Kano model
The Kano model is a theoretical framework for product development and customer satisfaction analysis, developed by Japanese quality expert Noriaki Kano and colleagues Nobuhiko Seraku, Fumio Takahashi, and Shin-ichi Tsuji in 1984. Published as "Attractive Quality and Must-Be Quality" in the Journal of the Japanese Society for Quality Control, it originally classifies customer requirements into three categories—must-be, one-dimensional, and attractive—with later extensions including indifferent and reverse categories, based on the nonlinear relationship between feature fulfillment and satisfaction levels, using a two-dimensional diagram to plot satisfaction against dissatisfaction.[1][2] The model's categories reflect varying customer reactions to the presence or absence of product or service attributes, recognizing that customer satisfaction is asymmetric and not captured by traditional linear models. Classification occurs via a structured questionnaire for each attribute, posing paired "functional" (feature present) and "dysfunctional" (feature absent) questions with response options like "I like it," "I expect it," "I am neutral," "I can tolerate it," or "I dislike it." Responses are evaluated against a lookup table to assign categories, often supplemented by customer satisfaction index coefficients to quantify impact (e.g., prioritizing one-dimensional and attractive elements for development).[1][2] The model aids prioritization in design by focusing resources on high-impact features, with the caveat that categories evolve over time—attractives may become must-bes as expectations shift. Widely applied in manufacturing, services, and healthcare, it promotes competitive differentiation through targeted enhancements.[3][2]Overview
Definition and Purpose
The Kano model is a theoretical framework in quality management that classifies customer requirements for products and services into distinct categories based on how their fulfillment or absence influences overall satisfaction. Developed by Noriaki Kano and colleagues, it posits that customer reactions to features are not linear, challenging traditional assumptions that satisfaction simply increases with better performance. The model's core purpose is to guide product development and resource allocation by helping organizations identify and prioritize features that drive customer loyalty and competitive advantage. By differentiating between requirements that merely meet expectations and those that exceed them to create delight, it enables more efficient decision-making in design processes, focusing efforts on high-impact attributes rather than uniform improvements across all areas. Central to the Kano model is its explanation of the asymmetric relationship between feature performance and customer satisfaction, where the absence of certain basics causes significant dissatisfaction but their presence yields only neutral results, while other elements unexpectedly boost excitement without equivalent penalties for omission. This nonlinear dynamic underscores the need for a nuanced approach to customer needs beyond simple fulfillment metrics. The model is typically visualized as a two-dimensional graph, with the horizontal axis representing the degree of feature performance (from low to high fulfillment) and the vertical axis depicting customer satisfaction levels (from dissatisfaction to satisfaction), where various requirement categories trace out characteristic curves illustrating their differential impacts.Historical Development
The Kano model was developed by Noriaki Kano, a professor of quality management at the Tokyo University of Science, and formally introduced in 1984 as a framework to analyze nonlinear relationships between product features and customer satisfaction.[1] This innovation stemmed from Kano's research in ergonomics and quality control, aiming to address limitations in traditional linear satisfaction models prevalent in Japanese manufacturing during the post-war economic boom.[4] Kano's work drew key inspirations from established psychological and motivational theories, particularly Frederick Herzberg's two-factor theory, which differentiates hygiene factors that prevent dissatisfaction from motivators that drive satisfaction, and Abraham Maslow's hierarchy of needs, which posits a progression from basic requirements to higher-level aspirations.[4] These influences shaped the model's emphasis on asymmetric customer responses, where the absence of certain attributes causes dissatisfaction but their presence does not proportionally increase delight. The foundational concepts were outlined in the seminal Japanese paper "Attractive Quality and Must-Be Quality," co-authored with Nobuhiko Seraku, Fumio Takahashi, and Shinichi Tsuji, and published in the Journal of the Japanese Society for Quality Control.[1] Initially confined to Japanese academic and industrial circles, the model gained wider international recognition in the 1990s through English translations and integrations into global quality management texts, including adaptations presented by scholars like Shoji Shiba to Western audiences. This period marked its entry into broader literature on customer-oriented quality, influencing standards like ISO 9000 and fostering empirical studies in sectors beyond manufacturing. By the 2000s, the Kano model evolved through deeper integration with Total Quality Management (TQM) frameworks, notably Quality Function Deployment (QFD), enabling systematic prioritization of customer requirements in product design processes. It also aligned with Lean methodologies, particularly within Lean Six Sigma, where it supported waste reduction by distinguishing essential from value-adding features in operational improvements.[5] These developments amplified its impact in global industries, with applications documented in high-profile case studies from automotive and electronics sectors. In the post-2010 era, the model has seen adaptations tailored to agile and digital product development, incorporating iterative feedback loops for software backlogs and user experience enhancements in fast-paced tech environments.[6] This shift reflects ongoing refinements to accommodate dynamic customer expectations in digital ecosystems, as evidenced in contemporary agile prioritization tools and research on service innovation.[7]Customer Satisfaction Categories
Must-be Quality
The must-be quality category in the Kano model encompasses the fundamental attributes that customers anticipate as inherent to a product or service, serving as basic requirements for it to be deemed functional. These elements, when absent, generate significant customer dissatisfaction, but their fulfillment merely results in a neutral level of satisfaction without providing additional delight or positive impact. This asymmetric relationship distinguishes must-be quality from other categories, as exceeding these basics does not proportionally enhance perceived value.[1] These attributes function as hygiene factors, meaning they are often taken for granted by customers once met and rarely acknowledged as sources of satisfaction. Their presence establishes the essential baseline for acceptability, but failure to deliver them leads to sharp declines in customer perception. For instance, in consumer electronics, the ability of a television to turn on and off reliably exemplifies must-be quality, as its absence would render the device unusable and highly dissatisfying, yet its standard operation yields no particular praise. Similarly, accurate timekeeping in a table clock is expected without comment, but inaccuracies provoke frustration.[1] In the context of the Kano model's satisfaction-performance curve, must-be quality forms the horizontal baseline at neutral satisfaction levels, with non-fulfillment driving an exponential increase in dissatisfaction along the vertical axis. This positioning underscores their role in preventing negative outcomes rather than driving competitive advantage. Common real-world examples include safety features like functional brakes in an automobile, which prevent harm but do not excite when operational, or the core ability of a mobile phone to make calls, essential for basic utility yet unremarkable when provided. Legal compliance, such as food safety standards in packaging, also falls into this category, ensuring avoidance of dissatisfaction without elevating satisfaction beyond neutrality.[8]One-dimensional Quality
One-dimensional quality, also known as performance quality, refers to product or service attributes where customer satisfaction increases linearly with the degree of fulfillment, and dissatisfaction arises proportionally with inadequate performance. These features represent explicit customer requirements that are voiced as desires, where higher functionality directly correlates with greater satisfaction, but they do not typically exceed expectations to create delight.[9][10] In the Kano model, one-dimensional quality exhibits a straight-line relationship on the satisfaction-fulfillment graph, often depicted as a 45-degree diagonal axis, reflecting a direct proportionality: as the attribute improves from poor to excellent, satisfaction scales accordingly from dissatisfaction to satisfaction. These characteristics are typically articulated by customers during needs assessment, forming part of the "voice of the customer" and serving as key performance indicators in quality management. Unlike must-be qualities, which are baseline expectations whose absence causes strong dissatisfaction but whose presence yields neutral satisfaction, one-dimensional qualities involve higher expectation levels where fulfillment actively drives positive outcomes.[11][12] This category impacts the overall model by occupying the central diagonal, emphasizing that meeting these stated needs maintains competitive parity but rarely generates enthusiasm, as improvements yield predictable rather than exponential satisfaction gains. In product development, prioritizing one-dimensional attributes ensures alignment with customer-specified performance standards, supporting incremental enhancements over revolutionary changes.[10][11] Representative examples include battery life in smartphones, where longer duration proportionally boosts user satisfaction; vehicle speed, which enhances driving experience in direct relation to acceleration capability; and display resolution in monitors or TVs, where higher pixel density leads to clearer visuals and commensurate approval.[11][10]Attractive Quality
Attractive quality in the Kano model refers to product or service attributes that customers do not anticipate, yet their presence elicits strong positive satisfaction and delight, while their absence generates no dissatisfaction. These elements address latent or unspoken customer needs, providing unexpected value that enhances perceived quality without serving as a baseline expectation. Key characteristics of attractive quality include its innovative and surprising nature, often appearing as non-essential add-ons or novel enhancements that trigger emotional "wow" responses. Such features stem from a deep understanding of unarticulated desires, enabling differentiation in competitive markets by fostering enthusiasm rather than mere adequacy.[12] Within the model, attractive quality traces the upper, concave portion of the satisfaction curve, demonstrating nonlinear gains where incremental performance yields disproportionately high satisfaction benefits but incurs no downside risk for non-implementation. This asymmetry underscores its role in driving customer loyalty and advocacy through positive surprises.[12] Illustrative examples encompass unexpected innovations like a heads-up display projecting navigation data onto a vehicle's windshield or forward- and rear-facing collision radars in automobiles. In hospitality, complimentary room upgrades or personalized welcome amenities exemplify attractive quality by exceeding norms and creating memorable experiences.[12]Indifferent Quality
Indifferent quality attributes in the Kano model represent product or service features to which customers exhibit complete neutrality, such that their fulfillment or absence generates neither satisfaction nor dissatisfaction.[13] These elements are essentially irrelevant to the user's experience, as they do not contribute to perceived value or quality perception.[9] Such attributes are characterized by their low priority in customer expectations, often stemming from features that fall outside core usage scenarios or fail to align with user needs.[14] Resources devoted to developing or enhancing indifferent qualities yield no return in terms of customer loyalty or preference, making them inefficient for strategic allocation in product design.[9] For example, the color of internal cables in an electronic device or the specific typeface used in backend software documentation that users never encounter exemplify indifferent attributes, as they hold no bearing on overall satisfaction.[15] In the graphical representation of the Kano model, indifferent attributes plot as scattered points along or near the horizontal neutral axis, distinct from the curved trajectories of other categories and underscoring their negligible influence on the satisfaction-dissatisfaction continuum.[16] This positioning highlights their minimal strategic value, advising organizations to deprioritize them to focus efforts on higher-impact features. Over time, however, indifferent attributes may shift to other categories amid evolving market dynamics or technological advancements.[17]Reverse Quality
In the Kano model, reverse quality refers to product or service attributes that lead to customer dissatisfaction when present and satisfaction when absent, representing a counterintuitive reversal of typical expectations. This category arises from features that contradict a subset of customers' preferences, where fulfillment actively harms perceived value rather than enhancing it.[18][17] Characteristics of reverse quality often stem from inadequate segmentation of user needs, resulting in attributes that flip positive expectations into negative ones for specific groups; for instance, what delights one segment may overwhelm another due to mismatched assumptions about preferences or expertise levels. These attributes are identified through the model's paired questionnaire, where responses indicate dislike for the functional form and like for the dysfunctional form, highlighting variability across customer experiences.[18][19] In the graphical representation of the Kano model, reverse quality attributes plot below the neutral line, where increasing performance decreases overall satisfaction, signaling the need for removal, redesign, or targeted exclusion to avoid alienating users. This impacts product strategy by emphasizing the importance of customer segmentation in evaluation, as reverse elements can undermine broader satisfaction efforts if not addressed.[17][18] Representative examples include a preference for manual transmissions over automatic ones in vehicles, where the presence of automation dissatisfies drivers who value control, even if cost-effective. Similarly, complex website interfaces can serve as reverse quality for novice users in online booking services, where added functionality increases frustration rather than utility. Another case is over-engineering with excessive options in software, leading to dissatisfaction from decision overload among less experienced users.[18][19]Terminology and Concepts
Satisfaction Drivers
In the Kano model, satisfaction drivers refer to the mechanisms by which product or service attributes influence customer reactions, categorized primarily as dissatisfiers, satisfiers, and neutrals. Dissatisfiers include must-be quality attributes, which are basic expectations whose absence causes significant dissatisfaction but whose presence merely prevents it, and reverse quality attributes, whose presence actively generates dissatisfaction.[12][20] Satisfiers encompass one-dimensional quality attributes, which linearly increase satisfaction with improved performance, and attractive quality attributes, which delight customers unexpectedly without causing dissatisfaction when absent. Neutrals represent attributes that have no notable impact on satisfaction regardless of their presence or absence.[12] These drivers align closely with Frederick Herzberg's two-factor theory of motivation, which distinguishes hygiene factors—elements that prevent dissatisfaction by addressing pain points, akin to dissatisfiers—and motivators, which create positive gains and fulfillment, similar to satisfiers. In the Kano framework, hygiene drivers like must-be attributes focus on averting negative experiences, while motivator drivers such as attractive attributes foster excitement and loyalty. This integration allows organizations to prioritize attributes that not only meet baseline needs but also drive competitive advantage through enhanced customer delight.[21] Unique to the model are threshold effects observed in must-be attributes, where a minimum fulfillment level must be achieved to eliminate dissatisfaction; subpar performance below this threshold amplifies frustration, but exceeding it offers little additional benefit. For one-dimensional attributes, satisfaction hinges on expectation thresholds, with performance meeting or surpassing spoken customer needs yielding proportional gains, while shortfalls lead to equivalent dissatisfaction. Reverse attributes introduce a negative threshold, where even basic implementation can provoke aversion, such as an intrusive feature that alienates users.[12] Central terminology in evaluating these drivers involves functional and dysfunctional questioning pairs, employed in customer surveys to gauge reactions: functional questions assess feelings when an attribute is present and performing as intended, while dysfunctional questions probe reactions to its absence or failure, enabling classification into the model's categories without revealing the full methodology here.[1]Nonlinear Satisfaction Curve
The nonlinear satisfaction curve forms the graphical core of the Kano model, illustrating the asymmetric relationship between product or service attribute performance and customer satisfaction. The vertical axis represents customer satisfaction, ranging from high dissatisfaction at the bottom to high satisfaction at the top, while the horizontal axis depicts the degree of attribute fulfillment or performance, extending from poor or dysfunctional implementation on the left to excellent or fully functional on the right.[20][22] This diagram highlights the model's nonlinearity through distinct curve patterns for each satisfaction category, emphasizing that improvements in performance do not always yield proportional satisfaction gains. Must-be quality attributes follow a curve that remains low and relatively flat at dissatisfaction levels under poor performance, then rises steeply to a neutral satisfaction plateau once basic thresholds are met, with further enhancements yielding minimal additional benefit. One-dimensional quality attributes trace a linear diagonal path, where satisfaction increases directly and proportionally with performance improvements. Attractive quality attributes start near neutral satisfaction under low performance, forming a curve that rises sharply and nonlinearly toward high satisfaction as performance excels, often flattening at the peak. Indifferent quality attributes plot as a horizontal line centered at neutral satisfaction across all performance levels, indicating no impact. Reverse quality attributes appear below the neutral line, with a downward-sloping curve showing increasing dissatisfaction as performance improves, reflecting undesired features.[20][12] The nonlinearity underscores a qualitative asymmetry in customer responses: fulfilling attractive attributes can amplify satisfaction beyond expectations, creating delight disproportionate to effort, whereas failing must-be attributes causes frustration that basic fulfillment merely mitigates without equivalent uplift. This visual structure, with labeled axes and plotted category curves, enables prioritization by revealing how attribute performance unevenly influences overall satisfaction.[20][22]Model Dynamics
Attribute Evolution Over Time
The Kano model posits that customer requirements for product attributes are not static but evolve dynamically across satisfaction categories as market conditions and user expectations change. Initially, an attribute may function as an attractive quality, generating delight when present since it exceeds expectations; over time, it transitions to a one-dimensional quality, where satisfaction increases proportionally with performance; and eventually, it solidifies as a must-be quality, becoming a fundamental expectation whose absence causes significant dissatisfaction.[23] This progression reflects the model's recognition that delights are transient, as repeated exposure diminishes their novelty.[24] A classic illustration of this evolution is airbags in automobiles, which initially served as an attractive feature offering excitement through enhanced safety in the 1970s and 1980s, but over subsequent decades shifted to a one-dimensional attribute as consumers expected improved reliability, and ultimately became a must-be basic in modern vehicle standards.[25] The primary drivers of such shifts include market saturation, where widespread availability erodes uniqueness; competitor adoption, which standardizes the attribute across offerings; and technological normalization, as innovations integrate into everyday use and user familiarity grows.[26][23] Timeline effects vary by industry and context, but short-term delights typically fade into expected basics within years, with empirical studies indicating transitions from one-dimensional to must-be qualities often occurring over 5-7 years due to routine integration into consumer habits.[23] In some cases, attributes can regress into reverse qualities if they become outdated, leading to dissatisfaction when present because they no longer align with evolved preferences or introduce inefficiencies.[10] These shifts underscore the importance of continuous attribute reassessment in product strategy, ensuring organizations innovate beyond current must-bes to cultivate new attractives and sustain competitive differentiation. Failure to adapt risks commoditization, where former delighters fail to differentiate offerings in mature markets.[24]Factors Influencing Shifts
Shifts in the Kano model categories occur due to a variety of external and internal drivers that alter customer perceptions of product or service attributes over time. These factors can accelerate the typical progression where attractive qualities become one-dimensional and eventually must-be, or cause unexpected movements such as elevating indifferent attributes to higher-impact categories. Understanding these drivers is essential for organizations to anticipate changes in customer expectations and adjust their strategies accordingly.[27] Market factors play a significant role in category shifts, primarily through customer habituation, where repeated exposure to a feature diminishes its delight factor and transforms it into an expected norm. As customers become accustomed to innovative attributes, such as wireless charging in smartphones, what once generated excitement shifts to one-dimensional quality, requiring proportional performance to maintain satisfaction. Economic changes can further influence this by altering affordability and accessibility; during periods of prosperity, higher standards may elevate previously indifferent features to performance attributes, while recessions reinforce must-be basics like reliability. Regulatory updates often compel attributes to must-be status, such as mandatory safety standards in vehicles, where compliance becomes a non-negotiable baseline rather than an optional enhancement.[27][10][28] Competitive factors drive shifts by eroding the uniqueness of attractive qualities through imitation, turning differentiators into commoditized one-dimensional elements. When rivals adopt and standardize features like free WiFi in hotels, the original innovator loses its delight advantage, as customers begin to view it as a standard expectation rather than a surprise benefit.[10] This imitation effect is particularly pronounced in mature markets, where rapid benchmarking by competitors accelerates the downward migration of attributes, compelling companies to innovate continuously to maintain competitive edges.[29] Technological factors can disrupt category assignments by rendering established must-be or one-dimensional attributes obsolete or by promoting indifferent ones to attractive status. Advancements such as Bluetooth connectivity in cars can elevate previously overlooked features from indifferent to performance or even attractive, as they meet emerging needs for convenience. Conversely, breakthroughs like widespread high-speed internet adoption may obsolete older connectivity standards, shifting them from must-be to reverse qualities if they hinder user experience. These changes highlight how technological diffusion influences the nonlinear satisfaction curve, often requiring periodic reevaluation of attributes.[5][30] Organizational factors, including feedback loops and innovation cycles, can either hasten or mitigate shifts by integrating customer insights into development processes. Robust feedback mechanisms allow companies to detect early signs of habituation or competitive pressures, enabling proactive adjustments that prevent attractive qualities from degrading too quickly. Innovation cycles, when aligned with market dynamics, accelerate the introduction of new delighters, but misaligned cycles—such as infrequent product updates—can cause indifferent attributes to persist unnecessarily, missing opportunities for elevation. In agile environments, continuous feedback loops have been shown to sustain higher satisfaction levels by anticipating shifts before they impact customer loyalty.[31][32]Methodology
Data Collection and Evaluation
The data collection process in the Kano model begins with designing a structured questionnaire that captures customer reactions to product or service features through paired questions. For each feature under evaluation, respondents are asked a functional question, such as "How do you feel if this feature is present and functions as expected?" and a corresponding dysfunctional question, such as "How do you feel if this feature is absent?" Both questions utilize a five-point semantic differential scale: "I like it that way," "It must be that way," "I am neutral," "I can live with it that way," and "I dislike it that way."[1] This pairing allows researchers to assess nonlinear satisfaction patterns by contrasting positive and negative scenarios, as originally outlined in the model's foundational methodology.[1] Responses from the questionnaire are then classified using a 5x5 evaluation matrix that maps the functional and dysfunctional answers to one of six categories: attractive, one-dimensional, must-be, indifferent, reverse, or questionable. The matrix treats the five scale options as ordinal levels (e.g., 1 for "like," 2 for "must-be," 3 for "neutral," 4 for "live with," 5 for "dislike") and assigns categories based on combinations; for instance, a "like" response to the functional question paired with a "neutral," "live with," or "must-be" response to the dysfunctional question indicates an attractive attribute, while "like-like" or "dislike-dislike" pairs are flagged as questionable due to inconsistency. Reverse categories emerge when a feature's absence delights or presence frustrates, signaling a potential mismatch in expectations.[1] The overall evaluation follows a systematic sequence of steps to ensure reliable classification and prioritization. First, relevant features are selected based on preliminary customer insights or product specifications, followed by surveying a diverse sample of target customers to represent key demographics and usage patterns. Responses are tallied to compute frequency distributions for each category per feature, with the mode often used for primary classification. For prioritization, a satisfaction index is calculated to quantify impact, using the formula for the better coefficient: (number of attractive + number of one-dimensional) / (number of attractive + one-dimensional + must-be + indifferent), which ranges from 0 to 1 and indicates potential for satisfaction gains; a complementary worse coefficient, -(number of one-dimensional + number of must-be) / (number of attractive + one-dimensional + must-be + indifferent), measures dissatisfaction avoidance, with reverse and questionable responses typically excluded from the denominator or analyzed separately.[13] Statistical considerations are essential to validate results and mitigate biases. Recommended sample sizes range from 50 to 300 respondents per feature set to achieve a margin of error of 5-9% at 95% confidence, though smaller pilots of 12-24 can suffice for initial validation if resources are limited; larger samples enhance category stability, particularly for low-frequency attractive or reverse attributes. Ambiguities, such as questionable responses (typically 5-10% of data), are handled by exclusion or reclassification via follow-up clarification, while reverse cases prompt feature reevaluation to avoid misprioritization. Diversity in the sample—spanning user segments—helps address cultural or contextual variations in satisfaction drivers.[33][34]Example Analysis
To illustrate the Kano model's application, consider a hypothetical survey assessing customer reactions to three proposed smartphone features: a high-resolution camera, extended battery life, and AI-powered photo editing. The survey was administered to 100 respondents, using paired functional and dysfunctional questions to gauge feelings of satisfaction or dissatisfaction when each feature is present or absent. Responses were classified into the five Kano categories based on the standard evaluation table, where the dominant category determines the feature's primary classification.[35] For the AI-powered photo editing feature, the response distribution across categories was as follows: 40% attractive (A), 20% one-dimensional (O), 10% must-be (M), 25% indifferent (I), and 5% reverse (R). This distribution assigns the feature primarily to the attractive category, as it garners the highest percentage, indicating it delights users when present but does not cause significant dissatisfaction when absent. Similar classifications were derived for the other features: high-resolution camera (35% M, 30% O, 15% A, 15% I, 5% R; must-be category) and extended battery life (45% M, 25% O, 10% A, 15% I, 5% R; must-be category). Category assignments enable quantitative assessment via satisfaction and dissatisfaction coefficients, as extended by Berger et al. These indices quantify a feature's impact on overall customer satisfaction. The satisfaction coefficient (CS+) is calculated as: \text{CS}^+ = \frac{A + O}{A + O + M + I} The dissatisfaction coefficient (CS-) is: \text{CS}^- = -\frac{M + O}{A + O + M + I} For the AI photo editing feature, CS+ = (40 + 20) / (40 + 20 + 10 + 25) = 0.63, indicating moderate potential to increase satisfaction, while CS- = -(10 + 20) / (40 + 20 + 10 + 25) = -0.32, showing limited risk of dissatisfaction. In contrast, the high-resolution camera yields CS+ = 0.47 and CS- = -0.68, highlighting its strong role in both enhancing satisfaction and preventing dissatisfaction. Calculations exclude reverse responses to focus on valid classifications.[35] The results guide prioritization: attractive features like AI photo editing should be pursued for innovation and differentiation to delight customers, while must-be features such as the high-resolution camera and extended battery life must be reliably implemented as basic expectations to avoid dissatisfaction. This approach ensures resources align with nonlinear satisfaction dynamics, where exceeding must-bes yields diminishing returns compared to attractives' upside potential.[35]| Feature | A (%) | O (%) | M (%) | I (%) | R (%) | Primary Category | CS+ | CS- |
|---|---|---|---|---|---|---|---|---|
| AI Photo Editing | 40 | 20 | 10 | 25 | 5 | Attractive | 0.63 | -0.32 |
| High-Resolution Camera | 15 | 30 | 35 | 15 | 5 | Must-be | 0.47 | -0.68 |
| Extended Battery Life | 10 | 25 | 45 | 15 | 5 | Must-be | 0.37 | -0.74 |