and Teodora Mihaela Tarcza. 2024. "Optimized Decisions for Smart Tourism Destinations: A Cross-Generational Perspective Using an Improved Importance–Performance Analysis" Systems 12, no. 8: 297. https://doi.org/10.3390/systems12080297

APA Style

Botezat, E. -A., Ban, O. -I., Popa, A. L., Coita, D. -C., & Tarcza, T. M. (2024). Optimized Decisions for Smart Tourism Destinations: A Cross-Generational Perspective Using an Improved Importance–Performance Analysis. Systems, 12(8), 297. https://doi.org/10.3390/systems12080297

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