Volume 28 Issue 1 Article 4 March 2026 Applying Means–End Chain Theory to Online Buying and Applying Means–End Chain Theory to Online Buying and Understanding Consumers' Motivation to Disclose Private Understanding Consumers' Motivation to Disclose Private Information Information Brikena Berisha University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia, berishabrikena@gmail.com Maja Koneč nik Ruzzier University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Follow this and additional works at: https://www.ebrjournal.net/home Part of the Marketing Commons Recommended Citation Recommended Citation Berisha, B., & Koneč nik Ruzzier, M. (2026). Applying Means–End Chain Theory to Online Buying and Understanding Consumers' Motivation to Disclose Private Information. Economic and Business Review, 28(1), 59-72. https://doi.org/10.15458/2335-4216.1368 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 Applying Means–End Chain Theory to Online Buying and Understanding Consumers’ Motivation to Disclose Private Information Brikena Berisha a, * , Maja Koneˇ cnik Ruzzier b a University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia b University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia Abstract The purpose of this study is to dene and understand the motives that inuence consumers’ decisions to disclose per- sonal data online. Using means–end chain theory as the main theoretical framework, 10 in-depth interviews conducted through soft laddering revealed how both utilitarian and hedonic motivations jointly shape disclosure behavior. The ndings show that these motivations interact, demonstrating that data disclosure is driven not only by functional con- siderations but also by emotional and experiential factors embedded in consumers’ value systems. The study concludes that motivation plays a central and multifaceted role in online data disclosure decisions. Keywords: Means–end chain, Online shopping, Private information, Motivation JEL classication: M3 1 Introduction P ersonalization, dened as the process where cus- tomer experience is becoming increasingly rele- vant for companies and consumers globally. Faced with new challenges such as load of information and global competitiveness that can limit the attraction of new consumers in the digital environment, retailers seek to offer personalized experiences, from personal- ized ads to the personalized features of products and services (e.g., De Keyzer et al., 2024; Tran et al., 2024; Zanker et al., 2019). Built on personal traits, targeting consumers through personalized ads requires the use of their personal information (Hofacker et al., 2016). Given that access to consumer private data is con- sidered benecial by both consumers and companies (Mazurek & Małagocka, 2019), consumers often trade off their personal information (Plangger & Montecchi, 2020). There have been growing concerns about the sensitivity of data and about unethical approaches from companies or third parties, which has led to legal regulation. The General Data Protection Regulation (GDPR) came into force in May 2018 in Europe (Reg- ulation 2016/679). For any data to be gathered and used by companies or third parties, consumers have to consent, signifying agreement to share the data (Regulation 2018/1725). Many stores collect consumer data after payment has been conducted, and these data are then of- ten stored in their databases. Until recently, building and maintaining a relationship between the company such as a retail store and the consumer relied on different means of communication, phone calls and post deliverables. These more traditional means of communication have been recently replaced or exist alongside more digitalized forms of communication such as emails and mobile messages (Hoffman et al., 2022). Within such a diverse range of communication means, personalized content built on private infor- mation of the consumer is an efcient and popular method used today (Schweidel et al., 2022). Given that growing concerns about privacy breaches and usage Received 8 September 2025; accepted 5 December 2025. Available online 5 March 2026 * Corresponding author. E-mail address: berishabrikena@gmail.com (B. Berisha). https://doi.org/10.15458/2335-4216.1368 2335-4216/© 2026 School of Economics and Business University of Ljubljana. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/). 60 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 of data by third parties affect data disclosure nowa- days, it is important to note that the phenomenon of data disclosure has presented a challenge even in the ofine context. Yet customers felt more secure since the “divulgence of personal information occurred from one person to at least another one” (Wheeless & Grotz, 1976). Importantly, Derlega et al. (1993) sug- gested that this data exchange was characterized by voluntariness and uniqueness of customers. Although many researchers tackle online shopping, data disclosure as a process has not been sufciently explored. As we will explain later, there are sev- eral challenges affecting the buying process and data disclosure. Miltgen suggests that self-disclosure is affected by privacy policies (Miltgen, 2009). Con- sidering the application of new legislation for EU, it is pertinent to investigate the extent to which consumer rights and responsibilities are important aspects which motivate the discerning of private information. Malhotra et al. (2004) argue that con- sumers’ awareness of their private data distribution is very important, whereas Mazurek and Małagocka (2019) conclude that data disclosure is typical of con- sumers with high awareness. According to a recent study conducted by Weydert et al. (2019), the impact of regulation has directly led to a lower data volume. Considering technological development and changing consumption patterns, this article explores the kinds of changes that occur in the sphere of data disclosure in the online environment by focusing on customers’ motivations for revealing their personal data. The main objective of the paper is to elaborate and gain a better understanding of data disclosure. Many authors have acknowledged the low inclusion of motivation in data disclosure, along with shortcomings of the privacy of shopping. We place the study within a growing scholarship that tackles the online context of consumption through motivation in online buying, such as utilitarian and hedonic values (e.g., Bridges & Florsheim, 2008; Childers et al., 2001; Nili et al., 2013; Overby & Lee, 2006; Rintamäki et al., 2006) and complement it with the means–end chain (MEC) theory. Such theoretical dialogue carries the potential to generate new knowledge of consumer privacy and disclosure behavior. By extending the MEC theory into online shopping behavior, this study contributes to the eld of con- sumption behavior by – deepening the understanding of consumers’ data disclosure in decision making, mapping and structuring their motivations; – building the framework for concepts of utilitarian and hedonic motives; and – applying the MEC theory and laddering tech- nique in the context of data. This article is structured as follows. We rst in- troduce the primary background for investigating motivation in online buying, continuing with the framework of the MEC theory. A review of literature on online buying, motives, and data disclosure and the ndings from different authors serve as the basis for the development of this research. This is followed by 10 in-depth interviews, which provide important insights about the inuence of data disclosure on online buying and were analyzed through content analysis. 2 Theoretical background 2.1 Foundations of motivation and data disclosure Motives that drive the behavior of consumers have long been part of researchers’ interest in marketing categories, with a focus on consumer psychology and related important variables (Csikszentmihalyi & LeFevre, 1989; Dholakia, 1999; Schiffman & Kanuk, 1997; Sheth, 1973; Solomon, 1996). A considerable body of research strongly argues that thoughts and senses (utilitarian and hedonic values) are an unde- niable part of consumers’ shopping activity (Bridges & Florsheim, 2008; Childers et al., 2001; Fülöp et al., 2023; Hirschman, 1984; Hirschman & Holbrook, 1982). Motivations are categorized into utilitarian, or “functional and non-extrinsic,” and hedonic ones, or “non-functional and intrinsic” (Botti & McGill, 2011). Utilitarian motivation is dened as the motives for functional buying. On the opposite side of consump- tion, there are the hedonic motives dened as “the pleasured experience” of buying. Evidence of motivation can be found in several studies. Most of the time, motivation has been elab- orated with emphasis on intrinsic and extrinsic pat- terns (e.g., Deci & Ryan, 1985; Vallerand et al., 1997). As such, Tauber (1972) argued that shopping moti- vations are a variety of psychological needs that are not exclusively related to the acquisition of a product, and they can be classied into personal and social motivations. A model developed by Howard and Sheth in 1969 highlights the importance of stimuli and environment during customers’ purchase decisions (Howard & Sheth, 1969). Recently, there has been heightened interest in the relevance of motives in online buying in academic research (e.g., Adaji et al., 2020). Still, it remains crucial to analyze the effect of motivation on online buying due to the expansion of technology and online buying. The impact relates especially to the ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 61 distinction between shopping activity in online and physical situations. Data disclosure, the voluntary sharing of personal information, is shaped by individual motivations, perceived benets and risks, and contextual factors (Abramova et al., 2017; Martin & Murphy, 2017). Cus- tomers typically disclose basic information to transact online, while additional data may be shared for social or interactional benets, such as enhancing social cap- ital or projecting a desired image (Degutis et al., 2023; Zanker et al., 2019). As a practice, data disclosure can be very rational (e.g., Dinev et al., 2015), but still, numerous intan- gible and emotional benets and costs (Havlena & Holbrook, 1986) inuence the consumption activity. In this context, while purchasing online, studies often emphasize nancial benets. For example, Andrade et al. (2002) suggest that nancial incentives do not always lead to data disclosure and are mainly per- ceived as “an indicator of prot.” Yet, data disclosure and consumer awareness are much wider and are in- terrelated with the educational level of individuals, given that education is considered a signicant factor in enhancing cognitive and perceptual skills (Cole & Balasubramanian, 1993; Cole & Gaeth, 1990; Moor- man, 1990). Social exchange theory explains this behavior, sug- gesting that disclosure depends on the expected value of reciprocal or negotiated exchanges, moderated by trust, privacy concerns, and affective engagement (Blau, 1964; Cropanzano & Mitchell, 2005; Zimaitis et al., 2022). However, the data disclosure process must be viewed through the lens of a wider technol- ogy adoption. Tamilmani et al. (2019) conclude that hedonic motivation is often not elaborated in technol- ogy adoption study and recommends more research in it. Still, data disclosure is not a new practice, since disclosing data and giving up personal details has been a prevailing shopping dynamic in the ofine context as well. 2.2 MEC theory in the data disclosure context Inuenced by several theories in psychology, the MEC model analyses marketing problems as con- sumer decisions (Kilwinger & van Dam, 2021). The MEC theory has helped in explaining and under- standing shopping as “a desired end state” (Gutman & Alden, 1985). This has been framed primarily by classifying motives into a hierarchy (e.g., Moora- dian & Olver, 1996) which has helped to make the theory applicable to the motivation context. Motiva- tions cover both the underlying reasons of desired attributes and their consequences (Indrawati et al., 2022; Reynolds & Gutman, 1988) and are triggered by cognitive elements such as values (Vinson et al., 1977). Irvin Rock and Daniel Gutman emphasize the importance of expectancy–value and have identied attributes, consequences, and values as the driver of the MEC theory (Rock & Gutman, 1981). Central to the MEC theory are the laddering prod- uct attributes (A), consequences (C), and individual values (V), whose role is important in determining the consumer as a goal-oriented individual (Borgardt, 2020; Grunert & Grunert, 1995; Jaeger et al., 2023; Reynolds & Olson, 2001). In particular, laddering as a method elicits constructs systematically, until reach- ing the highest level. It is precisely the values which represent the higher level that were rst used to model the concepts and beliefs of people (Hinkle, 1965). There are several studies that link motivations with online buying. However, some seemingly irrelevant aspects of online buying are often neglected. Data disclosure is one such aspect which has not been prop- erly addressed yet (Turow et al., 2008). Even though data disclosure can be very rational (e.g., Dinev et al., 2015), numerous intangible and emotional benets and costs (Havlena & Holbrook, 1986) inuence the consumption activity. The MEC theory has been widely used as a ground- ing framework in studies to gain a deeper under- standing of consumer behavior (Anastasiadis & van Dam, 2014; Campos et al., 2024; Costa et al., 2007; Mer- feld et al., 2019; Phillips & Reynolds, 2009; Reynolds & Olson, 2001; Wagner, 2007; Walker & Olson, 1991). The productivity of this theory lies in its attention to the motivational aspect. Many authors have ac- knowledged the theory as “ladders of motives” or “motivational layers” (e.g., Bagozzi et al., 2003; Co- hen & Warlop, 2001), linking the attributes, values, and consequences as drivers of consumers’ motiva- tions (Claeys et al., 1995; Reynolds & Olson, 2001). The theory’s different degrees of abstraction facili- tate the exploration of human motivation (Bagozzi & Edwards, 1998). According to Gutman (1991), con- sumers choose products with attributes that lead to their desired consequences. In this sense, “attributes are features or aspects of products or services” (Valette-Florence & Rapacchi, 1991, p. 31). In the eld of online behavior, several remarkable studies use different dimensions of online buying and decision making grounded on the MEC theory. Xiao et al. (2014) laddered the attributes, consequences, and values of online buying by utilizing the usage of the theory on the online context. Other authors have recognized MEC analysis through the perspective of motivations in the domain of technology adoption (e.g., Lagerkvist et al., 2012; Naspetti et al., 2016; Ngigi et al., 2017; Okello et al., 2019; Urrea-Hernandez et al., 62 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 2016). Grounded on the MEC theory layers, there are studies that focus on advertising and user experience studies (Bech-Larsen et al., 2001; Eberhard, 2017; Van- den Abeele et al., 2013), product development and evaluation (Costa et al., 2004; Patrick & Xu, 2018; Phillips & Reynolds, 2009), business and organiza- tional research (Bourne & Jenkins, 2005; Inoue et al., 2017; Ronda et al., 2018), or even motivations behind local buying (Arsil et al., 2016; Campos et al., 2024). When studying the motivation for consumption in an online environment in this study, the MEC theory serves as a good methodological tool to understand how motivation in online context is shaped in relation to values and benets. Specically, MEC is a value- driven theory where attributes, benets, and values serve as subgoals. Values are motivational constructs that serve as a standard that guides the selection or evaluation of actions or things. Therefore, connecting benets with values is at the core of the MEC theory, which means that benets and goals are higher-level goals that motivate choice behavior (Chiu et al., 2014). Taking into account that different motivational layers are posited on different attributes, consequences, and values, we conclude that the MEC theory can cover the person’s choice behavior in relation to private data. 2.3 Applying MEC theory in investigating motives in online buying and data disclosure The choice for using the MEC theory in this research is threefold. First, it is one of the fundamental frame- works in building motivation in consumer behavior. This applies specically to the way this research aims to insight into why and how consumers value products (Grunert & Grunert, 1995; Gutman, 1982; Reynolds & Gutman, 1988). Jiang et al. (2014) have concluded that the MEC theory can serve as a key step to demonstrating the use of motivation in con- sumer behavior. As the focus of this study is on the motives behind an action or decision, the MEC theory provides a framework for this because it puts the cog- nitive structures of individuals at its core (Aurifeille & Valette-Florence, 1995). In addition, the theory an- alyzes and frames marketing problems as consumer decisions (Borgardt, 2020). Second, this research is grounded on consumer behavior in online modality, meaning that the the- oretical process and performance of consumption is studied in virtual reality. Several authors describe that both pragmatic and functionalist marketing drive the MEC theory (Alderson, 1957; Dixon & Wilkinson, 1984). This approach makes this theoretical back- ground a great t for this model. There have been several attempts to utilize and apply the MEC theory as a one-sided approach (Costa et al., 2004; Kuisma et al., 2007; Reynolds & Olson, 2001); still, this theory remains inclusive of both affective and emotional el- ements (Huber et al., 2004; Zanoli & Naspetti, 2002). According to Mort and Rose (2004) the features that consumers emphasize and ignore are used as moti- vation when evaluating a product or service. Further- more, this dichotomy is crucial in the theory, implying constructs as contrasts (Kilwinger & van Dam, 2021) and allowing the analysis of a range of possible eval- uations for a single construct (Fransella et al., 2004). MEC takes “individuality of consumers seriously and does not quantify it” (Grunert & Grunert, 1995). Third and equally important, the laddering chain with product attributes, consequences, and individ- ual values covers the crucial aspects of utilitarian and hedonic motivations. As this study uses inter- views to delve deeper into the motivations behind data discussion, the MEC theory supports the usage of research techniques “based on sorting procedures, elicitation, ranking or scaling tasks” (Borgardt, 2020, p. 3). In addition, the MEC theory makes it possible that “participants verbalize and choose own descrip- tion constructs of personal values and goals” (Walker & Olson, 1991). In this study, this applies specically to the sorting and ranking measurement items of util- itarian and hedonic ranges of motivation, important especially for the outputs and ndings of qualitative interviews about the content of the questionnaire. 3 Methodological approach and sample 3.1 Research design The purpose of this study is to identify and ex- plain the deeper motivations that drive consumers to disclose their personal data during online buy- ing, with a particular focus on how utilitarian and hedonic motives translate into disclosure-related de- cisions. In line with this objective, the methodology was designed to uncover the underlying motivational structures shaping consumers’ willingness to share personal information in digital marketplaces. Guided by the aim of mapping utilitarian and hedonic mo- tives and understanding their inuence on disclosure behavior, the study employs a qualitative research de- sign grounded in the MEC framework. To generate contextually rich and meaningful insights, in-depth interviews were conducted with purposively selected experienced online buyers whose reections could illuminate the motivational and cognitive links rel- evant to this investigation. This sampling strategy aligns with the exibility of the MEC method, which emphasizes that methodological choices should be ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 63 driven by research objectives rather than rigid stan- dardization (Kilwinger & van Dam, 2021). The analysis of the qualitative data followed a systematic four-step process guided by the MEC the- ory to uncover the cognitive structures underlying consumer data disclosure. First, value ladders were constructed to trace connections between product attributes, consequences, and core values. Second, content analysis coded and categorized these linkages across participants to identify consistent patterns. Third, an implication matrix quantied the frequency and strength of attribute–consequence–value connec- tions, highlighting central and peripheral pathways. Finally, a hierarchical value map visually represented the dominant cognitive paths, providing an integra- tive view of the shared motivations driving disclosure behavior. 3.2 Sample The sample for this study consisted of 10 par- ticipants with prior experience in online buying. Specically, the group included seven females and three males, aged between 26 and 45, representing diverse educational backgrounds, occupations, and online shopping habits. Each interview lasted approx- imately one hour, and no compensation was provided for participation. Table 1 summarizes the main demo- graphic characteristics of participants in the study. Table 1. Characteristics of the sample. Frequency of Employment Participant Gender Age online shopping status A Female 35–40 Regular Employed B Male 25–30 Occasional Employed C Female 25–30 Regular Employed D Female 30–35 Regular Employed E Female 30–35 Regular Employed F Female 30–35 Occasional Employed G Male 40–45 Regular Employed H Male 30–35 Regular Employed I Female 35–40 Regular Employed J Female 25–30 Regular Employed 3.3 Research instrument and procedure The interviews followed a three-phase structure. The rst phase gathered general information about participants’ online purchasing behavior, including frequency, consumption patterns, payment methods, delivery preferences, and their ongoing relationships with brands. The second phase focused on the core topic of the study, exploring the motivations underly- ing personal data disclosure, with particular attention to utilitarian and hedonic drivers. The nal phase examined participants’ awareness, knowledge, and perceptions related to data disclosure processes. De- mographic questions were asked at the end, and all interviews were conducted anonymously. After introducing the research purpose, the partici- pants were asked about their opinions on the process of online buying and data disclosure. This was meant as the entry concept (Olson, 1978), which served as an identication of tasks and categorization of stimuli from the participant’s perspective (Reynolds & Gut- man, 1988). As the entry concept encouraged them to rethink the shopping experience in the online en- vironment, in some cases it was supplemented “by a usage of a situation” (Olson & Muderrisoglu, 1979) as a stimulus to express more information. To contextualize, this was done through prompts such as “The last time you bought online: : :” or “Recall the last time you were searching on the website: : :” In many cases, this served as a push to continue the conversation and be more concrete. Par- ticipants typically described online shopping as their primary mode of purchase, highlighting its conve- nience, time-saving benets, and overall efciency, while noting that certain products still required phys- ical inspection. For instance, one participant stated: “Always, I buy mostly online. Physical shopping is al- most rare. Very convenient.” Another one highlighted that online shopping “makes life easier, saves time, and you don’t lose time shopping.” Afterwards, they were asked the following ques- tions: “What are the factors that contribute to data dis- closure?” and “What would make you decide not to disclose your personal information?” Subsequently, the laddering technique followed, by repeating at- tributes mentioned by the interviewee and asking for almost each attribute the questions such as “Why is this important to you?” or “Why do you think this is important?” (Phillips & Reynolds, 2009). This was done especially to have a higher degree of abstraction, and it was crucial in dening and reaching terminal values. Even when some answers were incomplete, the interview continued because the limits and will- ingness of participants were crucial (Wansink, 2000). Participants provided a range of responses re- garding their motivations for data disclosure. Some highlighted practical, outcome-oriented reasons, such as “When they ask for my email, it is usually for offers, sales, discount as a motivation.” and “If you don’t disclose the data, you lose some options, like track the process. They still send you an email, but you lose something.” Others emphasized more personal or emotional considerations, for example, “Personal issues are one of the reasons, people disclose private data immediately to feel validated.” Participants’ decisions not to disclose personal information were primarily guided by privacy and 64 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 security concerns. For example, one participant noted, “I think it is dangerous, and they can be used in other forms or by other parties involved. Very often I am afraid that my bank details can be used for bad purposes,” while another added, “I don’t disclose my data when I see that the website looks sketchy, credit card information, trials for free.” Participants also described strategies for limiting disclosure, such as using guest checkout or selective account options: “Guest checkout. Register and give. Connect with Google account when I can,” and “ID number: : : Credit card: : : Never save. I never save the credit card details.” Platform-specic preferences further shaped behavior, as one participant stated, “Usually not with Facebook. I usually continue with an account.” The nal part of the interviews focused on partic- ipants’ perspectives regarding consumer awareness, supported by follow-up questions. To clarify distinc- tions in responses and further explore motivations, the preference–consumption differences method was applied repeatedly. Additionally, the “differences by occasion” approach was employed, acknowledging that context plays a crucial role in providing mean- ingful insights for the laddering process (Barker, 1968; Gutman, 1982; Runkel & McGrath, 1972). Participants highlighted their understanding of consumer rights and awareness, with statements such as “The Euro- pean Union has it, it makes you have control over your data. By that, you can write to companies and complain, etc.,” and “There is an effect, at least you have an address and reason to complain or express your experience.” 3.4 Data analysis Following the interviews and in accordance with the literature review’s ndings, we developed a pool of items. The content of this pool was intended to have an inclusive coverage of all aspects of the motiva- tion dimension toward private data disclosure. As the outputs include individual items, they were useful for clarity and analyzed several times. Then the tran- scription followed, where the interviews were sum- marized using content analysis (Kassarjian, 1977). After that, we continued with conducting the codes, which was done through collecting the responses and bringing them to the “same denominator.” Due to the open questions, the participants used their own verbalization; therefore, the coding process was re- viewed carefully, and the codes were developed along the way. Semantics was very important, and in many cases, there were different approaches or words used to refer to the same thing. An example is descrip- tions such as “lack of time” and “nish faster,” which needed adjusting and were put together. In this pro- cess, the central work was to categorize elements of answers into attributes, values, and consequences, which were then assigned to master codes. However, some variables that appeared similar were intentionally kept separate to preserve nuanced differences in meaning. For instance, “convenience” reects the general ease and relaxation of the shop- ping experience; “spend less time on my next pur- chase” captures the anticipation of efciency in future transactions; and “continue and proceed further” rep- resents the navigational process within the current interaction. Maintaining these distinctions ensured that subtle but meaningful differences in how partici- pants experienced and evaluated the online shopping process were preserved in the analysis. When it comes to the “chain of values,” there was variety on the chain between answers and respon- dents. The chain was not always the same as in theory (e.g., Van Rekom & Wierenga, 2007), and in several cases more than one attribute (A) or consequence (C) was used to describe and arrive at the value level (V). The hierarchy was rened iteratively, both during the interviews and throughout the analytical process. While patterns of similarity emerged among partici- pants, unique responses were also retained through content analysis, even when mentioned only once, to preserve the richness of individual perspectives. The differentiation between attributes, con- sequences, and values was informed by the inductive laddering process. While the MEC theory provided the deductive framework to categorize these constructs, the actual coding and chain development were inductive, based on participants’ own verbalizations. Attributes were identied as the starting points mentioned spontaneously by respondents. Consequences were selected based on participants’ explanations of the outcomes or benets of these attributes, while values were derived from the higher-order goals participants implicitly or explicitly linked to these consequences. The transformation of answers occurred by assign- ing a code to the content and analyzing them. When appropriate, subordinate constructs were coded into superordinate categories to streamline the hierarchi- cal value map, which ensured that the meaning and valence of each construct were preserved. This consis- tent level of abstraction across the data set allowed for effective aggregation of responses, balancing clarity and manageability without sacricing critical moti- vational information (Fransella et al., 2004; Urrea- Hernandez et al., 2016). The iterative coding process ensured that the hierarchical structure reected par- ticipants’ perspectives rather than the researchers’ assumptions. Once the content code table was nalized, the next step in the MEC analysis involved construct- ing the implication matrix. This matrix quanties ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 65 how often each attribute, consequence, and value is linked within participants’ ladders, capturing both direct and indirect relationships. For analytical rigor, only relationships with a frequency of two or more, whether direct or indirect, were considered mean- ingful and retained for further interpretation. In the matrix, whole numbers represent direct links, while decimal values indicate the strength of indirect links, allowing a distinction between the two. This step claries which cognitive connections are most salient across participants and prepares the foundation for constructing the hierarchical value map. The implication matrix is very important before constructing the hierarchical value map, because it shows the times that elements are related, and the chain is sorted (Arsil et al., 2019). The representa- tion of connection between elements is crucial, and we analyzed this by summarizing the table to then be reected in a hierarchical value map. As the nal step, the visualization of all relationships between at- tributes, consequences, and values are represented on a tree-like graph, a hierarchical value map (Veludo- de-Oliveira, 2006). We used the Ladderux software to analyze and visualize the data, rst in the implication matrix and then on the hierarchical value map. 4 Results 4.1 The ladders Ten interviews yielded the development of 44 lad- ders. Each interview generated approximately four ladders on average. There were interviews in which six ladders were generated, but there were also a few with fewer ladders, especially with a particu- lar participant who could not generate more than three. Figs. 1 and 2 present examples of how the chain was composed. The values are the result of a selective process through attributes and then conse- quences. In these cases, the participants started with attributes such as “convenience” and “spend less time Fig. 1. Ladder from interview. on my next purchase.” The chain then was devel- oped through consequences, and nally values were developed. 4.2 Content code table and implication matrix The content code table was established, and it was composed of 28 variables. This number is the nal edited version, since in the rst step this number of codes was nearly 40 in total. A large number of codes is not recommended because it complicates the anal- ysis and interferes with the result. Therefore, the list of content codes was controlled and ltered several times, until the optimal quantity of attributes was reached. More particularly, the nal summary content code table includes 8 attributes, 8 consequences, and 12 values. Table 2 contains all variables with the specic ladders on the chain. The table also contains the numbers that represent how many times each of these variables was mentioned (in parentheses). Based on this frequency, we argue that there were some more frequent variables, but there were also some variables unique and specic to participants. In that sense, there are some variables mentioned 11 times, including “spend less time on my next purchase” for attributes and “benet from the service” for values. However, there were also unique answers mentioned only once, and this applied especially to the value part. Values such as “get free trial, newsletter, and membership” as well as “see how the process is designed for inspiration” are values that were mentioned only once among participants. The laddering analysis identied eight key at- tributes underlying consumers’ willingness to dis- close personal data: convenience, time optimization on the process, spending less time on future pur- chases, avoiding newsletters or subscriptions, being associated with a brand or website, continuing and proceeding further, nishing the action, and situa- tions where there are no alternative options. While Fig. 2. Ladder from interview. 66 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 Table 2. Summary content code table. Attributes (A) Consequences (C) Values (V) 1. Convenience (7) 2. Time optimization on the process (8) 3. Spend less time on my next pur- chase (11) 4. Avoid getting newsletters or sub- scriptions (2) 5. Be associated with the brand or website (4) 6. Continue and proceed further (6) 7. Finish the action (4) 8. There are no other options (4) 9. Track the process (7) 10. Get points and discounts (5) 11. Feeling comfortable while buying (9) 12. Routine (6) 13. Cause of no or low awareness (4) 14. Laziness and lack of patience (6) 15. Avoid the inuence and stimulation of unnecessary buying (7) 16. Avoid the “attack” from aggressive marketing (2) 17. Have wider offer (3) 18. Energy with brands (7) 19. Get a smoother experience (4) 20. Feel important and contribute (1) 21. Get free trial, newsletter, and membership (1) 22. Get gratication (2) 23. Feeling of belonging and validation (3) 24. Better communication with the company (9) 25. Get personalized offers (7) 26. Save money and get priority when there are sales (4) 27. Benet from the service (11) 28. See how the process is designed (get inspired) (1) these attributes primarily reect procedural and ex- periential aspects of online shopping rather than the intrinsic sensitivity of the data itself (e.g., email, credit card, home address), they serve as tangible starting points in the MEC hierarchy that lead to consequences and higher-order values. This pattern indicates that participants evaluate data disclosure more in terms of perceived functional and hedonic benets, emphasiz- ing ease, efciency, and relational engagement with brands, rather than focusing on the inherent charac- teristics of personal data. Recognizing this distinction helps contextualize the ndings and claries the focus of the analysis in relation to consumer awareness and motivations. The identied consequences illustrate the imme- diate outcomes that consumers associate with dis- closing personal data, including tracking the process, receiving points and discounts, feeling comfortable while buying, following routines, compensating for low awareness, and managing convenience versus ef- fort (e.g., laziness or avoiding aggressive marketing). These consequences bridge the tangible attributes to higher-order values, which reect consumers’ broader goals such as smoother experiences, energy and engagement with brands, gratication, belonging and validation, personalized offers, nancial benets, and enhanced communication with the company. To- gether, these chains underscore how functional and psychological outcomes of data disclosure translate into meaningful value for consumers, supporting the hierarchical structure posited by the MEC theory. After the content table analysis, the implication matrix was created, as shown in Table 3. A num- ber of relationships, including 12, 17, and 20, appear Table 3. Implication matrix. Note. Order of variables: attributes (1–8), consequences (9–16), values (17–27). CCD content codes. ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 67 Fig. 3. Hierarchical value map. as empty because they were either too weak, show- ing fewer than two occurrences on each side of the bracket, or exhibited no association at all. 4.3 Constructing hierarchical value map The hierarchical value map shows the remaining variables, which means that some of the variables did not make it to the map. The nal version expressing the relationships includes the nal seven attributes, six consequences, and six values, displayed in Fig. 3. We have ltered the visualization of the map in order to express the relationships more clearly and to not have many interruptions in the arrows, which in this case show relationships. The stronger relationships, starting from 4, are marked with thicker bold lines. All the pathways included in the hierarchical value map consist of more than two direct rela- tionships, which means that only relevant chains are expressed in the row attributes, consequences and values, expressed in this formula: L n D [A,C,V]. In particular, there are three main pathways of great relevance, each having various connections in the chain, which is expressed in bolder lines. The rst pathway, L 1 D [1,11,18], is related to the buy- ing process where the benet and easiness of the process relate to the participation in brands. As such, consumers value the engagement with brands through comfort in the buying process and general convenience. An important pathway is the chain-of-process out- come that relates to the nalization of the process. As the ndings suggest, participants want a quicker ending and to make the data disclosure part of their routine, expressed as L 2 D [6,12,26]. Customers would disclose data to end the process as quickly as pos- sible, and this is part of their routine. Importantly, what customers evaluate are also the monetary sav- ings, which were often expressed as important for both the current buying process and future benets. This chain highlights the relationship with brands, in the perspective of nancial benets. Moreover, all the ladders in this chain are of a utilitarian background, which highlights the importance of utilitarian mo- tives on data disclosure. The contribution to future buying is another part of the buying process. This has been mentioned as an attribute by many participants. Even though time optimization was an attribute in itself, “spend less time on my next purchase” was put as a separate one. Through the interviews with participants, there was a distinction made between time on the current buy- ing process, mainly focused on nishing, compared to the time on future processes. The importance of this attribute is the appreciation of long relationships with brands, and there are motives to disclose data even when there are no benets in the current buying process. Further, this attribute is highly related to the contribution for future purchase processes, such as the avoidance of different modes of marketing meth- ods on stimulating “unnecessary buying,” and relates to the comfort of the buying process. Both these con- sequences lead to the value of belonging, validation, and the engagement by the brands, which emphasizes the importance of gratication, role, and value for customers as hedonic motivations. As such, this chain contemplates the view of consumer motivation to- ward data disclosure and results in L 3 D [3,15+11,23]. 68 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 There were cases when participants mentioned “be associated with the brand or website” as an important attribute in their purchase process. The matrix and value map imply the hierarchy of this attribute in relation to awareness or even benets from the action in continual relationships with the brands, such as points and discounts. The variable “get points and discounts” was separated from mon- etary savings because its meaning is much broader, both in the sense of relationship and benet. Par- ticipants declared that once you have a continuous relationship with the brand, the brand rewards your activity with points, discounts, or other methods. Usually, the rewards are not directly related to the present buying action, which brings further relevance to the relationship. From the chain L 4 D [5,10,25], the value remains the gain of offers that are per- sonalized, which is a different engagement or in- volvement with the brand. Again, this chain puts the emphasis on the hedonic motivation of consumers, which relates to the social gratication, role, and value. An interesting pathway remains the one when there are no other options than disclosing private data. In order to continue, the participants conrmed that they give the basic data due to their laziness and lack of patience. Even though customers’ actions would not have taken this direction if there was the possi- bility to continue without personal data disclosure, the value of this chain suggests better communication from the company is expected. This means utilitarian motives are an important factor when buying on- line and revealing private data, which is expressed through the chain L 5 D [8,14,24]. 4.4 Discussion The analysis followed the MEC framework, in which classication depends on how each statement functions within the motivational hierarchy rather than on its literal meaning. Attributes represent tan- gible features that consumers interact with directly, such as convenience, time optimization, comfort dur- ing purchase, tracking ability, and reward programs. Consequences describe the immediate outcomes of these attributes, such as ease of transaction or time savings, while values reect higher-order personal goals including belonging, validation, personaliza- tion, trust, and gratication. Together, these reveal that participants’ concrete experiences of data dis- closure translate into emotionally and symbolically meaningful outcomes. Emphasis was placed on func- tional and psychological consequences, suggesting that motivations are expressed more through per- ceived outcomes than through direct consideration of the underlying data characteristics. Although MEC provides a deductive structure, the analysis in this study was conducted inductively to respect the participants’ own perspectives. Ladder- ing interviews identied the chain in A-C-V order, as elements naturally emerged from participants’ reec- tions, showing how concrete shopping experiences connect to psychological goals. For instance, while “being associated with a brand” could theoretically be classied as a value, several respondents referred to it as a concrete experience that led to emotional sat- isfaction and recognition. This exible interpretation ensured that the hierarchical value map reected re- spondents’ authentic reasoning processes rather than predetermined categories. The results indicate that both utilitarian and he- donic motivations shape consumers’ willingness to disclose personal data. Utilitarian motivations are re- ected in the search for efciency, convenience, and functionality, supporting previous ndings that em- phasize instrumental goals in online decision making (Xiao et al., 2014). In contrast, hedonic motivations are expressed through the emotional and symbolic con- nections that consumers form with brands, such as identity expression, belonging, and enjoyment, con- sistent with prior studies on emotional involvement in digital behavior (Chennamaneni & Taneja, 2015; Weydert et al., 2019). The relation of pathways to mo- tivation is expressed in Table 4. The interviews further revealed that motivations are inuenced by broader individual and contex- tual factors. Participants’ disclosure behaviors were shaped by their technological condence, prior on- line experiences, and perceived control over data use. This aligns with privacy theories that emphasize the Table 4. Relation of pathways to motivation. Pathway Dominant motivation Convenience! feeling comfortable while buying! engage with brands Utilitarian Continue and proceed further! routine! save money and get priority when there are sales Utilitarian Spend less time on my next purchase! avoid the inuence and stimulation of unnecessary buying + feeling comfortable while buying! feeling of belonging and validation Hedonic Be associated with the brand or website! get points and discounts! get personalized offers Hedonic/utilitarian There are no other options! laziness and lack of patience! better communication with the company Utilitarian ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 69 role of individual boundaries, trust, and autonomy in regulating information sharing (Petronio, 2012). For instance, participants with a prevention focus prior- itized security and efciency, whereas those with a promotion focus highlighted emotional rewards and personalization. These ndings demonstrate that mo- tivational hierarchies are embedded within personal and situational contexts rather than existing as iso- lated constructs. Incentives such as discounts, loyalty points, and personalized offers were also found to encourage data disclosure, particularly when accompanied by transparent communication about data usage. These results support earlier research suggesting that - nancial incentives and clear information can increase consumers’ willingness to share data (Andrade et al., 2002; Cole & Gaeth, 1990). Overall, the ndings sug- gest that consumers evaluate data disclosure through a multidimensional lens that combines functional ef- ciency, emotional engagement, and perceived privacy control. 5 Conclusions, implications, and limitations This study extends the application of the MEC the- ory to the domain of online data disclosure, offering new insights into the psychological mechanisms that guide consumers’ willingness to share personal in- formation. Through qualitative laddering interviews (Reynolds & Gutman, 1988), the study mapped the structure of motivations linking attributes, conse- quences, and values, demonstrating how specic online interactions transform into symbolic and emo- tional goals. The ndings indicate that consumers disclose per- sonal data online through a combination of utilitarian and hedonic motivations, each shaping disclosure be- havior in interconnected ways. As a result, disclosure reects the interplay of these functional and experien- tial drivers, where practical benets are reinforced by emotional satisfaction. Based on this, we can conclude that data disclosure is not a purely functional act but a value-driven process embedded in relational and experiential dynamics. Customers perceive the shar- ing of personal data as an element of trust building and relationship formation with brands. This nding aligns with theories emphasizing the interconnection between privacy, control, and emotional attachment in digital interactions (Chennamaneni & Taneja, 2015; Petronio, 2012). The identied hierarchy, where at- tributes such as convenience and discounts lead to consequences such as satisfaction and ease, culminat- ing in values such as belonging and trust, highlights the dual utilitarian and hedonic nature of disclosure motivations. From a theoretical perspective, this research broad- ens the scope of the MEC theory beyond tradi- tional product choice studies and contributes to a deeper understanding of motivational drivers to- ward data disclosure. Previous applications of MEC have explored areas such as patient volunteer- ing (Jalalian et al., 2010; Pieters et al., 1995; Tey et al., 2020), digital self-disclosure (Rothschild & Aharony, 2022), and technology adoption in agri- culture (Lagerkvist et al., 2012; Ngigi et al., 2017; Okello et al., 2019). The present study demonstrates that MEC can effectively explain privacy-related behaviors by capturing how consumers link data- sharing actions to emotional, cognitive, and social outcomes. From a managerial viewpoint, practitioners can get insights into the motives of consumers such as being part of the process and having a relationship with brand to reveal their private data. In-depth interviews provided a detailed overview about motivations to re- veal private data; therefore, marketing strategies can practically improve their communication and design the process of online shopping and data disclosure accordingly. These outcomes can serve as a focal point for the designing process of purchasing online as well as building relationships with consumers. Nevertheless, the study is subject to certain limita- tions. As a qualitative investigation based on a limited number of interviews, its ndings are interpretive and context-dependent. For future avenues, it would be important to elaborate motives deeper through focus groups and bigger samples. As qualitative methods in general can leave space for subjectivity, complement- ing the study with quantitative research would be highly appreciated as a contribution to the scientic topic. In this case, it would be possible to know the correlation between constructs and group the most signicant ones. Data availability statement The data that support the ndings of this study consist of interview transcripts from qualitative con- versations. Due to the sensitive nature of these data and to protect participant condentiality, they are available from the corresponding author upon reasonable request, subject to ethical and privacy restrictions. Funding statement This article does not include any relevant nancial or nonnancial competing interests to report. 70 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 References Abramova, O., Wagner, A., Krasnova, H., & Buxmann, P . (2017). Understanding self-disclosure on social networking sites: A lit- erature review. In AMCIS 2017 proceedings: Adoption and diffusion of information technology (SIGADIT) (Article 30). https://aisel .aisnet.org/amcis2017/AdoptionIT/Presentations/30/ Adaji, I., Oyibo, K., & Vassileva, J. (2020). E-commerce shopping motivation and the inuence of persuasive strategies. Frontiers in Articial Intelligence , 3, Article 67. https://doi.org/10.3389/ frai.2020.00067 Alderson, W. (1957). Marketing behavior and executive action: A func- tionalist approach to marketing theory. Richard D. Irwin. Anastasiadis, F., & van Dam, Y. K. (2014). Consumer driven supply chains: The case of Dutch organic tomato. Agricultural Engineer- ing International, 11–20. Andrade, E. B., Kaltcheva, V . D., & Weitz, B. A. (2002). Self- disclosure on the web: The impact of privacy policy, reward, and company reputation. ACR North American Advances, 29, 350–353. Arsil, P ., Ardiansyah, Y., & Yanto, T. (2019). Consumers’ intention and behaviour towards sh consumption: A conceptual frame- work. IOP Conference Series: Earth and Environmental Science, 255, Article 012006. https://doi.org/10.1088/1755-1315/255/ 1/012006 Arsil, P ., Li, E., & Bruwer, J. (2016). Using means-end chain analy- sis to reveal consumers’ motivation for buying local foods: An exploratory study. Gadjah Mada International Journal of Business, 18(3), 285–300. https://doi.org/10.22146/gamaijb.6061 Aurifeille, J. M., & Valette-Florence, P . (1995). Determination of the dominant means-end chains: A constrained clustering ap- proach. International Journal of Research in Marketing, 12(3), 267–278. https://doi.org/10.1016/0167-8116(95)00026-X Bagozzi, R. P ., Dholakia, U. M., & Basuroy, S. (2003). How effortful decisions get enacted: The motivating role of decision processes, desires, and anticipated emotions. Journal of Behavioral Decision Making, 16(4), 273–295. https://doi.org/10.1002/bdm.446 Bagozzi, R. P ., & Edwards, J. R. (1998). A general approach for representing constructs in organizational research. Organiza- tional Research Methods, 1(1), 45–87. https://doi.org/10.1177/ 109442819800100104 Barker, R. G. (1968). Ecological psychology: Concepts and methods for studying the environment of human behavior. Stanford University Press. Bech-Larsen, T., Grunert, K. G., & Poulsen, J. B. (2001). The accep- tance of functional foods in Denmark, Finland and the United States. Appetite, 36(2), 111–128. Blau, P . M. (1964). Exchange and power in social life. Wiley. Borgardt, E. (2020). Means-end chain theory: A critical review of literature. Research Papers of Wroclaw University of Economics, 64(3), 141–160. https://doi.org/10.15611/pn.2020.3.12 Botti, S., & McGill, A. L. (2011). The locus of choice: Personal causality and satisfaction with hedonic and utilitarian deci- sions. Journal of Consumer Research, 37(6), 1065–1078. https:// doi.org/10.1086/656570 Bourne, H., & Jenkins, M. (2005). Eliciting managers’ personal values: An adaptation of the laddering interview method. Or- ganizational Research Methods, 8(4), 410–428. https://doi.org/ 10.1177/1094428105280118 Bridges, E., & Florsheim, R. (2008). Hedonic and utilitarian shop- ping goals: The online experience. Journal of Business Research, 61(4), 309–314. https://doi.org/10.1016/j.jbusres.2007.06.017 Campos, R. D. C. L., Vilas Boas, L. H. D. B., Rezende, D. C. D., & Botelho, D. (2024). Food safety and consumption of fruits and vegetables at local markets: A means-end chain approach. Qual- itative Market Research: An International Journal, 27(2), 337–355. https://doi.org/10.1108/QMR-10-2023-0135 Chennamaneni, A., & Taneja, A. (2015). Communication privacy management and self-disclosure on social media: Acase of Face- book. In AMCIS 2015 Proceedings: Information systems security, assurance and privacy (SIGSEC) (Article 32). https://aisel.aisnet .org/amcis2015/ISSecurity/GeneralPresentations/32 Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online shopping behavior. Jour- nal of Retailing, 77(4), 511–535. https://doi.org/10.1016/S0022 -4359(01)00056-2 Chiu, M. C., Wang, G. T. E., Fang, H. Y., & Huang, Y. H. (2014). Understanding customers’ repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value, and perceived risk. Information Systems Journal, 24(1), 85–114. https://doi.org/10.1111/j.1365-2575.2012.00407.x Claeys, C., Swinnen, A., & Vanden Abeele, P . (1995). Consumers’ means-end chains for “think” and “feel” products. International Journal of Research in Marketing, 12(3), 193–208. https://doi.org/ 10.1016/0167-8116(95)00021-S Cohen, J. B., & Warlop, L. (2001). A motivational perspective on means–end chains. In T. J. Reynolds & J. C. Olson (Eds.), Un- derstanding consumer decision making: The means-end approach to marketing and advertising strategy (pp. 389–412). Lawrence Erl- baum Associates Publishers. Cole, C. A., & Balasubramanian, S. K. (1993). Age differences in consumers’ search for information: Public policy implications. Journal of Consumer Research, 20(1), 157–169. https://doi.org/ 10.1086/209341 Cole, C. A., & Gaeth, G. J. (1990). Cognitive and agerelated differences in the ability to use nutritional information in a com- plex environment. Journal of Marketing Research, 27(2), 175–184. https://doi.org/10.2307/3172844 Costa, A. I. A., Dekker, M., & Jongen, W. M. F. (2004). An overview of means-end theory: Potential application in consumer- oriented food product design. Trends in Food Science & Tech- nology, 15(7–8), 403–415. https://doi.org/10.1016/j.tifs.2004.02 .005 Costa, A. I. A., Schoolmeester, D., Dekker, M., & Jongen, W. M. F. (2007). To cook or not to cook: A means-end study of motives for choice of meal solutions. Food Quality and Preference, 18(1), 77–88. https://doi.org/10.1016/j.foodqual.2005.08.003 Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874– 900. https://doi.org/10.1177/0149206305279602 Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experi- ence in work and leisure. Journal of Personality and Social Psychology, 56(5), 815–822. https://doi.org/10.1037/0022-3514 .56.5.815 De Keyzer, F., Buzeta, C., & Lopes, A. I. (2024). The role of well- being in consumers’ responses to personalized advertising on social media. Psychology & Marketing, 41(6), 1206–1222. https:// doi.org/10.1002/mar.21977 Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self- determination in human behavior. Springer. Degutis, M., Urbonaviˇ cius, S., Hollebeek, L. D., & Anselmsson, J. (2023). Consumers’ willingness to disclose their personal data in e-commerce: A reciprocitybased social exchange perspective. Journal of Retailing and Consumer Services, 74, Article 103385. https://doi.org/10.1016/j.jretconser.2023.103385 Derlega, V ., Metts, S., Petronio, S., & Margulis, S. (1993). Self- disclosure. Sage Publications. Dholakia, R. R. (1999). Going shopping: Key determinants of shop- ping behaviors and motivations. International Journal of Retail & Distribution Management, 27, 154–165. https://doi.org/10.1108/ 09590559910268499 Dinev, T., McConnell, A. R., & Smith, H. J. (2015). Informing privacy research through information systems, psychology, and behav- ioral economics: Thinking outside the “APCO” box. Information Systems Research, 26(4), 639–655. https://doi.org/10.1287/isre .2015.0600 Dixon, D. F., & Wilkinson, I. F. (1984). An alternative paradigm for marketing theory. European Journal of Marketing, 18(3), 40–50. https://doi.org/10.1108/EUM0000000004780 Eberhard, D. (2017). Translating means-end research into adver- tising strategy using the MECCAS model. Economia AgroAli- mentare/Food Economy, 19(3), 333–356. https://doi.org/10.3280/ ECAG2017-003003 Fransella, F., Bell, R., & Bannister, D. (2004). A manual for repertory grid technique (2nd ed.). John Wiley & Sons. Fülöp, M. T., Topor, D. I., Ionescu, C. A., & Akram, U. (2023). Utilitarian and hedonic motivation in e-commerce online ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 71 purchasing intentions. Eastern European Economics, 61(5), 591– 613. https://doi.org/10.1080/00128775.2023.2197878 Grunert, K. G., & Grunert, S. C. (1995). Measuring subjective meaning structures by the laddering method: Theoretical con- siderations and methodological problems. International Journal of Research in Marketing, 12(3), 209–225. https://doi.org/10 .1016/0167-8116(95)00022-T Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60–72. https://doi.org/10.1177/002224298204600207 Gutman, J. (1991). Exploring the nature of linkages between conse- quences and values. Journal of Business Research, 22(2), 143–148. https://doi.org/10.1016/0148-2963(91)90048-3 Gutman, J., & Alden, S. D. (1985). Adolescents’ cognitive structures of retail stores and fashion consumption: A means-end chain analysis of quality. In J. Jacoby & J. Olson (Eds.), Perceived quality: How consumers view stores and merchandise. Lexington Books. Havlena, J. W., & Holbrook, B. M. (1986). The varieties of con- sumption experience: Comparing two typologies of emotion in consumer behavior. Journal of Consumer Research, 13(3), 394–404. https://doi.org/10.1086/209078 Hinkle, D. N. (1965). The change of personal constructs from the view- point of a theory of construct implications [Unpublished doctoral dissertation]. Ohio State University. Hirschman, E. C. (1984). Experience seeking: Asubjectivist perspec- tive of consumption. Journal of Business Research, 12(1), 115–136. https://doi.org/10.1016/0148-2963(84)90042-0 Hirschman, E. C., & Holbrook, B. M. (1982). Hedonic con- sumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92–101. https://doi.org/10.1177/ 002224298204600314 Hofacker, F. C., Malthouse, C. E., & Sultan, F. (2016). Big data and consumer behavior: Imminent opportunities. Journal of Con- sumer Marketing, 33(2), 89–97. https://doi.org/10.1108/JCM-04 -2015-1399 Hoffman, D. L., Moreau, C. P ., Stremersch, S., & Wedel, M. (2022). The rise of new technologies in marketing: A framework and outlook. Journal of Marketing, 86(1), 1–6. https://doi.org/10 .1177/00222429211061636 Howard, J. A., & Sheth, N. J. (1969). The theory of buyer behavior. Wiley. Huber, F., Beckmann, S. C., & Herrmann, A. (2004). Means-end analysis: Does the affective state inuence information process- ing style? Psychology & Marketing, 21(9), 715–737. https://doi .org/10.1002/mar.20026 Indrawati, I., Ramantoko, G., Widarmanti, T., AbdulAziz, I., & Khan, F. U. (2022). Utilitarian, hedonic, and self-esteem motives in online shopping. Spanish Journal of Marketing - ESIC, 26(2), 231–346. https://doi.org/10.1108/SJME-06-2021-0113 Inoue, Y., Funk, D. C., & McDonald, H. (2017). Predicting be- havioral loyalty through corporate social responsibility: The mediating role of involvement and commitment. Journal of Busi- ness Research, 75, 46–56. https://doi.org/10.1016/j.jbusres.2017 .02.005 Jaeger, S. R., Chheang, S. L., & Bredahl, C. L. (2023). Means-end chain generation with online laddering: A study on vertical farming with consumers in Singapore and Germany. Food Qual- ity and Preference, 106, Article 104794. https://doi.org/10.1016/ j.foodqual.2022.104794 Jalalian, M., Latiff, L., Hassan, S. T. S., Hanachi, P ., & Othman, M. (2010). Development of a questionnaire for assessing fac- tors predicting blood donation among university students: A pilot study. Southeast Asian Journal of Tropical Medicine and Public Health, 41(3), 660–666. Jiang, S., Scott, N., Ding, P ., & Zou, T. T. (2014). Using means-end chain theory to explore travel motivation: An examination of Chinese outbound tourists. Journal of Vacation Marketing, 21(1), 87–100. https://doi.org/10.1177/1356766714535599 Kassarjian, H. H. (1977). Content analysis in consumer research. Journal of Consumer Research, 4(1), 8–18. https://doi.org/10 .1086/208674 Kilwinger, F. B. M., & van Dam, Y. K. (2021). Methodological consid- erations on the means-end chain analysis revisited. Psychology & Marketing, 38, 1513–1524. https://doi.org/10.1002/mar.21521 Kuisma, T., Laukkanen, T., & Hiltunen, M. (2007). Mapping the reasons for resistance to internet banking: A means-end ap- proach. International Journal of Information Management, 27(2), 75–85. https://doi.org/10.1016/j.ijinfomgt.2006.08.006 Lagerkvist, C. J., Ngigi, M., Okello, J. J., & Karanja, N. (2012). Means-end chain approach to understanding farmers’ motiva- tions for pesticide use in leafy vegetables: The case of kale in peri-urban Nairobi, Kenya. Crop Protection, 39, 72–80. https:// doi.org/10.1016/j.cropro.2012.03.018 Malhotra, N., Kim, S. S., & Agarwal, J. (2004). Internet users’ in- formation privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032 Martin, K. D., & Murphy, P . E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135– 155. https://doi.org/10.1007/s11747-016-0495-4 Mazurek, G., & Małagocka, K. (2019). Perception of privacy and data protection in the context of the development of arti- cial intelligence. Journal of Management Analytics, 6(4), 344–364. https://doi.org/10.1080/23270012.2019.1671243 Merfeld, K., Wilhems, M. P ., & Henkel, S. (2019). Being driven autonomously: A qualitative study to elicit consumers’ over- arching motivational structures. Transportation Research Part C: Emerging Technologies, 107, 229–247. https://doi.org/10.1016/ j.trc.2019.08.007 Miltgen, L. C. (2009). Online consumer privacy concern and will- ingness to provide personal data on the internet. International Journal of Networking and Virtual Organisations, 6(6), 579–603 Mooradian, T. A., & Olver, J. M. (1996). Shopping motives and the ve factor model: An integration and preliminary study. Psychological Reports, 78(2), 579–592. https://doi.org/10.2466/ pr0.1996.78.2.579 Moorman, C. (1990). The effects of stimulus and consumer char- acteristics on the utilization of nutrition information. Journal of Consumer Research, 17(3), 362–374. https://doi.org/10.1086/ 208563 Mort, G. S., & Rose, T. (2004). The effect of product type on value linkages in the means-end chain: Implications for theory and method. Journal of Consumer Behaviour: An International Research Review, 3(3), 221–234. https://doi.org/10.1002/cb.136 Naspetti, S., Bteich, R. M., Pugliese, P ., & Salame, N. (2016). Motiva- tion and values of farmers in Lebanon: A comparison between organic and conventional agricultural producers. New Medit, 15(2), 70–80. Ngigi, M. W., Mueller, U., & Birner, R. (2017). Gender differ- ences in climate change adaptation strategies and participation in group-based approaches: An intra-household analysis from rural Kenya. Ecological Economics, 138(C), 99–108. https://doi .org/10.1016/j.ecolecon.2017.03.019 Nili, M., Delavari, D., Tavassoli, N., & Barati, R. (2013). Impacts of utilitarian and hedonistic values of online shopping on preferences and intentions of consumers. International Jour- nal of Academic Research in Business and Social Sciences, 3(5), 82–92. Okello, D. M., Bonabana-Wabbi, J., & Mugonola, B. (2019). Farm level allocative efciency of rice production in Gulu and Amuru districts, Northern Uganda. Agricultural Economics, 7, Article 19. https://doi.org/10.1186/s40100-019-0140-x Olson, J. C. (1978). Theories of information encoding and storage: Implications for consumer research. In A. A. Mitchell (Ed.), The effect of information on consumer and market behavior (pp. 49–60). American Marketing Association. Olson, J. C., & Muderrisoglu, A. (1979). The stability of responses obtained by free elicitation: Implications for measuring at- tribute salience and memory structure. Advances in Consumer Research, 6(1), 269–275. Overby, W. J., & Lee, J. E. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10–11), 1160–1166. https://doi.org/10.1016/j.jbusres.2006.03.008 Patrick, K., & Xu, Y. (2018). Exploring Generation Y con- sumers’ tness clothing consumption: A means-end chain approach. Journal of Textile and Apparel Technology & Management, 10(3). 72 ECONOMIC AND BUSINESS REVIEW 2026;28:59–72 Petronio, S. (2012). Boundaries of privacy: Dialectics of disclosure. SUNY Press. Phillips, J. M., & Reynolds, T. J. (2009). A hard look at hard laddering: A comparison of studies examining the hierarchi- cal structure of means-end theory. Qualitative Market Research: An International Journal, 12(1), 83–99. https://doi.org/10.1108/ 13522750910927232 Pieters, R., Baumgartner, H., & Allen, D. (1995). A means-end chain approach to consumers’ goal structures. International Journal of Research in Marketing, 12(3), 227–244. https://doi.org/10.1016/ 0167-8116(95)00023-U Plangger, K., & Montecchi, M. (2020). Thinking beyond privacy calculus: Investigating reactions to customer surveillance. Jour- nal of Interactive Marketing, 50, 32–44. https://doi.org/10.1016/ j.intmar.2019.10.004 Regulation 2016/679. On the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Reg- ulation). European Parliament and Council. http://data.europa .eu/eli/reg/2016/679/2016-05-04 Regulation 2018/1725. On the protection of natural persons with regard to the processing of personal data by the Union institutions, bodies, ofces and agencies and on the free movement of such data, and re- pealing Regulation (EC) No 45/2001 and Decision No 1247/2002/EC. European Parliament and Council. http://data.europa.eu/eli/ reg/2018/1725/oj Reynolds, T. J., & Gutman, J. (1988). Laddering theory, method, analysis, and interpretation. Journal of Advertising Research, 28(1), 11–13. Reynolds, T. J., & Olson, J. C. (2001). Understanding consumer decision making: The means–end approach to marketing and advertising strat- egy. Psychology Press. https://doi.org/10.4324/9781410600844 Rintamäki, T., Kuusela, H., Kanto, A., & Spence, M. T. (2006). Decomposing the value of department store shopping into util- itarian, hedonic and social dimensions: Evidence from Finland. International Journal of Retail & Distribution Management, 34(1), 6–24. https://doi.org/10.1108/09590550610642792 Rock, I., & Gutman, D. (1981). The effect of inattention on form per- ception. Journal of Experimental Psychology: Human Perception and Performance, 7(2), 275–285. https://doi.org/10.1037/0096-1523 .7.2.275 Ronda, L., Valor, C., & Abril, C. (2018). Are they willing to work for you? An employee-centric view to employer brand attrac- tiveness. Journal of Product & Brand Management, 27(5), 573–596. https://doi.org/10.1108/JPBM-07-2017-1522 Rothschild, N., & Aharony, N. (2022). Self-disclosure of mentally ill individuals in private Facebook groups: The means-end chain model. Proceedings of the Association for Information Science & Technology, 59(1), 788–790. https://doi.org/10.1002/pra2.727 Runkel, P . J., & McGrath, J. E. (1972). Research on human behavior: A systematic guide to method. Holt, Rinehart & Winston. Schiffman, L. G., & Kanuk, L. L. (1997). Consumer behavior (6th ed.). Prentice Hall. Schweidel, D. A., Bart, Y., Inman, J. J., Stephen, A. T., Libai, B., Andrews, M., Babi´ c Rosario, A., Chae, I., Chen, Z., Kupor, D., Longoni, C., & Thomaz, F. (2022). How consumer digital signals are reshaping the customer journey. Journal of the Academy of Marketing Science, 50(6), 1257–1276. https://doi.org/10.1007/ s11747-022-00839-w Sheth, J. N. (1973). A model of industrial buyer behavior. Journal of Marketing, 37(4), 50–57. https://doi.org/10.1177/ 002224297303700408 Solomon, M. (1996). Consumer behavior: Buying, having, and being. Prentice Hall. Tamilmani, K., Rana, N. P ., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of brain vs. heart: A literature review and meta- analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, 46, 222–235. https://doi.org/ 10.1016/j.ijinfomgt.2019.01.008 Tauber, E. M. (1972). Why do people shop? Journal of Marketing, 36, 46–49. https://doi.org/10.2307/1250426 Tey, Y. S., Arsil, P ., Brindal, M., Lee, S. K., & Teoh, C. T. (2020). Motivation structures of blood donation: Ameans-end chain ap- proach. International Journal of Health Economics and Management, 20(1), 41–54. https://doi.org/10.1007/s10754-019-09269-8 Tran, T. P ., Blanchower, T. M., & Lin, C. W. (2024). Examin- ing the effects of Facebook’s personalized advertisements on brand love. Journal of Marketing Theory and Practice, 32(1), 61–80. https://doi.org/10.1080/10696679.2022.2096637 Turow, J., Hennesy, H. M., & Bleakley, A. (2008). Consumers’ understanding of privacy rules in the marketplace. Journal of Consumer Affairs, 42(3), 411–424. https://doi.org/10.1111/j.1745 -6606.2008.00116.x Urrea-Hernandez, C., Almekinders, C. J. M., & van Dam, Y. K. (2016). Understanding perceptions of potato seed quality among small-scale farmers in Peruvian highlands. NJAS Wa- geningen Journal of Life Sciences, 76(1), 21–28. https://doi.org/ 10.1016/j.njas.2015.11.001 Valette-Florence, P ., & Rapacchi, B. (1991). Improvements in means- end chains analysis: Using graph theory and correspondence analysis. Journal of Advertising Research, 31(1), 30–45. https://doi .org/10.1080/00218499.1991.12466758 Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence in a real-life setting: Toward a motivational model of high school dropout. Journal of Personality and So- cial Psychology, 72(5), 1161–1176. https://doi.org/10.1037/0022 -3514.72.5.1161 Vanden Abeele, M., Beullens, K., & Roe, K. (2013). Measuring mo- bile phone use: Gender, age, and real usage level in relation to the accuracy and validity of self-reported mobile phone use. Mobile Media & Communication, 1(2), 213–236. https://doi.org/ 10.1177/2050157913477095 Van Rekom, J., & Wierenga, B. (2007). On the hierarchical nature of means-end relationships in laddering data. Journal of Business Research, 60(4), 401–410. https://doi.org/10.1016/j.jbusres.2006 .10.004 Veludo-de-Oliveira, T. M. (2006). Laddering in the practice of marketing research: Barriers and solutions. Qualitative Mar- ket Research: An International Journal, 9(3), 297–306. https://doi .org/10.1108/13522750610671707 Vinson, D. E., Scott, J. E., & Lamont, L. M. (1977). The role of personal values in marketing and consumer behavior. Journal of Marketing, 41(2), 44–50. https://doi.org/10.1177/ 002224297704100215 Wagner, H. (2007). Principles of operations research with application to managerial decision. Prentice Hall Press. Walker, A. B., & Olson, J. C. (1991). Means-end chains: Connecting products with self. Journal of Business Research, 22(2), 111–118. https://doi.org/10.1016/0148-2963(91)90045-Y Wansink, B. (2000). New techniques to generate key marketing in- sights. Journal of Marketing Research, 12(2), 28–36. Weydert, V ., Desmet, P ., & Lancelot, C. (2019). Convincing con- sumers to share personal data: Double-edged effect of offering money. Journal of Consumer Marketing, 37(1), 1–9. https://doi .org/10.1108/JCM-06-2018-2724 Wheeless, L. R., & Grotz, J. (1976). Conceptualization and mea- surement of reported self-disclosure. Human Communication Re- search, 2(4), 338–346. https://doi.org/10.1111/j.1468-2958.1976 .tb00494.x Xiao, L., Guo, Z., D’Ambra, J., & Fu, B. (2014). Understanding online group purchase decision making: A means-end chain approach. In P ACIS 2014 proceedings, Article 290. https://aisel.aisnet.org/ pacis2014/290/ Zanker, M., Rook, L., & Jannach, D. (2019). Measuring the impact of online personalization: Past, present and future. International Journal of Human-Computer Studies, 131, 160–168. https://doi .org/10.1016/j.ijhcs.2019.06.006 Zanoli, R., & Naspetti, S. (2002). Consumer motivations in the purchase of organic food: A means-end approach. British Food Journal, 104(8), 643–653. https://doi.org/10.1108/ 00070700210425930 Zimaitis, I., Urbonaviˇ cius, S., Degutis, M., & Kaduskeviˇ ci ¯ ut˙ e, V . (2022). Inuence of trust and conspiracy beliefs on the dis- closure of personal data online. Journal of Business Economics and Management, 23(1), 150–165. https://doi.org/10.3846/jbem .2022.16119