Volume 25 Issue 2 Article 3 June 2023 Relationship Between Customer Expectations and Financial Relationship Between Customer Expectations and Financial Performance of Food Industry Businesses in a Customer Performance of Food Industry Businesses in a Customer Satisfaction Model Satisfaction Model Petr Suchanek Masaryk University, Faculty of Economics and Administration, Brno, Czech Republic, Petr.Suchanek@econ.muni.cz Maria Kralova Ambis. University, Department of Economics and Management, Brno, Czech Republic Follow this and additional works at: https://www.ebrjournal.net/home Part of the Corporate Finance Commons, and the Marketing Commons Recommended Citation Recommended Citation Suchanek, P ., & Kralova, M. (2023). Relationship Between Customer Expectations and Financial Performance of Food Industry Businesses in a Customer Satisfaction Model. Economic and Business Review, 25(2), 103-117. https://doi.org/10.15458/2335-4216.1320 This Original Article is brought to you for free and open access by Economic and Business Review. It has been accepted for inclusion in Economic and Business Review by an authorized editor of Economic and Business Review. ORIGINAL ARTICLE Relationship Between Customer Expectations and Financial Performance of Food Industry Businesses in a Customer Satisfaction Model PetrSuchanek a, * ,MariaKralova b a Masaryk University, Faculty of Economics and Administration, Brno, Czech Republic b Ambis. University, Department of Economics and Management, Brno, Czech Republic Abstract Research on customer satisfaction in repeat purchases shows that the relationship between customer expectations and customer satisfaction can be inverse to what is commonly reported. This also has an impact on the nancial performance of an enterprise, which is therefore directly inuenced by customer expectations. The goal of this paper is to determine whether customer satisfaction affects customer expectations and whether these expectations have a direct impact on the nancial performance of an enterprise. The variables representing factors of customer satisfaction, including customer expectations, are measured using a customer survey. Business nancial performance (BFP) was measured using the ROA, ROE, and Asset T urnover indicators. The model was created using Structural Equation Modelling. The research conrmed a positive direct effect of customer expectations on BFP (specically ROA). Customer satisfaction impacted nancial performance indirectly via customer expectations in two years. This suggests that the inuence of customer expectations on BFP is long-term in nature, although this effect is rather weak. As customers make repeat purchases, customer expectations change. These changes reect relationships primarily with customer satisfaction and loyalty and BFP . Customer satisfaction is shown to inuence customer expectations, which in turn inuence BFP . Therefore, it is advisable to focus on (raising) customer expectations in repeat purchases if the businesses want to achieve higher nancial performance. Keywords: Customer expectations, Customer satisfaction, Food industry, Financial performance, ROA JEL classication: L25, L66, M31 Introduction C ustomer expectations are a signicant compo- nent of a wider framework of customer satisfac- tion, which may be why this variable has been subject to research since approximately the 1980s, generally in connection with customer satisfaction (see Miller, 1977, in Sachdev & Verma, 2002). Research on cus- tomer expectations has been focused either only on a specic part of business activity, such as supply (Lang & Bressolles, 2013), or on expectations regarding ex- ibility of deliveries (Gligor, 2018). The likely reason behind this is that customer expectations have gener- ally been researched as part of customer satisfaction models. Within complex models, customer satisfac- tion is most often affected by customer expectations (Cassel & Eklöf, 2001; Fornell et al., 1996; Wong & Dioko, 2013); however, a model has been validated in which the relationship is reversed, i.e., customer satisfaction affects customer expectations (Suchánek & Králová, 2019). Using the Kano model, Dinçer et al. (2020) investigated the relationship between cus- tomer expectations and customer satisfaction, which they then related to the performance of banks, nding that most customer expectations had a negative effect on customer satisfaction. However, the relationship between customer expectations and satisfaction is not nearly as clear (Johnson et al., 1996). The purpose Received 22 April 2022; accepted 3 May 2023. Available online 5 June 2023 * Corresponding author. E-mail address: Petr.Suchanek@econ.muni.cz (P . Suchanek). https://doi.org/10.15458/2335-4216.1320 2335-4216/© 2023 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). 104 ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 of this paper is therefore to clarify the relationship between customer expectations and customer satis- faction; and highlight its implications for business nancial performance. The relationship between customer expectations and business performance has been investigated in the context of CRM facility management (Hoots, 2005) or in relation to ROI within training programs in SMEs (Satiman et al., 2015). Growth of nancial per- formance based on growing customer expectations has been proven in the supply industry by Lang and Bressolles (2013). A positive relationship between - nancial performance, including ROA, and (future) customer expectations has been proven (from a sup- ply chain management perspective) across industries by Tan et al. (1998). There is no research directly addressing the relationship between customer expec- tations and business nancial performance from the perspective of a business as a whole. Understand- ing customer expectations is a prerequisite to offering excellent service (Parasuraman et al., 1991), and one may assume that the same applies to products. In this paper, understanding customer expectations is con- sidered equivalent to nding out what affects these expectations. As customer expectations increase in connection with increasing expected price (Hua et al., 2009), the conclusion may be drawn that (at least in the tourism industry) business protability will grow in tandem with customer expectations (assuming that growing prices will lead to growing revenues). The relationship between customer expectations and - nancial performance is therefore considered positive. As such, factors and variables which affect customer expectations are assumed to (indirectly) also affect business nancial performance. This highlights the importance of testing possible alternative connec- tions between investigated variables in such a way that managers obtain a complex view of all existing connections between important variables and factors which affect business nancial performance (through either customer expectations, satisfaction, or loyalty). If it is the goal of a manager to achieve predicted prots, they must be able to make decisions regarding customer satisfaction which will truly lead to achiev- ing that goal. Prior consumer expectations of a service measured after a service encounter will be affected by the type of experience, but consumers tend to shift their prior expectations to ensure their overall evaluation of the experience is justied (Clow et al., 1998). This leads to the question of whether customer expectations will remain stable over time or un- stable in the case of products (food), because the stability of customer expectations can change (Lin & Lekhawipat, 2016; Rufín et al., 2012). There are a number of approaches to measuring customer expec- tations, which change depending on whether they concern a product (Bayraktar et al., 2012; Bridges et al., 1995; Suchánek & Králová, 2019) or service (Eren, 2021; Parasuraman et al., 1991; Robledo, 2001). Moreover, some of these approaches focus on parts of the business, e.g., purchase intention (Mauri & Minazzi, 2013), employees (Choi et al., 2014), inno- vations (Berraies & Hamouda, 2018), etc., rather than the business as a whole. However, from a nancial performance perspective, it is imperative to evaluate the business as a whole based on its output, which is the product and its evaluation by the customer (Neely et al., 1995). As such, efforts to research the effect of customer expectations on business nancial perfor- mance as well as factors and variables which affect customer expectations should be based on a validated complex model of measuring customer satisfaction. Such a model should contain a number of fac- tors, including customer expectations (compare with Anderson et al., 2004; Eklof et al., 2020; Juhl et al., 2002). Current research on long-term (cumulative) cus- tomer satisfaction constructs and tests (among oth- ers) the effect of customer expectations on customer satisfaction and business nancial performance (An- derson et al., 2004; Eklof et al., 2020). However, these models do not take into account the fact that the rea- sons to buy may change with each repeated purchase, leading to changes in individual variables (compare with Lin & Lekhawipat, 2016; Rufín et al., 2012). These models (Anderson et al., 1994, 2004; Eklof et al., 2020) assume that the causal relationships between vari- ables including customer expectations do not change, in other words that they are stable in time. Some re- sults suggest that this may not be the case (Lin & Lekhawipat, 2016; Rufín et al., 2012). If the causal rela- tionships that affect customer expectations do change, the question is which relationships change and in what direction. This means that it is necessary to nd out whether some relationships within the com- plex model of satisfaction are oriented in a different direction. We are currently unaware of any research on customer expectations using complex models of cus- tomer satisfaction and their effect on business nan- cial performance. It is not obvious whether there are alternative causal relationships within the complex model of customer satisfaction which may affect busi- ness nancial performance. Furthermore, it is not obvious how these relationships may be oriented (whether they are positive or negative), nor how strong their effect on business nancial performance may be. We believe that this prevents managers ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 105 from making correct decisions in the long term (repeatedly). By reformulating the relationships between the variables (product knowledge) and factors (perceived quality, perceived value, customer expectations, com- petitiveness, customer satisfaction, and customer loy- alty) within the customer satisfaction model, this paper contributes to the theory primarily by testing the direct effect of customer satisfaction on customer expectations as well as by testing the direct effect of customer expectations on business nancial perfor- mance. Thus, the goal of this paper is to determine whether customer satisfaction affects customer ex- pectations in the food industry and whether these expectations have a direct effect on business nancial performance within the customer satisfaction model. This research on customer expectations and busi- ness nancial performance was conducted in two parts. The rst part investigated the customer satis- faction model; the other part investigated business nancial performance. The same approach was used by Dutta and Dutta (2009). Their research was fo- cused on customer expectations and nancial per- formance, but they did not address this relationship directly, i.e., how customer expectations affect nan- cial performance. Rather, they looked for differences in customer expectations and nancial performance across various groups of businesses—banks (Dutta & Dutta, 2009). The uniqueness of the approach of this paper lies in connecting investigated customers with businesses—the respondents were customers of the investigated businesses. Customer expectations were investigated using a complex model of customer satisfaction. The paper is organized as follows: the Literature review section analyses relationships between com- plex models of customer satisfaction and business nancial performance, especially with regard to the orientation of this relationship and the method of measurement. The Method section outlines variables and factors which are part of the model constructed in the Model section. This section also contains the hypothesis. This is followed by the Results section, where the results of the research are presented, and the Discussion section, which outlines the implica- tions of the results, which are also juxtaposed with current literature. Implications for theory and prac- tice of food industry businesses are presented in the Conclusion. 1 Literature review Complex models of customer satisfaction are com- posed of a variety of factors connected by positive and negative causal relationships. The models gener- ally differ from each other in specic factors and in the existence of certain causal relationships. However, all investigated models included customer expecta- tions (see, e.g., the EPSI model in Eklof et al., 2020; Swedish National Index in Anderson et al., 1994; American Customer Satisfaction Index in Anderson et al., 2004). Customer satisfaction may in some cases (Ali et al., 2020; Chi & Gursoy, 2009; Galbreath & Shum, 2012; Jyoti et al., 2017) be measured as a separate multi-dimensional construct; however, cus- tomer expectations are always one of the dimensions. It is therefore possible to focus on the relationship between these models (or constructs) and business nancial performance since customer expectations have (within the model or construct) an effect on performance. The effect of customer satisfaction models on - nancial performance is assumed to be positive (Eklof et al., 2020). In service industries, specically the banking sector, a positive relationship has been found between the EPSI model and nancial performance measured using ROA, ROE and other nancial indica- tors (Eklof et al., 2020), ROI, and the Swedish National Index (Anderson et al., 1994) or based on Tobin’s Q and American Customer Satisfaction Index—ASCI (Anderson et al., 2004). The models in these investi- gations also included customer expectations, which positively, directly as well as indirectly (Anderson et al., 1994, 2004), or only indirectly affected cus- tomer satisfaction (Eklof et al., 2020), thereby also positively inuencing (indirectly) business nancial performance. A positive effect of customer satisfaction on nan- cial performance was also proven by Berraies and Hamouda (2018) in the services industry (banking) using a variety of nancial indicators, including ROA and ROE. These indicators were, however, evaluated in connection to the previous time period subjectively by bank managers in comparison with competition. A positive relationship between customer satisfaction and subjectively measured nancial performance has also been found by Eren et al. (2013) in banks and by Agus et al. (2000) in manufacturing. Research by Anderson et al. (2004) demonstrated a positive effect of customer satisfaction (using ACSI, i.e., including the indirect effect of customer expecta- tions) on business nancial performance also in the food manufacturing sector, where this relationship was much weaker than in services (almost half as strong compared to banks, more than half as strong compared to retail). The literature demonstrates that a direct relation- ship can be seen when studying the general satis- faction of customers, where customer satisfaction is measured as a singular construct which also includes 106 ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 customer expectations (Ali et al., 2020; Berraies & Hamouda, 2018; Chi & Gursoy, 2009; Jyoti et al., 2017). Relationships between satisfaction measured in this way and business nancial performance have been found to be mostly positive, with the exception of Wiley (1991) and Foster and Gupta (1997), who repre- sent older research from the retail sector. A negative relationship can be expected in the services indus- try (Brown & Mitchell, 1993), specically for discount stores and shoe stores (Anderson et al., 2004). In the food industry, a positive relationship between cus- tomer satisfaction and nancial performance can be expected. When customer satisfaction is viewed as a singular construct, customer expectations represent a signi- cant component of customer satisfaction (Galbreath & Shum, 2012). However, upon closer look it be- comes clear that the construct also includes a number of other variables (e.g., competitiveness, loyalty, per- ceived value) which otherwise constitute part of a complex model of customer satisfaction, meaning that they are standalone constructs (Chi & Gursoy, 2009; Galbreath & Shum, 2012). If customer expectations represent an integral part of their satisfaction (as a standalone construct), then they positively and di- rectly affect nancial performance in the context of modelling Corporate Social Responsibility (CSR) (Ali et al., 2020; Galbreath & Shum, 2012) or employee satisfaction (Chi & Gursoy, 2009) or Total Quality Service—TQS (Jyoti et al., 2017). On the contrary, if customer satisfaction is mea- sured using a specialized structural model in which satisfaction, expectations, loyalty, perceived value, perceived quality, competitiveness, etc. are stan- dalone constructs, then the effect of customer satis- faction is indirect, via customer loyalty (Anderson et al., 2004; Eklof et al., 2020; Juhl et al., 2002). Per- ceived quality can be understood as an assessment of the recent experience of consuming the product (Fornell et al., 1996) or as a form of overall product evaluation (Snoj et al., 2004). This evaluation is sub- jective, i.e., it is the customer’s perception (Mitra & Golder, 2006). This paper thus approaches perceived quality from the marketing perspective (Stylidis et al., 2020). Perceived value is related to the consumer’s experience, knowledge, purchase and use of the prod- uct, and consumer perception; it cannot be objectively determined by the business and represents a trade- off between the benets and sacrices perceived by customers in the business offering (Snoj et al., 2004). Perceived value thus represents the overall mental evaluation of a particular product (Yang & Peterson, 2004). Competitiveness (of a product) is based on the assertion that the customer buys a product based on a comparison of the values of competing products (Dubrovski, 2001). Customer loyalty can be dened as the tendency or behavior to prefer the same product for repeated purchases, i.e., the consumer’s desire and behavior to opt for the same product when making a purchase (Khan, 2013). The relationship between customer satisfaction, loyalty, and nancial performance found through a specialized structural model is positive, meaning that an increase in customer satisfaction leads to an in- crease in loyalty, which causes an increase in nancial performance. In these models, customer expectations are a standalone construct which, directly or indi- rectly, positively affects customer satisfaction, and thus positively and indirectly affects business nan- cial performance (Anderson et al., 2004; Eklof et al., 2020; Juhl et al., 2002). In food manufacturing enter- prises, where customer satisfaction is measured using a specialized structural model, a positive and indirect relationship between customer expectations, their sat- isfaction, and nancial performance can therefore be expected. Financial performance is usually measured objec- tively using accounting data, since these data are relatively reliable (Tosi et al., 2000). Financial mea- surement of business performance based on account- ing data is also relatively widespread (Gunasekaran et al., 2005; Gupta & Galloway, 2003). On the other hand, it is relatively common in research to use subjective methods of measuring - nancial performance (Berraies & Hamouda, 2018). This kind of measurement is generally performed in cases where objective data are unavailable (Zulkif- i & Perera, 2011) or unreliable (Dess & Robinson, 1984), or due to the possibility to compare business performance across industries and contexts (Song et al., 2005). However, subjective measurement may be disrupted by the opportunism of evaluators or by cognitive restrictions (Bol, 2008). Research has proven that the results of objective and subjective measure- ment correspond to each other (Dess & Robinson, 1984; Wall et al., 2004). There is even research suggest- ing a strong correlation between these two methods of measurement (Dawes, 1999). In the context of customer satisfaction, business success is dependent on sales volume and conse- quently prot and viability (cf. Neely et al., 1995). This makes business viability indicators, specically ROA, (cf. Anderson et al., 1997; Terpstra & Verbeeten, 2014; Yeung et al., 2002) key variables when evaluat- ing business performance. However, authors also use ROE in the context of customer satisfaction (Heath & Seldin, 2012; Ilyas et al., 2018). There are several variables to choose from when measuring nancial performance, for example, a combination of absolute indicators and ratios (for details see, e.g., Chia et al., ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 107 2009) or several ratio indicators (Eklof et al., 2020; Jantarakolica et al., 2017; Sun & Kim, 2013). 2 Materials and methods This research employs a specialized model of cus- tomer satisfaction in order to investigate the rela- tionship between customer expectations and select nancial indicators: ROA, ROE, and Asset Turnover (ATO). The modelling was done in accordance with Eklof et al. (2020), i.e., an initial model of partial variables which affect and are related to customer expectations and satisfaction was created. This was followed by an investigation of the relationship be- tween the respective variables and select nancial indicators. As such, three independent models were constructed for this research for ROA, ROE, and ATO, respectively. Furthermore, models were created for two time periods, the current time period (the year 2016, when data about customer expectations and sat- isfaction were collected) and for the following time period (year 2017). This was based on Mittal et al. (2005), whose research suggests that customer satis- faction (measured by ASCI, which also includes the construct of customer expectations) has a long-lasting effect on business nancial performance. From this follows the possibility of a difference in the strength of the dependence between customer expectations and nancial performance within the customer satisfac- tion model across time periods. Customer satisfaction was measured using a sur- vey. This survey consisted of six sections based on the respective factors of customer satisfaction. These were: general customer satisfaction (CS), product competitiveness (C), product perceived value (PV), product perceived quality (PQ), customer expecta- tions (CE), and customer loyalty (CL). Product knowledge (PK) was added to the factors above. This variable was measured using only a sin- gle question (as such, it is not a construct). Questions in the survey and factor creation, including identi- cation and validation of the relationships between the factors, are based on research by Suchánek and Králová (2019). The survey was answered by a random and rep- resentative (according to the age (18+), gender, and region) sample of 1530 adult citizens of the Czech Republic. All the survey questions were designed as scale variables ranging from 1 to 10, with higher values in- dicating a more positive assessment of the business in terms of satisfaction. Respondents assessed 102 busi- nesses so that each business was represented by one product and assessed by fteen respondents. Then an- swers from respondents evaluating the same product were averaged so that each business was associ- ated with the averaged satisfaction variables. In this way the respondents’ data and business data were combined. The criterion for business selection was the avail- ability of balance sheets and prot and loss statements to allow the calculation of nancial performance in- dicators. Due to the fact that the research focused on customer satisfaction with the company’s product, it was necessary to exclude companies dealing only with the resale of products manufactured by their parent company and companies operating as a sales representative in the Czech Republic. Furthermore, businesses that produce products for industrial pro- cessing and not for consumption by consumers were excluded from the survey. The resulting sample thus comprised a total of 102 companies out of the starting 4255 companies. Customer satisfaction was measured as the over- all purchase experience (general satisfaction) in line with Fornell et al. (1996). Customer expectations were focused on examining the expected quality, consider- ing the specics of the product (food) in accordance with Brunsø et al. (2002). Expectations were measured based on the customer’s knowledge and experience with the product in accordance with Fornell et al. (1996). Perceived product quality was examined with respect to the specic focus of the research—food. Thus, perceived quality was surveyed from a sen- sory perspective in accordance with Cardello (1995). Perceived value was focused on two dimensions: functional and economic value perception and per- formance/quality perception (Suchánek & Králová, 2019; Sweeney & Soutar, 2001). Competitiveness was focused on product image, specically brand, gen- eral quality, and level of marketing communication. Competitiveness related to the product and was measured against competitors in accordance with Suchánek and Králová (2019). Customer loyalty was measured within the behavioral dimension according to Suchánek and Králová (2019). Specically, behav- ioral loyalty was measured by repurchase intentions, switching intentions, and exclusive intentions accord- ing to Jones and Taylor (2007). Business nancial performance was assessed using accounting documents (balance sheets and prot and loss statements). These nancial data, which are pub- licly available and whose publication is required by law, were obtained from the Bisnode database. Finan- cial data were collected for the current time period (2016, same year as customer satisfaction data were collected) and for the following time period (2017). Methods of Structural Equation Modelling (SEM) were used to model the relationships between sat- isfaction factors (CS, C, PV , PQ, CE, CL), product 108 ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 knowledge, and nancial indicators (ROA, ROE, ATO). Since satisfaction factors are latent variables, the measurement part of the model, which models the relationships between latent variables and manifest variables, was developed rst (Suchánek & Králova, 2016). It is briey described in the Results section. Manifest variables were represented in the research by scale questions in the survey. The path model (Suchánek & Králová, 2019) rep- resenting the relationships between satisfaction fac- tors formed the structural part of the SEM models. Furthermore, nancial performance indicators were implemented into the derived model. The consistency of the extended model with data was evaluated. Due to the complex nature of structural equation models, there is no overall test that would unambigu- ously conrm or refute the accuracy of the model (for example, on the basis of a single p-value). In- stead, various indices are recommended, the value of which shows whether the hypothetical model of the relationships is in accordance with the data observed. Schumacker and Lomax (2016) listed the most pop- ular indices with their acceptable levels, such as the Tucker-Lewis Index (TLI), Comparative Fit In- dex (CFI), Standardized Root-Mean-Square Residual (SRMR), and many others. For TLI and CFI, which take values from an interval of (0, 1) where a higher value means a better model, they suggest values above 0.9. Such values reect a good model t. For SRMR, values lower than 0.05 indicate a good model t. According to Hu and Bentler (1999), the acceptable range for the SRMR index is between 0 and 0.08. SEM models were estimated using the R language (R Core Team, 2021) and the lavaan package (Rosseel, 2012). The SEM method is used to test relationships be- tween factors researched in the context of customer satisfaction (see, e.g., Fornell et al., 1996). On the other hand, it is necessary to recognize that, due to its limi- tations, this method does not allow to explicitly verify causal relationships between factors that result from the model, even when the above tests are satised (Fang et al., 2021). 2.1 The model “Customer expectations change rapidly and vary widely depending on the beliefs or standards the in- dividual customer holds, including past experience as a source of revisions to these expectations” (Lin & Lekhawipat, 2016, p. 443). Memories of past experi- ences with a product can shape current expectations (Kangis & Passa, 1997). Expectations are also inu- enced by previous products received (Rust & Oliver, 1993). Thus, the quality of the product perceived by the customer at the rst purchase will inuence the customer’s expectations at the next purchase. H1. Perceived (product) quality inuences customer ex- pectations. Because the research was focused on customers who purchased the products repeatedly (controlled via survey, necessary condition for including the cus- tomer in the sample), it can be expected that the customer expectations factor is affected by their ex- perience with previous purchases. The inuence of past experience on expectations has been shown in research by Zeithaml et al. (1993). The customer’s ex- perience is based on the knowledge acquired before the purchase, during the purchase, and the use of the product after the purchase, and this knowledge is further used in the next purchase and is transferred into the customer’s expectations (before the next purchase). H2. Product knowledge inuences customer expectations. “In modern organizations, knowledge is the fun- damental basis of competition” (Aghamirian et al., 2015, p. 63). The impact of knowledge on a rm’s com- petitiveness has been conrmed by several studies (Aghamirian et al., 2015; Akhavan & Heydari, 2007). Therefore, it can be assumed that product knowledge (as a sub-component of knowledge) will also have an impact on the competitive ability of a company. H3. Product knowledge inuences (product) competitive- ness. Customer expectations change as a result of fur- ther purchases, and expectations are formed prior to the rst purchase, as well as reected in customer satisfaction. Before the next purchase, however, the customer has already processed the experience of buying and using the product, their satisfaction has reached a certain level, and they then transfer this level (or degree) of satisfaction into their expectations before this (next) purchase. Findings by Yi and La (2004) and Lin and Lekhawipat (2016) indicate that customer expectations are inuenced by customer satisfaction. H4. Customer satisfaction inuences customer expecta- tions. Customer loyalty is a relatively stable variable (over time), especially if it is genuine (Bove & Johnson, 2009). At the same time, the relationship between cus- tomer satisfaction and customer loyalty is probably ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 109 the most stable in general, as has been repeatedly proven by research to the present time (cf., e.g., Eklof et al., 2020; Fornell et al., 1996). Thus, customer sat- isfaction will also inuence customer loyalty in this case. H5. Customer satisfaction inuences customer loyalty. Perceived product quality has an impact on a company’s competitive advantage (Awang & Jusoff, 2009). The perception of the product by the customer, however, is primarily reected in the competitiveness of the company’s product and, in turn, in the compet- itiveness of the company as a whole. H6. Perceived (product) quality inuences (product) com- petitiveness. Customer loyalty can be understood as a func- tion of customer experience (Mascarenhas et al., 2006). Knowledge transfers into the customer experi- ence (Roggeveen & Rosengren, 2022). Thus, customer loyalty may inuence knowledge, especially when product knowledge is associated with repeat pur- chases. This relationship is supported by the ndings of Suchánek and Králová (2019). H7. Customer loyalty inuences product knowledge. Business performance consists of a nancial part (represented by nancial indicators) and a non- nancial part (represented by factors related to cus- tomer satisfaction) (Neely et al., 1995). In the case of repeat (subsequent) purchases, the relationships be- tween customer expectations and a number of other variables, including the nancial part of the business performance, change. As a result, the nancial per- formance of the business (represented by the selected nancial ratios) is inuenced by customer expecta- tions. This is conrmed by the ndings of Lang and Bressolles (2013), which demonstrated a direct ef- fect of customer expectations on business nancial performance. H8. Customer expectations inuence the nancial perfor- mance of the enterprise. The model therefore represents the relationships between the factors examined above. The individual relationships have been created using the above hy- potheses, and by testing these hypotheses (using sta- tistical tools), the functionality and robustness of the model will be further veried. The model develops relationships based on the nding that customer ex- pectations change and that in the case of a subsequent purchase, customer expectations may be inuenced by factors that are in turn inuenced by customer expectations in the rst purchase, which is based on the ndings of Suchánek and Králová (2019). The non-nancial relationships between factors related to customer expectations are further supplemented by business nancial performance measured (indi- vidually) by ROA, ROE, and ATO in the year of the customer expectation survey and in the follow- ing year to verify not only the short-term impact of the customer expectation model on nancial perfor- mance, but also the long-term impact. Thus, in the Results section, we constructed six models for years 2016 and 2017 and the three indi- cators used, i.e., ROA 2016, ROA 2017, ROE 2016, ROE 2017, ATO 2016, and ATO 2017. In the model, the following factors (constructs) are used: customer expectation (CE), perceived product quality (PQ), per- ceived product value (PV), product competitiveness (C), customer satisfaction (CS), and customer loy- alty (CL). Furthermore, the model uses the product knowledge (PK) factor, which is not a construct. 3 Results First, in the measurement part of the model, latent variables were constructed via 24 manifest variables from a questionnaire (see Suchánek & Králová, 2019) (CE1–CE4, PQ1–PQ5, PV1–PV5, CS1–CS3, CL2–CL5, C1, C2, C4), as listed in Table 1. Reliability was as- sessed via Cronbach’s alpha coefcients (0.957 [CE], 0.949 [PQ], 0.947 [PV], 0.963 [CS], 0.853 [CL], 0.927 [C]). Validity was accepted on the basis of average variance extracted greater than 0, AVE: 0.851 (CE), 0.800 (PQ), 0.788 (PV), 0.896 (CS), 0.569 (CL), 0.763 (C). The t indices were CFI D 0.906, TLID 0.889, SRMRD 0.056. Thus the measurement part can be accepted as empirically veried. In Tables 1–3 the rst column (Estimate) contains the estimated parameter value for each model pa- rameter; the second column (Std.Err) contains the standard error for each estimated parameter; the third column (z-value) contains the Wald statistic (which is obtained by dividing the parameter value by its standard error), and the last column (P(> |z|)) con- tains the p-value for testing the null hypothesis that the parameter value equals zero in the population. Two extra columns of standardized parameter values follow: in the rst column (labeled Std.lv), only the la- tent variables are standardized. In the second column (labeled Std.all), both latent and observed variables are standardized. The research then proved an indirect effect of cus- tomer satisfaction on nancial performance measured using ROA 2016, via customer expectation; see Table 2 110 ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 Table 1. Latent variables in measurement model. Factors Estimate Std.Err z-value P(>|z|) Std.lv Std.all Customer expectations Customer Expectation1 0.904 0.076 11.913 0.000 0.904 0.909 Customer Expectation2 0.913 0.075 12.114 0.000 0.913 0.918 Customer Expectation3 0.940 0.074 12.759 0.000 0.940 0.945 Customer Expectation4 0.914 0.075 12.123 0.000 0.914 0.918 Perceived Quality Perceived Quality1 0.927 0.074 12.456 0.000 0.927 0.932 Perceived Quality2 0.900 0.076 11.831 0.000 0.900 0.905 Perceived Quality3 0.857 0.081 10.525 0.000 0.857 0.839 Perceived Quality4 0.806 0.081 9.912 0.000 0.806 0.810 Perceived Quality5 0.952 0.073 13.074 0.000 0.952 0.957 Perceived Value Perceived Value1 0.903 0.076 11.840 0.000 0.903 0.907 Perceived Value2 0.924 0.075 12.335 0.000 0.924 0.929 Perceived Value3 0.886 0.077 11.475 0.000 0.886 0.891 Perceived Value4 0.826 0.081 10.230 0.000 0.826 0.830 Perceived Value5 0.876 0.078 11.239 0.000 0.876 0.880 Customer Satisfaction Customer Satisfaction1 0.969 0.072 13.525 0.000 0.969 0.973 Customer Satisfaction2 0.902 0.076 11.893 0.000 0.902 0.907 Customer Satisfaction3 0.954 0.073 13.150 0.000 0.954 0.959 Customer Loyalty Customer Loyalty2 0.601 0.091 6.592 0.000 0.601 0.604 Customer Loyalty3 0.888 0.077 11.471 0.000 0.888 0.892 Customer Loyalty4 0.481 0.095 5.083 0.000 0.481 0.483 Customer Loyalty5 0.935 0.075 12.531 0.000 0.935 0.940 Competitiveness Competitiveness1 0.868 0.076 11.466 0.000 0.868 0.889 Competitiveness2 0.963 0.073 13.233 0.000 0.963 0.967 Competitiveness4 0.765 0.084 9.127 0.000 0.765 0.769 and Fig. 1. Moreover, the research proved an indirect effect of customer satisfaction also via customer loy- alty and product knowledge, which affect customer expectations, and via associations with perceived product quality (associated with perceived product value, which also associates with customer satisfac- tion), which also affects customer expectations. A direct effect of customer satisfaction on nancial per- formance was not proven; on the other hand, the Table 2. Results of the structural part including nancial performance ROA in 2016. Estimate Std.Err z-value P(>|z|) Std.lv Std.all CL CS 2.859 0.483 5.923 0.000 0.944 0.944 C PK 1.086 0.175 6.223 0.000 0.437 0.435 PQ 1.474 0.220 6.708 0.000 0.593 0.593 CE PK 1.018 0.229 4.454 0.000 0.219 0.218 PQ 1.777 0.827 2.150 0.032 0.382 0.382 CS 2.192 0.835 2.624 0.009 0.471 0.471 PK CL 0.180 0.038 4.774 0.000 0.547 0.549 ROA 2016 CE 0.066 0.023 2.866 0.004 0.308 0.309 effect of customer satisfaction was shown to possi- bly be multi-layered, i.e., that satisfaction may affect expectations either directly or indirectly through a number of other factors. Each relationship in the model in Table 2 is statis- tically signicant at the level of signicance 0.05 ( pD 0.004), except the association between product com- petitiveness (C) and nancial performance, portrayed Fig. 1. Model of customer satisfaction and business nancial performance (measured by ROA) in the years 2016 and 2017. ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 111 by the dotted line, which is positive (just like all other relationships), but statistically signicant only at the level of signicance 0.1. The CFI of the model is 0.900; TLID 0.886; SRMRD 0.063. Thus the model based on hypotheses H1–H8 can be accepted as empirically veried. During further testing of the effect of customer sat- isfaction on all three nancial indicators, the effect of satisfaction on ATO 2016 was questionable (in terms of statistical signicance, p D 0.032) due to the re- sulting tests, which are liminal (CFID 0.899; TLID 0.885; SRMR D 0.063). ATO 2016 appears to be in- directly dependent on customer satisfaction through customer expectations, as is the case for ROA 2016. However, the effect of customer expectations on ATO 2016 was considerably lower (beta coefcients are 0.066 on ROA 2016 and only 0.048 on ATO 2016). The effect of customer expectations on ROE 2016 was questionable too but even a bit smaller than ATO 2016 (in terms of statistical signicance, pD 0.501) due to the resulting tests, which are liminal (CFI D 0.895; TLID 0.881; SRMRD 0.063). The year 2017 shows similar results. The best results were again achieved by the model which includes ROA 2017 (p D 0.000; CFI D 0.906; TLI D 0.893; SRMRD 0.061), where the nancial indicator is di- rectly affected by customer expectations. The strength of the effect is somewhat higher than in 2016; see Fig. 1 and Table 3. Thus the model based on hypotheses H1–H8 can be accepted as empirically veried. During further testing of the effect of customer satisfaction via customer expectations on all three - nancial indicators, the effect of expectations turned out questionable for ROE 2017 (in terms of statisti- cal signicance, pD 0.008) due to the resulting tests, which are liminal (CFID 0.899; TLID 0.885; SRMRD 0.065), but it was higher than in 2016. The effect of modelled satisfaction on the ATO 2017 nancial indi- Table 3. Results of the structural part including nancial performance ROA in 2017. Estimate Std.Err z-value P(>|z|) Std.lv Std.all CL CS 2.851 0.481 5.928 0.000 0.944 0.944 C PK 1.141 0.184 6.214 0.000 0.436 0.435 PQ 1.578 0.235 6.704 0.000 0.603 0.603 CE PK 1.014 0.223 4.548 0.000 0.225 0.224 PQ 1.733 0.783 2.213 0.027 0.384 0.384 CS 2.095 0.788 2.658 0.008 0.464 0.464 PK CL 0.182 0.038 4.791 0.000 0.550 0.552 ROA 2017 CE 0.099 0.025 3.991 0.000 0.448 0.450 cator via customer expectations was questionable too due to the resulting tests, which are liminal (pD 0.052; CFID 0.903; TLID 0.889; SRMRD 0.063) but even a bit smaller than ROE 2017. The effect of customer expectations on ATO was, however, considerably lower (0.099 in case of ROA 2016 and only 0.045 in the case of ATO 2016). Com- paring between the years 2016 and 2017, the effect of customer expectations on ATO was somewhat lower in 2017, making it insignicant in 2017. For the year 2017, a statistically signicant effect of customer expectations on ROE was considerably lower than in the case of ROA (estimated coefcients were 0.099 for ROA and only 0.064 for ROE). The inuence on ROE, however, appears stronger in 2017 than the effect on ATO. Thus, in 2017, customer sat- isfaction, via customers expectation, proved to affect only ROA signicantly as well. 4 Discussion The results correspond to the results of Eklof et al. (2020) and Anderson et al. (1994) in the sense that a relationship was proven where nancial perfor- mance measured using ROA was dependent upon customer satisfaction. In contrast to the research cited above, this research shows an indirect effect of customer satisfaction via customer expectations. Customer satisfaction affects customer expectations directly, and the two are associated indirectly, not only via customer loyalty and product knowledge on the one hand, but also through perceived product quality on the other (together with association with the perceived value of the product). A direct rela- tionship between customer expectations and nancial performance corresponds to research by Lang and Bressolles (2013); however, this research expands it to the business as a whole. Furthermore, a greater indirect effect of customer satisfaction (although in this case not via loyalty, but rather via expectations) on nancial performance in the following investigated time period (2017) was conrmed. This nding corresponds to the results of Mittal et al. (2005), who proved an indirect effect of customer satisfaction on nancial performance in the long term. The present research suggests that cus- tomer expectations also affect nancial performance in the long term. In contrast with research by Mittal et al. (2005), as well as the effect of customer sat- isfaction, the relationship between expectations and performance is a direct one. Contrary to Eklof et al. (2020), the dependence was conrmed in two time periods only for ROA, in a single time period (current year, 2016) for ATO, and in a single time period (following year, 2017) for ROE. 112 ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 Another difference lies in the considerably greater effect of modelled total satisfaction via customer ex- pectations on the ROA indicator. Considering the statistical signicance of results and the size of cor- responding coefcients, it seems that the effect of customer satisfaction on nancial performance in terms of ROA is much greater than on performance in terms of ROE and ATO. The results also suggest that the ATO indicator has a lagged impact on business performance, which is consistent with the research of Patin et al. (2020), and specically on ROE, which is consistent with the research of Boyd et al. (2007). The effect of customer satisfaction via expectations was found to be weak (in both investigated time peri- ods), which corresponds to the ndings of Anderson et al. (2004), van der Wiele et al. (2002), as well as Eren et al. (2013) and Kyengo et al. (2019). The latter authors, however, in contrast to this research, proved a weak effect of customer satisfaction on nancial performance via customer loyalty. As opposed to re- search by van der Wiele et al. (2002), this research proved a stronger effect of customer satisfaction on business performance in the later year compared to the current year. The results conrm that in research on long- term (cumulative) customer satisfaction, a key role is played by customer expectations, through which business managers may positively affect total nan- cial performance (ROA). As such, in pursuit of higher nancial performance, it is important to evoke cus- tomer expectations by way of customer satisfaction, and then reinforce these expectations via growing lev- els of product knowledge, which corresponds with the conclusions of Rufín et al. (2012) and Yi and La (2004). The extent of product knowledge may be increased by binding the customer to the business (i.e., encouraging repeated purchases), or by having the customer themselves convince friends and ac- quaintances to buy the product, who thereby increase their product knowledge and their expectations. A change in customer expectations as a result of a change in customer satisfaction, via customer loy- alty and product knowledge, can be expected to take some time. This may be the reason why the effect on business nancial performance is clearly observable as late as the following year, 2017, when the effect of customer expectations on the ROA indicator was greater. However, in view of the limitations of the SEM method mentioned in the Materials and methods sec- tion and also in view of the fundamental changes in the causal relationships between the researched factors of customer satisfaction and customer expec- tations, it will be necessary to conrm the established causality using more appropriate statistical tools. When considering management interventions, whether the goal is increasing customer expectations directly or indirectly through satisfaction, loyalty, product knowledge, or perceived quality, these measures must be given time to develop and produce results in terms of nancial performance (ROA). That being said, the cost of any interventions focused on increasing customer satisfaction and expectations must be properly compensated by way of an adequate increase in revenue. That by itself, however, is not enough. The increase in revenue must be greater than the increase in costs, otherwise, neither prot nor ROA will increase. It must be noted that an increase in sales will lead not only to increase in revenue, but also to increase in direct costs (costs associated with production and distribution). It has been proven that costs associated with increasing customer satisfaction reduce business nancial performance (Anderson et al., 1997; Ittner & Larcker, 1998). The costs expended on increasing customer satisfaction and expectations will represent additional costs (either direct, e.g., discounts, or indi- rect, e.g., advertising); sales volumes will thus need to be proportionally greater, so that these additional costs are covered, or considerably greater, so that prof- its are increased and business nancial performance is not impeded. 5 Conclusion Several ndings stem from researching relation- ships between factors of customer satisfaction and nancial performance (represented by a select in- dicator) within the complex model. Not only do customer expectations have a direct signicant im- pact on customer satisfaction, but other factors also affect customer satisfaction through customer ex- pectations. In contrast to models of other authors, customer expectations do not affect customer satis- faction and thereby nancial performance; instead, customer satisfaction affects customer expectations, and through these expectations it also affects nancial performance. This is caused by the subject of research being the customer who purchases the product re- peatedly. As such, the customer is unable to separate and identify expectations they had before the (rst) purchase and those they had after the purchase (Rufín et al., 2012). It is important to note, from a long-term perspective of customer expectations, that these ex- pectations are not necessarily constant and change over time, which affects business nancial perfor- mance. Furthermore, the research demonstrates the im- portance of clearly dening the investigated factors, as well as questions which lead to the construction ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 113 of those factors. Questions in our survey focus on the evaluation of expectations regarding future pur- chases, whereas questions in the research by Fornell et al. (1996), which has been adapted by a number of authors, are specically aimed at evaluating expecta- tions regarding a purchase which took place in the past. As such, it seems that Fornell et al. (1996) and other researchers investigated different expectations from those investigated in this research. Considering the above, it is a matter of debate whether they took an appropriate approach in their measurements. If the evaluation of customer expectations is af- fected by previous purchases, it is logical that it is affected by product knowledge. Increased product knowledge, obtained through an increased number of purchases, has a positive effect on customer ex- pectations. Therefore, if the customer purchases the product more, their expectations increase, which pos- itively affects business nancial performance. This product knowledge is positively affected by customer loyalty, i.e., their willingness to purchase the product again and recommend it to others. As such, it seems that it is important to investigate product knowledge as a standalone variable, as it has an important place in the model of customer satisfaction. The importance of clearly dening variables and factors is made ap- parent here yet again, as product knowledge as we have measured it is generally measured within the factor of perceived quality (cf. Fornell et al., 1996). This general practice obscures the impact and impor- tance of this variable within the latent factor. Considering the denition of customer expecta- tions, it is no surprise that perceived product quality affects these expectations (and not the other way around). If previous purchases and product knowl- edge affect customer expectations, it is logical that product quality and its perception will affect them. Contrary to standard models, perceived quality, perceived value, and customer satisfaction affect each other, which means that there is no causal link be- tween these factors (in standard models, perceived quality affects perceived value, which affects cus- tomer satisfaction—see Fornell et al., 1996). The nature of this relationship may be caused by the con- struction of the perceived value factor, which is much more complex (5 variables are used here) compared to standard indices, where only one variable (Askari- azad & Babakhani, 2015) or two variables (Fornell et al., 1996) are used. Another and likely more im- portant reason for the nature of the relationship is the construction of the perceived value factor, which in our research focuses not only on the perception of the ratio of price and quality, but also on the ratio of price and functionality of the product and its attributes, as well as the ratio of costs and functionality of the product and its attributes. Fornell et al. (1996) and Eklof et al. (2020) construct the perceived value factor exclusively based on the ratio of price and quality. Results show that perceived product quality is also affected by product price (through perceived value) as product-related costs. Changes in the cus- tomer’s perception of product price or the perceived ratio of costs and quality translate into changes in perceived product quality. This corresponds well with the signicant sensitivity of Czech customers to changes in price, as found by Tomeš et al. (2016). The same reciprocal relationship exists between per- ceived product value and customer satisfaction. It is especially important to note the reciprocal effect of customer satisfaction on perceived product value, meaning that greater customer satisfaction improves perceived product value. From the perspective of customer expectations, perceived product value is of lesser importance. What is important is customer satisfaction and perceived product quality, through which perceived product value can be affected and which directly inuence customer expectations. In order to correctly assess customer satisfaction, it is necessary to correctly dene the investigated factors, including their mutual relationships, i.e., the relationships between factors depend on the de- nitions of those factors. Although the investigated factors seem to be the same as those investigated in other studies, they may in fact be signicantly different. This affects the mutual relationships be- tween factors of customer satisfaction and the extent to which they are known. By aggregating individual variables during the creation of latent factors, impor- tant information may be lost. This does not happen if those variables are investigated as standalones. As such, it is important to carefully consider which vari- ables are to be investigated as standalones and which may be aggregated into a single factor. It seems appropriate to understand customer ex- pectations as an evaluation of expectations after a purchase. This leads to the creation of different re- lationships between customer expectations, customer satisfaction, and other factors. It is necessary to con- sider that customer expectations are not constant and change over time. One may therefore expect that this will lead to changes in a number of other factors as well, which are in direct relationships with customer expectations, such as nancial business performance. Product knowledge appears as especially important to monitor, as it is relevant to customer satisfaction and business competitiveness. Perceived value may have a different position in the system of factors, in the sense that there is a mutual effect with other vari- ables (perceived quality and customer satisfaction) without a causal link between these variables. This 114 ECONOMIC AND BUSINESS REVIEW 2023;25:103–117 leads to a reduced importance of perceived value as a factor. It also seems that product competitiveness (as- sessed as perceived product image) is affected by perceived quality and product knowledge. The rela- tionship between customer satisfaction and loyalty seems stable without regard to used factors, variables, or their denitions. On the other hand, it must be noted that their position in the model of customer satisfaction is somewhat different, i.e., they are not among the key variables which are affected by the assessed factors and which then affect nancial per- formance (this is especially applicable to customer loyalty). Even though the association between product com- petitiveness and business nancial performance was shown to be on the weaker side, considering the hopeful results and especially the direct nature of the association, it would be appropriate to verify the statistical signicance of this relationship through fur- ther research, either in the food industry or in other industries. Further research would do well to also focus on other indicators of nancial performance (ROE, ATO) since the results of the model including ATO seem quite hopeful. The limitations of the research are especially the focus on food companies in the Czech Republic, the use of only individual nancial ratios for the exami- nation of nancial performance, and the static nature of the model. The number of respondents (both en- terprises and their customers) is also a limitation. It is shown that the model of customer satisfaction is not static, so it is proposed to dynamize the whole model, i.e., to combine the standard model, where customer expectations inuence customer satisfaction, and our model, where the opposite is the case, including the dynamization of nancial performance. Due to the fact that we have been able to demonstrate the in- uence of customer satisfaction or, here, customer expectations on various nancial performance indi- cators, we propose the use of a summary indicator and either multicriteria decision making (e.g., TOP- SIS method) or a summary model based on Altman’s z-score. All this using more businesses and their customers. 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I know the product a little (I have bought the product only a few times)::: I know the product very well (already many years) Competitiveness 1 How do you assess the image of the product with respect to its brand (tradition, reputation, prestige) in comparison with the competition? signicantly worse ::: signicantly better Competitiveness 2 How do you assess the image of the product with respect to its overall quality (i.e., nutritional value, taste, composition, appearance or packaging, etc.) in comparison with the competition? signicantly worse ::: signicantly better Competitiveness 4 How do you assess the image of the product with regard to the level of marketing communication (interest, how memorable, the intensity of advertising, sales promotion, etc.) which relates to the product in comparison with the competition? signicantly worse than the competition, I do not know any advertising, sales promotion, etc.::: signicantly better than the competition, advertising is funny, etc . Customer expectation 1 To what extent does the product meet your needs and requirements? does not satisfy them at all::: fully meets them Customer expectation 2 To what extent is the quality of the product stable over the period you have known it compared with your expectations of the characteristics of the product (i.e., no changes in taste, appearance, composition, nutritional value, etc.)? product is different every time::: product is always exactly the same Customer expectation 3 To what extent does the product meet your expectations (needs and requirements) in comparison with the promises (product information, advertising, etc.)? does not satisfy them at all::: fully meets them Customer Expectation 4 How do you evaluate the product in comparison with the expectation that you always have before its purchase and consumption? product is always signicantly worse ::: product is always signicantly better Perceived quality 1 How do you assess the quality of the product with regard to its taste? very low::: very high Perceived quality 2 How do you assess the quality of the product with respect to its composition (ingredients, including their origin, content ratio of components, etc.)? very low::: very high Perceived quality 3 How do you assess the quality of the product with respect to its appearance? very low::: very high Perceived quality 4 How do you assess the quality of the product with respect to its nutritional value (especially in terms of functionality—energy, health, sweetness, refreshment, etc.)? very low::: very high Perceived quality 5 How do you assess the overall quality (the overall assessment of its taste, composition, nutritional value, freshness, durability, appearance, smell, or packaging, etc.) of the product? very low::: very high Perceived value 1 Compared with the price of the product (the price you usually pay), do you assess its overall quality as: the price is signicantly higher than its quality ::: for its quality it could be signicantly more expensive Perceived value 2 Compared with the price of the product (the price you usually pay), do you assess the taste, composition, appearance, and smell of the product, i.e., the product’s features, as: the price is signicantly higher than its quality::: for its quality it could be signicantly more expensive Perceived value 3 Compared with the price of the product (the price you usually pay), do you assess the functionality of the product (i.e., the fullling of those functions that you expect from the product, e.g., how it satises the appetite, tastes, refreshes, etc.) as: the price is signicantly higher than its quality ::: for its quality it could be signicantly more expensive Perceived value 4 Evaluate the cost of getting the product (in acquiring or “hunting” for it, its storage, disposal, and price) in comparison with its durability, expiry date, use, freshness: the costs are signicantly higher ::: durability, expiry date, use, freshness, etc. is signicantly higher Perceived value 5 Evaluate the overall quality of the product, i.e., the features and functionality in comparison with the overall costs of the product (including product price, storage costs, disposal, time-related costs, e.g., to opening or closing of the packaging, the time cost related to “hunting” for the product that is not always available, etc.) the overall costs are signicantly higher ::: the overall quality is signicantly higher Customer satisfaction 1 How generally satised are you with the product? not at all::: completely Customer satisfaction 2 How much does your overall satisfaction with the product correspond with your expectations (the expected satisfaction)? the reality is worse than my expectations::: the reality is better than my expectations Customer satisfaction 3 What is your overall satisfaction with the evaluated product compared to the ideal product? extremely low::: extremely high Customer loyalty 2 How often do you buy a similar product from another manufacturer? often—I do not care which manufacturer I buy the product from::: never—I buy the product only from this particular manufacturer Customer loyalty 3 If there are several very similar products on offer, at a very similar price, do you always choose the evaluated product? certainly not—I do not mind, I decide according to the best offer::: denitely—I always choose the evaluated product—it is the best Customer loyalty 4 If the price of the product increased (by up to 50% of the current price), would the amount/number of the product you purchase be likely to: signicantly decrease ::: remain the same Customer loyalty 5 Do you or would you recommend the product to your friends, family or other customers? certainly not—it is better not to recommend the product::: denitely—I often recommend the product/it is worth letting more people know about it