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Article

A Holistic Quality Improvement Model for Food Services: Integrating Fuzzy Kano and PROMETHEE II

by
Claudia Editt Tornero Becerra
1,*,
Fagner José Coutinho de Melo
2,
Larissa de Arruda Xavier
3,
André Philippi Gonzaga de Albuquerque
1,
Aline Amaral Leal Barbosa
4,
Lucas Ambrósio Bezerra de Oliveira
5,
Raíssa Souto Maior Corrêa de Carvalho
6 and
Denise Dumke de Medeiros
1
1
Planasp Group, Departamento de Engenharia de Produção, Universidade Federal de Pernambuco—UFPE, Recife 50740-550, PE, Brazil
2
Departamento de Administração, Universidade de Pernambuco—UPE, Recife 50100-010, PE, Brazil
3
Departamento de Engenharia de Produção, Universidade do Estado do Amapá—UEAP, Macapá 68900-070, AP, Brazil
4
Departamento de Engenharia de Produção, Universidade Federal de Campina Grande—UFCG, Campina Grande 58429-900, PB, Brazil
5
Departamento de Engenharia, Universidade Federal Rural do Semi-Árido—UFERSA, Angicos 59515-000, RN, Brazil
6
Departamento de Administração, Universidade de Pernambuco—FCAP/UPE, Recife 50750-500, PE, Brazil
*
Author to whom correspondence should be addressed.
Systems 2024, 12(10), 422; https://doi.org/10.3390/systems12100422
Submission received: 7 September 2024 / Revised: 3 October 2024 / Accepted: 8 October 2024 / Published: 10 October 2024

Abstract

:
Service quality is crucial to consumer loyalty. However, it is challenging to understand and meet customer expectations effectively. Translating customer feedback into actionable insights in the service industry poses difficulties, particularly without a systematic approach that balances customer requirements with business constraints and strategic objectives. This study proposes an approach that integrates customer perspectives into multi-criteria decision models by utilizing the fuzzy Kano model to capture service perceptions and minimize response uncertainty. It also uses 5W2H and PROMETHEE II to formulate service improvement actions and establish prioritizations, providing a structured framework for managerial implementation. When implemented in the food truck sector, this framework proves effective in addressing unique challenges, enhancing service quality, boosting customer satisfaction, and fostering loyalty. This study offers a valuable contribution to management by presenting a replicable model that aids managers in making strategic decisions, aligning customer perspectives with management efforts, and providing insights for continuously improving initiatives within the food service industry.

1. Introduction

Service quality in the food industry plays a fundamental role in consumer loyalty [1]. This relationship is influenced by the degree of satisfaction experienced by consumers, highlighting the importance of identifying service attributes that significantly impact their satisfaction [2]. By understanding and prioritizing these attributes, businesses can more effectively meet consumer needs, fostering greater loyalty [3]. However, despite the significance of service quality in enhancing customer loyalty, handling customer feedback is difficult for food service managers. Complexities arise from the inherently subjective nature of perceived quality, which varies significantly among individual consumers [4]. Interpreting customers’ often vague and uncertain comments further complicates managers’ ability to evaluate service quality impressions [5]. Additionally, understanding the precise impact of service quality on consumer satisfaction is neither simple nor easy [6]. Despite efforts to deliver high-quality service, this does not always result in a proportionally positive customer perception of satisfaction [7].
Food service managers face the task of improving service and ensuring customer satisfaction while considering resource constraints and aligning with strategic business objectives [8]. Integrating the subjective and uncertain nature of customer feedback into the strategic decision-making process remains an ongoing challenge. Strategic decisions are usually analyzed exclusively from the provider’s perspective, without adequately considering the customer as a crucial actor. Practitioners often lack a practical framework to evaluate consumer impressions, determine service attributes that impact satisfaction, and identify improvement strategies [9]. This phenomenon is accentuated in small businesses, which face unique challenges due to resource limitations and family organizational structures [10].
Then, how can food service managers integrate customer feedback into their strategic decision-making processes to enhance service quality and improve customer satisfaction? This study addresses this challenge by providing a practical and customer-centered decision-making model. It employs the fuzzy Kano model to capture customer service perceptions and mitigate the imprecision and uncertainty inherent in customer response. Additionally, the study combines the 5W2H and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods to formulate improvement actions and establish prioritizations, providing a structured framework for managerial implementation. The utilization of PROMETHEE II in this study was deemed appropriate because of its capacity to deliver a comprehensive ranking of alternatives that assess multiple and potentially conflicting criteria. In contrast to other multi-criteria decision-making (MCDM) techniques, it is simple to implement, thus rendering it user-friendly while maintaining reliability [11]. The contributions of this study include an approach that aims to align customer perspectives with management decision making, enhancing organizational planning and facilitating the implementation of continuous improvement initiatives. Additionally, it addresses the growing demand for decision support and enhancement methods that effectively integrate customer perspectives while balancing business constraints [12].
The case study used to exemplify the proposed model concerns food trucks. Food trucks have emerged as a growing trend and have attracted considerable attention in the food service industry [13]. Despite this significant growth, there have been limited efforts to discuss service quality in the context of food trucks [14]. In this sector, distinct aspects of service quality significantly impact customer satisfaction [15], which relates to customer loyalty [3]. Despite sharing similar service elements with traditional restaurants, food trucks present distinctive characteristics [16]. Also, in this specific sector, hygiene and food contamination have become consumer concerns [17,18], influencing quality perception and customer satisfaction [16]. While food trucks have grown in popularity, there is a scarcity of studies examining their specific service quality attributes and their impact on customer satisfaction. Therefore, this study also aims to contribute to the food service field by providing a balanced and replicable model that assists managers in making strategic decisions to improve service quality and enhance customer satisfaction, yielding broader benefits for their businesses.
The remainder of this paper is structured as follows. Section 2 offers a brief review of the fuzzy Kano model, PROMETHEE II, and the integration of the Kano approach and MCDM methods. Section 3 outlines the proposed approach. In Section 4, a case study illustrates the application of this approach to the food truck sector. Finally, a discussion and the implications are presented, followed by the conclusions.

2. Methods

2.1. Fuzzy Kano Model

Based on the motivator-hygiene theory [19], Kano et al. [20] created the theory of “Attractive quality and Must-be quality.” This theory describes the relationship between two aspects—an objective aspect, such as physical sufficiency, and a subjective aspect, such as customer satisfaction—from a two-dimensional point of view [21]. The proposed model classifies customer requirements into the following categories:
  • Attractive elements (A) result in customer satisfaction. These attributes have a significant impact on customer satisfaction. However, their absence does not generate dissatisfaction;
  • One-dimensional elements (O) result in satisfaction when met and dissatisfaction when not met. These elements are considered “the more, the better” attributes;
  • Must-be elements (M) are taken for granted when met, but cause dissatisfaction when they are not. Customers expect these elements and thus consider them prerequisites;
  • Indifferent elements (I) do not lead to satisfaction or dissatisfaction. Clients do not consider whether these elements are present or not;
  • Reverse elements (R) cause more satisfaction due to their absence than due to their presence. A high degree of service performance for these attributes results in dissatisfaction.
The Questionable (Q) category represents a contradiction in responses to questions. Questionable answers should not be included in analyses [22]. The Kano questionnaire consists of question pairs: one refers to the consumer’s feelings when the attribute is present (functional question), and the other examines their feelings when the attribute is not present (dysfunctional question). An example of a pair of questions related to a food truck menu is shown below:
  • Functional question: How do you feel when a food truck offers a readable and visually appealing menu?
  • Dysfunctional question: How do you feel when a food truck offers an illegible and visually unappealing menu?
According to Albuquerque et al. [23], service attributes can be classified by combining these two responses in the Kano evaluation table (Table 1). For example, if the customer answers “I would feel very good” to the functional question and “I would feel nothing” to the dysfunctional question, then the service attribute will be considered an “Attractive” (A) attribute. In the traditional Kano approach, the final categorization of the attributes is determined according to the predominant frequency in the responses of all respondents.
The Kano method is a powerful technique for classifying a set of customer requirements [24]. Despite its practical strengths, one of the main criticisms in the literature is that it is unable to quantitatively estimate the relationship between customer satisfaction and the performance of specific attributes [7]. Several quantitative refinements have been proposed to address this limitation. Among these, the satisfaction indexes [22], the analytical Kano model [25], and regression methods [26,27] are particularly noteworthy. However, despite these advancements, the traditional qualitative approach of the Kano model remains popular in business and the social sciences [28]. Its strengths include its ease of use, straightforward analysis, and utility for product development, particularly when evaluating the presence or absence of attributes [29]. Nevertheless, the binary nature of the Kano model, which focuses on the presence or absence of attributes, does not effectively account for varying levels of attribute performance. This limitation can be particularly problematic in service contexts, in which more nuanced performance evaluations are necessary to reflect customer experiences accurately [28].
An alternative to gather more real perceptions of customers is fuzzy logic. Kano’s fuzzy approach interprets results more accurately and reasonably [9]. While the Kano model reflects the customer’s feelings in a single response, the fuzzy Kano model elicits customer perceptions that reflect human vagueness and uncertainty [30]. This notion was proposed by Zadeh [31], who introduced the fuzzy set theory to represent vague data. This theory relies on a function that reflects the scope of descriptive adjectives in human languages and is also a solution for capturing uncertainties such as ambiguity, vagueness, and fuzzy problems in life [32].
The fuzzy Kano model has the advantage of capturing complex and uncertain feelings in the customer’s responses, which can be expressed by the degree of possibility of obtaining customer satisfaction regarding the performance when an attribute is fulfilled (delight/positive) and dissatisfaction when the attribute is not fulfilled (dislike/negative). By applying the fuzzy mode to the Kano questionnaire, the linguistic scale, as shown in Table 2, enables the representation of customer evaluations. These evaluations are articulated based on the intensity of emotional responses, categorized by linguistic intervals that quantify the extent of positive delight or negative disgust elicited by a given attribute. This ability for clear and differentiated expression improves the objectivity of the classification compared to the traditional Kano model, which has been widely recognized in recent studies in various service sectors [5,33,34]. The fuzzy Kano model aligns more closely with human thinking; however, it still faces challenges like those of the traditional model, including time consumption, the inability to rank attributes within the same category, and sensitivity to minor changes [28].
In the food service context, Kano’s method is particularly relevant because of its simple ability to generate customer insights. Recent studies have evaluated how various aspects, such as food packaging [35], processing parameters [36], marketing elements [37], and customer complaints [38], influence customer satisfaction concerning food quality. However, its specific application in evaluating services, especially in the context of the food truck sector, is non-existent. This study addresses the gap in applying the fuzzy Kano method in the context of food service, providing actionable insights for service improvements tailored to the unique dynamics of the food truck sector.

2.2. PROMETHEE II

Decision making is a ubiquitous activity in everyday life, and many problems exhibit multi-objective attributes because decision makers often consider multiple goals when making choices [39]. In this context, PROMETHEE has emerged as a crucial tool in MCDM methods because it offers a variety of outranking methods. PROMETHEE operates under a non-compensatory rationale, by which criteria are evaluated independently, preventing the ability to trade or compensate for strengths in one area for weaknesses in another [40]. In this method, alternatives are evaluated and ranked through pairwise comparisons relative to the preference functions defined for each criterion to enable more informed and efficient decision making [41].
Brans [42] introduced two fundamental methods in the PROMETHEE methodology: PROMETHEE I (the partial ranking method) and PROMETHEE II (the complete ranking method). Subsequently, multiple extensions and additional techniques have been developed to address specific problems in the context of multi-criteria decision making. PROMETHEE III involves ranking based on intervals, whereas PROMETHEE IV applies to continuous cases [43,44]. PROMETHEE-GAIA [45] incorporates graphical representations using the Gaia module. PROMETHEE V [46] includes segmentation constraints, and PROMETHEE VI [47] represents decision processes akin to the functioning of the human brain. PROMETHEE II stands out for its consideration of the balance between the outgoing (positive) flow and the ingoing (negative) flow—that is, the net outflow, which results from the PROMETHEE I procedure. In this method, no incomparability remains, and thus, it provides a complete ranking [48], in which all alternatives under consideration are distinctly ranked from best to worst, with clear distinctions and no ties or incomparability between them.
The literature has pointed out some challenges associated with PROMETHEE methods, such as rank reversal [49], computational complexity, and sensitivity to criteria weights [50]. To mitigate these issues, researchers have proposed minor modifications aiming to reduce sensitivity to preference function parameters and address rank reversal [11]. Additionally, new versions of PROMETHEE have been developed along with hybrid methodologies to address these challenges effectively.
In practical applications, both PROMETHEE I and PROMETHEE II are recommended for analysts and decision makers. PROMETHEE II is preferred because of its ability to offer a complete ranking efficiently [48]. Furthermore, this method’s complete ranking is less sensitive to minor changes and facilitates the interpretation and discussion of results [51]. However, the incomparability analysis performed by PROMETHEE I is usually useful for making appropriate decisions as it identifies situations that require special attention [48].
In the food service field, PROMETHEE has proven to be effective in solving strategic problems in areas such as food safety risk management [52], supply chain management and packaging systems [53], and customer management and market segmentation [54]. However, there is a lack of studies that have focused on improving food service quality, and to date, PROMETHEE II has not been applied in food service settings. This study addresses this gap by integrating PROMETHEE II with the Fuzzy Kano model to propose a quality improvement framework specifically designed for food services, thereby enhancing decision making and service delivery.

2.3. Integration of the Kano Model and Multi-Criteria Decision-Making Methods

Various approaches have addressed the limitations of the traditional Kano model, enabling the more effective use of customer feedback. The most valuable contributions emerge from integrating the Kano model with MCDM methods, as these approaches provide a more robust framework for addressing real-world decision-making problems by incorporating the customer’s voice more systematically and quantitatively [55]. Recent applications vary across MCDM methods and contexts, with each offering distinct advantages and limitations. One of the most common combinations is Kano with the AHP (analytic hierarchy process) [56,57,58,59,60], in which the Kano model captures customer perceptions, while the AHP extracts customer requirements for core attributes, assigns weights to them, and manages multi-level specifications [56]. Another notable integration is Kano with the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) [61,62], which extends Kano’s classification of customer needs by ranking alternatives based on their proximity to an ideal solution [62]. Additionally, the Kano and DEMATEL (Decision-Making Trial and Evaluation Laboratory) [63,64,65] combination enhances the decision-making process by analyzing the influence of criteria on one another in hierarchical structures. Some studies have also incorporated fuzzy sets [33,66,67,68,69] to improve the accuracy and handling of uncertainty when eliciting customer perceptions.
The main advantage of these integrations is their ability to enhance customer-centered decision making by refining the decision process to prioritize customer satisfaction [70]. MCDM methods effectively oversee multiple and often conflicting criteria, allowing for more comprehensive assessments [71]. Furthermore, fuzzy logic in conjunction with Kano helps manage the uncertainty inherent in customer feedback [33]. However, combinations of the Kano model with PROMETHEE are scarce, and the existing ones do not manage uncertainty or imprecision in customer feedback [55]. PROMETHEE offers significant advantages over other methods, as it is a more flexible ranking technique, employing non-compensatory decision making [71]. This means that trade-offs in performance between criteria are not permitted, providing a more realistic approach when decision makers do not wish to offset a deficient performance in one area with excellence in another [72]. PROMETHEE’s outranking flows also provide a clearer indication of how much one alternative is preferred over the others, preventing undue compensation across criteria [50].
Studies that have proposed integrative approaches have revealed significant shortcomings. Primarily, these models have been applied in specific contexts, in product design and logistics operations, which limit their generalizability to other areas; one method often compensates for the deficiencies of another in achieving particular research objectives. Moreover, many of these approaches are complex and difficult to implement, frequently lacking detailed insights into the challenges encountered during applications. There is also a noticeable lack of exploration into the integration of the PROMETHEE method with the Kano approach for decision making, a combination that remains underutilized in the literature. Furthermore, a straightforward and practical framework is necessary to effectively leverage customer data and systematically formulate alternative solutions before the decision-making process, ensuring that the actions required to enhance quality and strategy are clearly defined. This study addresses these shortcomings by proposing a framework that combines PROMETHEE II with the Fuzzy Kano model and 5W2H to ensure that the necessary actions to enhance quality and strategy are clearly defined and actionable. Section 3 describes the methodology used to address these gaps in the literature in detail.

3. Proposed Model

The proposed integrated model includes customer perception in the decision-making process to improve food services. Although the model can be applied to various food service contexts, this study uses food truck services to illustrate its application and effectiveness. It begins by identifying service quality attributes that generate customer satisfaction. Customer perceptions of these attributes were collected through a fuzzy Kano questionnaire, with two subsets of questions: functional and dysfunctional. Subsequently, the responses were analyzed following the procedures of Wang and Wang [30] to quantify the customer feedback for the subsequent steps. Simultaneously, the process of determining service improvement actions was conducted with the support of the 5W2H tool to develop detailed and effective action plans to implement improvements in each service attribute, as recommended by Fofan et al. [55]. With these two sources of information, the PROMETHEE II method is applied to build and explore ranking relationships that support decision making, following the procedures outlined by Almeida et al. [72]. These guide the identification of specific service improvement actions aimed at maximizing customer satisfaction within the available resources of the service providers. The result of this model is a ranking of priorities for improvement actions that service providers can use as a guide. Figure 1 illustrates the proposed model, and the steps are detailed below.

3.1. Step 1—Determine Service Attributes

Organizations must identify the main quality attributes that align with customer needs and expectations. To achieve this, they can develop tailored measurement instruments or adapt existing tools based on a thorough literature review. By doing so, organizations ensure that they capture relevant data and insights that can effectively guide their service improvement strategies. After the set of service attributes are defined, they can be evaluated.
S = a 1 , a 2 , , a n
where:
S is the vector representing the set of attributes;
ai (for i = 1, 2, …, n) denotes the individual attribute; and
n is the total number of attributes.

3.2. Step 2—Collect Customer Data

Once organizations establish the service attributes, the next step is to design and administer a questionnaire to collect customer data. Practitioners can use different methods to gather customer feedback as input for the proposed model; however, this study opts for the Fuzzy Kano model due to its benefits, as discussed in prior sections. The fuzzy Kano questionnaire incorporates functional and dysfunctional questions to evaluate consumers’ perceptions of the performance of key service quality attributes. Responses were measured on a linguistic scale from “I would feel very bad” to “I would feel very good”, with participants also indicating the certainty or intensity of their feelings (on a scale from 0 to 1), as illustrated in Table 2. This additional measure captured how strongly the participants felt about their selected responses. In this step, it is crucial to emphasize the importance of collecting demographic and preference data from the respondents. This information will enhance the analysis by enabling cluster analyses and other advanced statistical methods.

3.3. Step 3—Analyze Customer Data: Apply Fuzzy Kano Analysis

The fuzzy Kano procedure proposed by Wang and Wang [30] serves as a foundational methodology for this step. It begins with estimating linguistic scale vectors, as illustrated in Equations (2) and (3).
F V = A f i % , B f i % , C f i % , D f i % , E f i %
D V = A d i % , B d i % , C d i % , D d i % , E d i %
where:
FV and DV represent the functional and dysfunctional vectors, respectively;
fi% (for i = 1, 2, …, n) denotes the functional response for the i-th attribute, expressed as a percentage based on the linguistic scale represented by A, B, C, D, and E; and
di% (for i = 1, 2, …, n) denotes the dysfunctional response for the i-th attribute, expressed as a percentage based on the linguistic scale represented by A, B, C, D, and E.
Once the linguistic vectors FV and DV are identified, the R matrix (5 × 5) is calculated using Equation (4).
R = F V T × D V = A f i % B f i % C f i % D f i % E f i % × A d i % B d i % C d i % D d i % E d i % R = r 11 r 12 r 13 r 14 r 15 r 21 r 22 r 23 r 24 r 25 r 31 r 32 r 33 r 34 r 35 r 41 r 51 r 42 r 52 r 43 r 53 r 44 r 54 r 45 r 55 = Q R R R R M I I I R M I I I R M I I I R O A A A Q
The R matrix can be interpreted as the Kano classification matrix, based on the paired responses to the attribute classification questions in the Kano model (Table 1). Subsequently, the degrees of possibility for each Kano category are calculated for each attribute (Equation (5)).
P o s s i b i l i t y = A A M M O O I I R R Q Q
Finally, the customer’s perceptions are quantified by calculating the positive coefficient of delight/positive (Di +) and the coefficient of disgust/negative (Di −), using Equations (6) and (7).
D i + = A i + O i R i A i + O i + M i + R i + I i
D i = O i + M i R i A i + O i + M i + R i + I i
The values Ai, Oi, Mi, Ri, and Ii are derived for the degree of possibility. Once the results of the D i + and D i coefficients have been determined, we proceed to calculate the value defined by “pleasure minus repulsion” ( D i + D i ) to quantify customer perception. These quantitative data are essential to the decision-making process in Step 5.

3.4. Step 4—Determine Improvement Actions for Each Service Attribute: Application of 5W2H

In this step, actions to improve each service quality attribute identified in Step 1 are defined. The decision maker can resort to various management tools. In this study, the 5W2H analysis is chosen, which consists of answering specific questions to analyze in greater depth the identified improvement alternatives (What will be done? Why will it be done? Where will it be done? When will it be done? Who is responsible for this? How will it be done? How much will it cost?). Thus, the set of alternatives O can be written as follows:
O = f S = f a 1 , f a 2 , , f a n = O 1 , O 2 , , O n
where:
O represents the application of the 5W2H analysis to the vector of attributes;
f(ai:) represents the evaluation of each i-th attribute through the 5W2H method, producing a corresponding alternative Oi; and
n is the total number of alternatives, which is equal to the number of attributes.
In the 5W2H analysis, the decision maker used structured questions to develop targeted improvement plans for enhancing specific service attributes. By answering these questions regarding all attributes, the decision maker can identify alternatives that consider resource constraints and align with their business strategy, forming a comprehensive set of plans to improve the business’s overall quality. Table 3 illustrates the use of this method for the attribute “Modern appearance of equipment and accessories”.
Once the set of alternatives O is defined, the decision maker must select the criteria used to evaluate each option. These criteria represent the key factors deemed most important by the decision maker. This model incorporates five criteria to guide the implementation of service improvements for strategic decision making [55]:
  • Cost (C1) refers to the total cost of implementing an alternative. Minimizing C1 is preferable for enhancing the overall efficiency, as more complex implementations incur higher expenses due to increased resource and management requirements;
  • Time (C2) indicates the time to implement the alternative. Minimizing C2 is desirable, as greater complexity leads to longer implementation times;
  • Impact on satisfaction (C3) refers to customer perception, expressed as D i + D i (“pleasure minus repulsion”), which reflects the effect of enhancing a specific quality attribute through an alternative and its subsequent impact on customer satisfaction. Maximizing C3 is preferable, as higher values indicate a greater positive impact on customer satisfaction;
  • Impact on strategic alignment (C4) ranges from an aligned strategy to a non-aligned strategy to the objectives of the business. Maximizing C4 is crucial, as higher scores reflect a stronger alignment of alternatives with strategic goals, enhancing the potential to achieve the organization’s objectives;
  • Personnel qualification (C5) represents the qualification level of the personnel involved in the implementation of the alternatives, ranging from fully qualified to unqualified. Maximizing C5 is advantageous because higher scores indicate a more skilled and knowledgeable workforce, which enhances the effectiveness of the implementation process.
Table 4 presents a concise overview of essential information for decision makers, outlining the characteristics of scale decision criteria, the categories of the generalized criteria selected, and the normalized weights allotted to each criterion.
Once the set of criteria C is defined, the decision matrix D can be established based on these criteria. The set of criteria C is represented as follows:
C = C 1 , C 2 , , C m
where:
Cj (for j = 1, 2, …, m) represents the j-th criterion; and
m is the total number of criteria.
The decision matrix D is then constructed as follows:
D = d 11 d 1 m d n 1 d n m
where:
dij represents the decision value for the i-th alternative based on the j-th criterion;
n is the number of alternatives; and
m is the number of criteria.

3.5. Step 5—Evaluate Improvement Actions: Application of PROMETHEE II

The PROMETHEE II method is an improvement method based on two stages: building the outranking relationship and exploring this relationship to support the decision process [62]. The first stage begins with determining the preference function to compare the alternatives under specific criteria. This consists of describing the intensity of the preference for alternative “a” over alternative “b” for each pair of attributes of the set of alternatives O, which is denoted by the preference function F(a,b) (Equation (11)).
F a , b = 1 , f a f b > 0 0 , f a f b 0
In the second stage, the outranking degree of each pair of alternatives is calculated using the preference index π(a, b), calculated with Equation (12).
π a , b = i = 1 m F j a , b × p j
where:
pj (for j = 1, 2, …, m) represents the relative importance of the j-th criterion;
pj > 0; and
j = 1 m p j = 1 .
Exploring the outranking relationship consists of evaluating the advantage or disadvantage of alternative “a” over all alternatives “b” as a function of the outgoing flow ϕ + and the ingoing flow ϕ , using Equations (13) and (14).
ϕ + a = b O π a , b
ϕ a = b O π a , b
Finally, the net flow ϕ is calculated using Equation (15), which provides an overall evaluation and ranking of alternatives.
ϕ a = ϕ + a ϕ a

3.6. Step 6—Perform a Sensitivity Analysis and Determine a Subset of Final Improvement Actions

The sensitivity and robustness analysis involves simulating two scenarios: one in which the value of the criterion with greater relative importance for the decision maker increases and the other in which the value of this criterion decreases. This variation is distributed proportionally among all criteria. Using the output of the ranking of alternatives, added to a sensitivity analysis and the Kano classification, subsets of service improvement actions are established and prioritized to guide the decision maker in implementation.

3.7. Step 7—Implement

This step involves the sequential implementation of improvement plans. During implementation, it is essential to recognize that the value variables and priorities may undergo modifications. Specifically, customer perceptions can shift over time, influencing the categorization of Kano categories. Furthermore, the priorities assigned to the criteria by service providers and the scenarios considered for the sensitivity analysis may also change. Consequently, the model should be reapplied to define the current sequential activities. It is advisable to conduct periodic reevaluations, particularly in response to specific situations that trigger significant changes in the variables and inputs.

3.8. Step 8—Future Extensions

Potential enhancements and extensions of the proposed approach involve incorporating artificial intelligence (AI) and information systems. AI-based techniques, such as predictive analytics and sentiment analysis, can extract valuable insights from qualitative data, enabling the prediction of customer behavior and preferences while identifying trends in customer satisfaction and dissatisfaction. This model can also be leveraged to enhance customer relationship management (CRM) solutions by automating the fuzzy Kano approach to analyze consumer data, ensuring that the CRM system functions as a centralized repository for all customer interactions and feedback. These enhancements make the framework adaptable, scalable, and responsive to emerging food trends and changing customer expectations.

4. Application of the Model

The case study used to illustrate the application of the proposed model (Figure 1) concerns the food truck sector. The food truck concept has emerged as a global phenomenon, gaining popularity worldwide [73]. However, in developing countries, the dynamics of service quality remain underexplored [74]. Previous research demonstrates that service quality significantly influences customer satisfaction and loyalty [3,15]. However, the literature exploring service quality attributes specific to the food truck industry in developing contexts is scarce. Additionally, there is a need to investigate how these attributes affect customer satisfaction and how this information can inform improvement strategies in the food truck sector. This study examines the food truck landscape in Northeastern Brazil, where food trucks have surged in popularity and become an integral part of urban life [75]. This case study presents an interesting context for testing the proposed model, as it displays the unique dynamics of interaction and innovation present in this context, warranting a thorough examination of their impact on consumer experiences.
The output of Step 1 is the identification of service attributes relevant to the case study. This research incorporates 36 attributes categorized into five dimensions of service quality, adapted from a prior study [76]. While validating the measurement scale falls outside the scope of this study, the selected attributes are grounded in a comprehensive review of the literature addressing service quality considering the food, retail, and local service characteristics specific to the context of this research [75,77,78,79,80,81,82]. Table 5 provides an overview of the five dimensions and the 36 attributes analyzed in this study.
In alignment with Step 2, the output comprises data reflecting consumers’ perceptions of the food truck service. Customer data were collected in Northeastern Brazil using convenience sampling, selected for its practicality in swiftly accessing participants from a specific region or locality [83]. Eligible participants included adults (18+ years), urban residents, and food truck customers. Data were gathered through Google Forms, and the survey was promoted across various social media platforms to enhance participation. A total of 547 valid responses were used for the analysis. The sample primarily consisted of young, educated consumers with diverse income levels and consumption patterns, characterized by occasional purchases and a preference for budget-friendly options. The sample profile is illustrated in detail in Table 6.
Step 3 involved analyzing customer data using a fuzzy Kano analysis. This method considers specific characteristics of the food sector, such as subjectivity, ambiguity, and imprecision in customer responses. The obtained results are shown in Table 7, which summarizes the fuzzy Kano model applied in this study. According to Table 7, most of the attributes (17 items) are “One-dimensional”, 13 attributes are “Attractive”, and six attributes are “Indifferent”.
In the decision context, the food truck service provider is the identified decision maker. In this case study, a pioneer owner of a food truck service in Northeast Brazil volunteered to develop and apply the proposed model, providing data from their business. The decision maker’s information was fundamental for the development of Step 4, which consists of the identification of specific actions, viable within resource limitations, to improve the service in each quality attribute. Improvement actions were evaluated for each attribute by considering the consequences associated with the objectives (Table 5). A matrix of the consequences of the alternatives was constructed using the weights assigned to each criterion (Table 4) and customer feedback. This matrix provided specific results for each combination of alternatives and criteria. Details on the consequence matrix can be found in Table 8.
Step 5 was consolidated using the PROMETHEE II method, whose input and output flow results are shown in Table 9. These results offer the first recommendation extracted from the decision model, which employs the outranking relation theory to obtain the ranking of alternatives.
Step 6 involved a sensitivity analysis, which consisted of evaluating different scenarios to modify the values of the critical criterion for the decision maker, in this case, the cost criterion (C1). In Scenario I, a +10% variation was applied to the cost criterion (C1), whereas in Scenario II, a −10% variation was applied to the same criterion (C1), due to its greater relative importance for the decision maker (0.3). The remaining variation was distributed proportionally among the other criteria. Although both scenarios showed an inversion in the order of the alternatives, this did not affect the robustness of the model because rank inversion in the PROMETHEE II method is a widespread problem when the net flow values (φ) of two alternatives are remarkably similar in a pairwise comparison [43].
Table 9 provides subsets of alternatives ranked in an order that can be implemented sequentially to maximize customer satisfaction. Step 7, which refers to the implementation of improvement actions, can be ordered as follows: Must-be, One-dimensional, Attractive, and Indifferent. For example, the decision maker should consider implementing actions such as arranging garbage bins around the consumption area (O11) and performing periodic maintenance of both the structure (internal and external) and the equipment (O5) as initial priorities with a direct impact on satisfaction, in that order. However, to achieve maximum customer satisfaction, it is suggested that the decision maker also considers the implementation of additional actions, such as providing direct access to the counter (O8), determining the workload per employee (O27), and determining the optimal operating hours for customers (O33). On the other hand, actions deemed as being of a higher priority by the decision model but that do not contribute significantly to satisfaction are placing legal documents visible to consumers and inspectors (O26) and guaranteeing adequate lighting levels (O6).
These results were presented to the decision maker, who is responsible for making the final decision regarding the order of implementation of the most relevant service improvement actions, to help achieve the strategic objectives of their business. The problem of order reversal should be considered as a final recommendation for the implementation phase. The decision maker should carefully examine which implementation scenario is most favorable before finalizing the order. In this context, the decision maker must evaluate the budgetary impact of improvement plans before investing in actions that are highly sensitive to change.
Step 8 proposes potential enhancements and extensions of the developed approach by incorporating AI-based models and information systems. In the context of the case study, these advanced solutions pose challenges and opportunities. Small food service businesses, including food trucks, may encounter barriers such as limited access to advanced technologies, as well as constraints in internet connectivity, digital literacy, and financial resources. However, cloud-based solutions and mobile applications represent feasible alternatives that can integrate key elements of the proposed model. As digitalization trends continue, these integrated models can become viable for enhancing business operations and customer service.

5. Discussion

The findings from applying the proposed model in food truck contexts provide valuable insights for food service providers. Most of the service attributes were classified as One-dimensional (17 attributes), indicating significant opportunities for food trucks to enhance service quality. By concentrating on the performance of these key attributes, food truck operators can effectively retain customers and improve their overall service delivery. To assist in this effort, the proposed model provides a systematic ranking of implementation alternatives, enabling sustainable service improvements. The second largest group of attributes fell into the Attractive category (13 attributes). The identification of attractive attributes highlights the variations in customer perceptions and reinforces the effectiveness of the fuzzy Kano approach in assessing customer preferences [33]. Given the competitive and informal nature of food truck businesses, these attractive elements can serve as crucial differentiators, helping to foster customer loyalty. For example, ensuring clear, unobstructed pathways and positioning the food truck in locations with easy parking can enhance customer experience (O8). Moreover, the identification of Indifferent attributes (six attributes) was equally relevant, as it helps food truck owners avoid investing resources in aspects that do not significantly impact customer experience. For example, food truck operators might reduce their efforts to display legal documents, such as permits or health certifications, at their service area by keeping them accessible for inspection upon request. This approach saves costs and time while ensuring compliance, allowing operators to focus on improving customer satisfaction through food quality and service speed.
Notably, the absence of Must-be attributes is a striking finding. Must-be attributes represent implicit customer requirements, such as cleanliness and food safety within the food truck context [17,18]. However, this study indicates that customers lack fundamental expectations that must be fulfilled to prevent dissatisfaction. This absence may be attributed to the nascent status of the food truck industry in this region, which has gained popularity over the last decade but has not yet matured to the level of traditional restaurants and other food service establishments. Additionally, the respondent profile reveals that the majority are young consumers who do not frequently engage with or spend significant amounts of money on this business. This implies that food truck service providers have significant opportunities to cultivate consumer loyalty by understanding customer preferences. Overall, the findings reinforce the critical role of service quality and customer satisfaction as essential strategies for achieving business success in the food truck industry, as highlighted in prior research [3,14,15].
Customer perception is a critical factor in making effective decisions within the food industry [6]. However, handling customer feedback requires caution. This study provides an integrative approach to decision making that addresses the lack of a clear method for utilizing this essential information resource—a common challenge faced by organizations [9]. The proposed model integrates the fuzzy Kano model with PROMETHEE II to incorporate both customer perspectives and managerial priorities, aiming to enhance service quality to satisfy both parties. Each method has its limitations when used independently. The traditional Kano approach primarily provides qualitative insights into the impact of product or service attributes on consumer satisfaction [24] and struggles with ambiguous customer responses, particularly in service contexts [7,28]. To overcome these flaws, fuzzy logic enhances the analysis of customer feedback [9]. The integration of the fuzzy Kano model with the 5W2H framework and PROMETHEE II addresses gaps in the literature. While PROMETHEE has been explored within the food industry [52,53,54], it has yet to be evaluated in a service context. Furthermore, the 5W2H framework facilitates the generation of alternative strategic decisions [55]. Together, these methodologies strengthen the decision-making process in a dynamic sector, as demonstrated in the case study context.
Compared to previous integrative solutions, the proposed model offers advantages and limitations. It facilitates strategic decision making and service quality management by providing a straightforward approach to evaluating service quality attributes that impact consumer satisfaction. This model supports continuous improvement actions as part of a management strategy to generate value and profit, addressing the significant drawback of decision support systems, which often overlook the business aspects of service quality [12]. Additionally, it presents valuable benefits over existing compensatory integrative approaches [57,58,61,62,63,66], including its flexibility in preference functions, use of sensitivity analysis and alternative ranking, and the ease of interpreting its results.
Despite its benefits, the model has notable limitations. The proposed model was applied to a single provider, which restricts the generalizability of the results across the broader food truck service industry. Although the findings may offer useful insights, the proposed solutions are specific to this provider and may not be directly applicable to others. Similarly, the characteristics of customer data, such as sample size and sampling methods, resulted in a dataset that may not accurately represent the broader consumer population. It is crucial to consider these factors when weighing criteria or assessing the significance of customer feedback in the model.
Limitations about the methods used in the model also have to be considered. Completing the questionnaire can be time-consuming. This study did not address the issue of questionnaire length, which arises from the need for both functional and dysfunctional responses. Additionally, the subjective nature of services necessitates nuanced applications and interpretations of the Kano model [28]. This study adapts service attributes based on a well-established body of literature on food service, ensuring meaningful results that are relevant and interpretable within this specific context. 5W2H proved valuable for initial planning due to its practicality and ease of use, although alternative methods could also be considered. Concerning PROMETHEE II, prior research highlights limitations, such as reversed rankings and sensitivity analysis, which are not addressed in this study. These limitations can affect the model’s generalizability and applicability across various food service contexts. Although the model may hold validity in diverse settings, a primary concern is its reliance on compensatory decision making, which can undermine the reliability of its recommendations for both customers and service providers. Addressing this issue is essential to determine the model’s appropriateness for specific contexts.
To address these limitations, future research should focus on gathering targeted customer data specific to particular food services to enhance the value of the findings. Streamlining the questionnaire to minimize its time-consuming nature is advisable, either by condensing the set of quality attributes or utilizing alternative data sources. Regarding the Kano model, this study employed the original qualitative approach for simplicity. Future research could explore its effectiveness along with other analytical or regression methods when measuring performance levels. Moreover, refining the 5W2H framework by integrating innovative problem-solving methodologies, such as TRIZ, design thinking, and value engineering, could further support strategic action development. Additionally, addressing the limitations of PROMETHEE II through the adoption of newer approaches and advanced methodologies [11,49,50] would strengthen the decision-making models in diverse contexts. Finally, future studies should validate the model’s findings across multiple providers to enhance its generalizability and identify broader patterns in service quality.

6. Conclusions

This study proposes an approach that combines the fuzzy Kano model, 5W2H, and PROMETHEE II to incorporate customer perceptions into businesses’ strategic decision-making processes to improve customer satisfaction. The application of this approach to food service contexts, particularly to food trucks, demonstrates the effectiveness and practicality of this model. The findings primarily reflect One-dimensional, Attractive, and Indifferent service attributes for food truck services. These results provide significant insights for food service providers who want to improve customer satisfaction but do not know how to interpret customer perceptions and use this information in their decision-making process to implement service improvements while accounting for resource limitations.
The proposed model utilized a fuzzy Kano model to analyze the customers’ perceptions of the service, which considered the ambiguity and uncertainty of this information. Moreover, the decision-making process is enhanced through the integration of the 5W2H framework, which systematically delineates the set of improvement actions. These inputs and PROMETHEE II as a decision-making method provide a multi-criteria framework that integrates customer feedback with essential business factors. This integrative approach yields a ranked list of actionable service improvement initiatives based on all evaluated criteria. By employing this methodology, companies can formulate strategic plans for service enhancements that maximize customer satisfaction, ensuring these initiatives are practical and contribute to a more efficient allocation of resources.
This study makes significant contributions by providing support for effective planning and decision making. The model satisfies multiple criteria and prioritizes a wide range of alternatives in a practical and accessible manner for managers. Its strengths are in facilitating continuous improvement actions and addressing gaps in traditional decision support systems, thereby enhancing value generation and profitability. However, the model does have limitations, including the time-intensive nature of the questionnaire and the subjective interpretation required for the Kano model. Future research should refine the model by incorporating alternative analytical methodologies and innovative problem-solving techniques to enhance its applicability and generalizability across diverse service contexts.

Author Contributions

Conceptualization, C.E.T.B., F.J.C.d.M. and D.D.d.M.; methodology, F.J.C.d.M., C.E.T.B. and D.D.d.M.; software, C.E.T.B., F.J.C.d.M., L.d.A.X. and A.P.G.d.A.; validation, C.E.T.B., F.J.C.d.M., L.d.A.X., A.P.G.d.A., A.A.L.B., L.A.B.d.O. and R.S.M.C.d.C.; formal analysis, F.J.C.d.M. and C.E.T.B.; investigation, C.E.T.B., F.J.C.d.M., L.d.A.X., A.P.G.d.A., A.A.L.B., L.A.B.d.O. and R.S.M.C.d.C.; resources, D.D.d.M., C.E.T.B., F.J.C.d.M. and L.d.A.X.; data curation, D.D.d.M., C.E.T.B., F.J.C.d.M., L.d.A.X. and A.P.G.d.A.; writing—original draft preparation, C.E.T.B., F.J.C.d.M., L.d.A.X. and A.P.G.d.A.; writing—review and editing, D.D.d.M., C.E.T.B., F.J.C.d.M., L.d.A.X., A.P.G.d.A., A.A.L.B., L.A.B.d.O. and R.S.M.C.d.C.; visualization, D.D.d.M., C.E.T.B., F.J.C.d.M., L.d.A.X., A.P.G.d.A., A.A.L.B., L.A.B.d.O. and R.S.M.C.d.C.; supervision, D.D.d.M. and F.J.C.d.M.; project administration, D.D.d.M., C.E.T.B., F.J.C.d.M. and L.d.A.X.; funding acquisition, L.d.A.X. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Programa de Apoio a Publicações Acadêmicas Nacionais e Internacionais da Universidade do Estado do Amapá (UEAP) (Edital No. 016/2024- PROPESP/UEAP).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

This study was published with resources from the Programa de Apoio a Publicações Acadêmicas Nacionais e Internacionais da Universidade do Estado do Amapá (UEAP) (Edital No. 016/2024- PROPESP/UEAP). Additionally, this study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001, the Conselho Nacional de Desenvolvimento Científico e Tecnológico — Brasil (CNPQ), and the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The proposed model.
Figure 1. The proposed model.
Systems 12 00422 g001
Table 1. Kano evaluation table.
Table 1. Kano evaluation table.
Dysfunctional Question (Negative)
Functional Question (Positive)I would feel very badI would feel badI would feel nothingI would feel goodI would feel very good
I would feel very badQRRRR
I would feel badMIIIR
I would feel nothingMIIIR
I would feel goodMIIIR
I would feel very goodOAAAQ
A = Attractive; O = One-dimensional; M = Must-be; I = Indifferent; R = Reverse; Q = Questionable.
Table 2. Fuzzy Kano questionnaire.
Table 2. Fuzzy Kano questionnaire.
AttributeQuestionI Would Feel Very BadI Would Feel BadI Would Feel NothingI Would Feel GoodI Would Feel Very Good
1Functional A f 1 % , B f 1 % C f 1 % D f 1 % E f 1 %
Dysfunctional A d 1 % B d 1 % C d 1 % D d 1 % E d 1 %
nFunctional A f n % B f n % C f n % D f 1 % E f 1 %
Dysfunctional A d n % , B d n % C d n % D d n % E d n %
Table 3. Example of application of 5W2H for an attribute.
Table 3. Example of application of 5W2H for an attribute.
A1: Modern Appearance of Equipment and Accessories.
5W2H ElementDetails
WhatPerform regular maintenance on equipment and accessories to enhance their appearance and functionality.
WhyTo ensure equipment looks modern, well maintained, and up-to-date, which can improve customer perception and user experience.
WhereAcross all locations and service areas where equipment and accessories are used (e.g., food preparation areas, serving counters, or storage sections).
WhenSchedule maintenance every 6 months or after significant use, with a total maintenance duration of 8 weeks per year.
WhoThe staff, in collaboration with external services as needed for specialized maintenance tasks.
HowCreate and implement a maintenance plan that includes thorough cleaning, repairs, and upgrades to worn-out equipment and accessories.
How muchEstimated budget of BRL 1000 for maintenance services, replacement parts, and labor costs.
Table 4. Characteristics of the criteria.
Table 4. Characteristics of the criteria.
LabelCriterionObjectiveValue FunctionWeightScale
C1CostMinimizeUsual0.30Monetary value (R$)
C2TimeMinimizeUsual0.20Week
C3Impact on satisfactionMaximizeUsual0.25Continuous ( D i + D i )
C4Impact on strategic alignmentMaximizeUsual0.15Highest Impact and Alignment|4
High Impact and Alignment|3
Medium Impact and Alignment|2
Small Impact and Alignment|1
No Impact and Alignment|0
C5Staff qualificationMaximizeUsual0.10Full qualification|4
High qualification|3
Moderate qualification|2
Low qualification|1
No qualification|0
Table 5. Quality attributes and improvement actions for food truck services.
Table 5. Quality attributes and improvement actions for food truck services.
DimensionAttributeImprovement Action (What)
Physical aspects1. Modern appearance equipment and accessoriesO1. Perform maintenance on equipment and accessories.
2. Visually attractive physical facilitiesO2. Improve the appearance of the facilities.
3. Readable and visually attractive menuO3. Improve menu design.
4. Clean and disinfected areasO4. Implement a cleaning routine.
5. Equipment and structure preservedO5. Carry out periodic maintenance of the structure (internal and external) and equipment.
6. Adequate lightingO6. Determine and ensure adequate lighting levels.
7. Proper equipment layoutO7. Evaluate and determine the best arrangement of the equipment.
8. Ease of access to the service areaO8. Provide direct access to the counter.
9. Convenient locationO9. Evaluate and determine the best location.
10. Comfortable space for consumptionO10. Determine a suitable space where clients feel comfortable.
11. Ease of access to garbage containersO11. Arrange garbage containers around the consumption area.
Reliability12. Delivery of the service in the promised timeO12. Inform the client of the time the food will take to prepare and comply with it.
13. Comply with promotions and offersO13. Specify offers and promotions.
14. Short waiting timeO14. Standardize preparation and delivery times
15. Respond to special requestsO15. Train staff in social skills.
16. Comply with health regulationsO16. Periodically check compliance with legal and hygiene requirements.
17. Safe operations and movementsO17. Implement a periodic checklist.
18. Right service the first timeO18. Train staff on their roles.
19. Availability of menu itemsO19. Estimate demand and plan production.
20. Error-free transactions and registrationsO20. Define corrective and preventive actions in case of errors.
Personal interaction21. Staff behavior inspires confidenceO21. Train staff (interdisciplinary training).
22. Safe behavior when handling foodO22. Periodically train staff in food safety.
23. The shelf life of food products is reportedO23. Label food products intended for consumption.
24. Direct interactions with staffO24. Create social campaigns.
25. Food preparation visible to customersO25. Implement an open-kitchen bar.
26. Legal documents visible to clientsO26. Place legal documents visible to consumers and inspectors.
27. Availability to meet ordersO27. Determine the workload per employee.
28. Personalized attentionO28. Personalize customer service.
29. Courteous and attentive staffO29. Reward the employee of the month for excellent service.
Problem resolution30. Easy returns or exchangesO30. Establish return and exchange policies for orders.
31. Sincere interest in solving problemsO31 Implement employee performance indicators.
Policy32. Expected quality of foodO32. Standardize the production process.
33. Convenient opening hoursO33. Determine the best service schedule for the customer.
34. Flexible paymentO34. Offer various payment methods.
35. Affordable priceO35. Update product sales prices.
36. Promotions and attractive offersO36. Establish marketing strategies for the product.
Table 6. Sample profile; N = 547.
Table 6. Sample profile; N = 547.
VariableCategoryn%
Age18–2325446.4
24–2915728.7
30–357313.3
36–41244.4
≥42397.1
SexFemale26147.7
Male28652.3
EducationHigh school499.0
Higher education38770.7
Graduate studies11120.3
Monthly income<USD 957.6127550.3
USD 957.61–1915.2311721.4
>USD 1915,238816.1
Not declared6712.2
Frequency of consumption (per month)1–242878.2
3–46111.2
≥55810.6
Average expenditure (per purchase)<USD 7.2036867.3
USD 7.21–23.9917331.6
>USD 2461.1
Table 7. Results of the fuzzy Kano model.
Table 7. Results of the fuzzy Kano model.
AttributeAOMIRQCategory D i + D i ( D i + D i )
132.1%10.6%13.6%41.3%1.4%0.9%I0.417−0.2300.646
231.5%26.7%18.3%21.6%0.8%1.1%A0.581−0.4481.029
334.6%21.2%16.0%26.2%0.9%1.1%A0.555−0.3670.923
416.4%54.7%20.8%6.2%0.5%1.5%O0.715−0.7601.475
525.5%30.9%23.0%19.0%0.7%0.9%O0.562−0.5361.098
633.5%13.2%14.7%37.3%0.7%0.6%I0.462−0.2740.736
734.9%11.3%12.8%39.6%0.8%0.5%I0.457−0.2350.692
836.6%17.6%14.5%30.1%0.7%0.6%A0.538−0.3160.854
939.3%22.2%13.2%23.3%0.9%1.1%A0.612−0.3480.961
1031.3%24.0%18.8%24.6%0.6%0.7%A0.550−0.4250.975
1128.1%29.5%20.4%19.4%1.2%1.4%O0.573−0.4941.067
1232.2%44.2%12.7%9.2%0.5%1.2%O0.769−0.5711.340
1322.1%52.1%17.4%7.4%0.3%0.9%O0.745−0.6981.442
1424.8%45.1%18.5%10.2%0.4%0.9%O0.702−0.6391.341
1524.7%39.1%21.4%13.5%0.5%0.9%O0.638−0.6041.242
1616.6%57.1%19.3%5.6%0.3%1.0%O0.742−0.7691.510
1722.5%36.9%24.6%14.9%0.4%0.7%O0.594−0.6151.208
1839.4%24.7%13.5%21.5%0.4%0.5%A0.640−0.3801.020
1939.2%23.0%13.6%23.2%0.4%0.5%A0.622−0.3650.987
2025.1%34.1%22.9%16.9%0.4%0.5%O0.592−0.5701.162
2126.5%29.3%22.5%20.4%0.6%0.7%O0.557−0.5161.073
2216.4%56.3%19.7%5.7%0.5%1.4%O0.733−0.7661.499
2328.5%17.6%19.6%31.7%1.5%1.1%I0.451−0.3620.813
2429.7%16.5%18.9%34.0%0.5%5.1%I0.459−0.3510.810
2532.7%18.2%17.2%30.9%0.4%0.5%A0.507−0.3520.859
2630.9%17.4%17.8%31.6%1.4%1.0%I0.474−0.3420.815
2740.8%23.9%12.6%21.6%0.4%0.7%A0.647−0.3631.010
2841.1%13.7%11.0%33.1%0.6%0.5%A0.545−0.2430.788
2919.0%51.4%20.4%7.6%0.5%3.0%O0.708−0.7211.429
300.1%37.0%19.6%27.1%0.4%0.7%O0.436−0.6671.103
3122.4%55.9%23.0%5.8%0.3%1.1%O0.726−0.7311.457
3235.9%42.2%11.3%9.6%0.3%0.8%O0.784−0.5361.321
3341.8%15.3%11.1%30.4%0.6%0.8%A0.570−0.2590.829
3446.8%22.4%9.4%19.7%0.7%0.9%A0.692−0.3141.006
3525.8%47.9%16.4%8.8%0.3%0.8%O0.740−0.6451.385
3644.7%29.5%9.7%14.7%0.5%1.0%A0.744−0.3901.134
Table 8. Consequence matrix.
Table 8. Consequence matrix.
Criterion
AlternativeC1C2C3C4C5
O11000.0080.64632
O2500.0041.02912
O3300.0020.92343
O4900.0011.47524
O550.0031.09832
O630.0020.73623
O7100.0040.69201
O850.0010.85433
O9400.0050.96133
O1050.0010.97523
O11100.0011.06724
O12200.0041.34032
O1315.0041.44222
O14500.0091.34141
O151000.0041.24214
O1660.0071.51044
O1750.0061.20822
O18500.0081.02033
O19100.0010.98732
O20300.0051.16233
O21500.0071.07323
O221000.0081.49934
O23500.0020.81342
O24200.0070.81022
O25300.0050.85933
O2615.0010.81533
O2750.0021.01042
O28150.0040.78821
O29400.0071.42921
O30200.0041.10332
O31100.0081.45730
O32500.00101.32142
O33100.0030.82942
O34300.0051.00623
O35200.0081.38543
O36400.00101.13431
Table 9. Ranking of alternatives, including sensitivity analysis.
Table 9. Ranking of alternatives, including sensitivity analysis.
CategoryAlternative φ + a φ a φ a RankScenario I (+10%)Scenario II (−10%)
OO1120.4010.509.900999
O519.3510.808.550101011●
O1617.0015.951.0501516●16●
O1315.6515.150.5001615●19●
O3014.7015.05−0.350191918●
O1714.6016.50−1.900202021●
O1213.2016.55−3.350222223●
O2013.5016.95−3.450232322●
O3513.3017.85−4.550242424
O410.9521.15−10.2002728●27
O3110.0521.00−10.9502827●30●
O219.1521.15−12.000303029●
O158.9523.50−14.5503133●31
O328.2522.80−14.5503132●32●
O147.6524.40−16.750343434
O296.4025.20−18.800353536●
O226.0525.20−19.150363635●
AO825.154.6020.550222
O2723.407.5515.850444
O3323.208.1515.05056●5
O1022.507.7014.80065●6
O1920.808.8511.950778●
O321.459.9011.550887●
O2818.2513.654.600131313
O2517.0013.453.550141414
O915.6015.150.4501718●15●
O3414.1016.80−2.700212120●
O1810.5519.10−8.550262626
O29.8521.10−11.250292928●
O368.4523.20−14.7503331●33
IO2627.453.2024.250111
O625.706.1019.600333
O2318.7011.956.750111110●
O718.6013.605.000121212
O2415.5015.300.2001817●17●
O112.0518.40−6.350252525
No change from the initial ranking. ● Change from the initial ranking.
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Becerra, C.E.T.; Melo, F.J.C.d.; Xavier, L.d.A.; Albuquerque, A.P.G.d.; Barbosa, A.A.L.; Oliveira, L.A.B.d.; Carvalho, R.S.M.C.d.; Medeiros, D.D.d. A Holistic Quality Improvement Model for Food Services: Integrating Fuzzy Kano and PROMETHEE II. Systems 2024, 12, 422. https://doi.org/10.3390/systems12100422

AMA Style

Becerra CET, Melo FJCd, Xavier LdA, Albuquerque APGd, Barbosa AAL, Oliveira LABd, Carvalho RSMCd, Medeiros DDd. A Holistic Quality Improvement Model for Food Services: Integrating Fuzzy Kano and PROMETHEE II. Systems. 2024; 12(10):422. https://doi.org/10.3390/systems12100422

Chicago/Turabian Style

Becerra, Claudia Editt Tornero, Fagner José Coutinho de Melo, Larissa de Arruda Xavier, André Philippi Gonzaga de Albuquerque, Aline Amaral Leal Barbosa, Lucas Ambrósio Bezerra de Oliveira, Raíssa Souto Maior Corrêa de Carvalho, and Denise Dumke de Medeiros. 2024. "A Holistic Quality Improvement Model for Food Services: Integrating Fuzzy Kano and PROMETHEE II" Systems 12, no. 10: 422. https://doi.org/10.3390/systems12100422

APA Style

Becerra, C. E. T., Melo, F. J. C. d., Xavier, L. d. A., Albuquerque, A. P. G. d., Barbosa, A. A. L., Oliveira, L. A. B. d., Carvalho, R. S. M. C. d., & Medeiros, D. D. d. (2024). A Holistic Quality Improvement Model for Food Services: Integrating Fuzzy Kano and PROMETHEE II. Systems, 12(10), 422. https://doi.org/10.3390/systems12100422

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