Keywords

Introduction

In environments characterized by rapidly rising competition, online brand-related platforms, including social media or virtual communities, are proven tools to engage consumers (Bowden et al., 2017; Brodie et al., 2013; Carlson et al., 2017). These environments offer myriad interactive tools, including through virtual reality-, digital content marketing-, or gamification-based applications, to name the substantial ones (e.g. Hollebeek et al., 2020; Leclercq et al., 2020), which let consumers to engage with brands, including by sharing brand-related ideas, posting brand-related content, or private messaging the company (Labrecque et al., 2013; Pookulangara & Koesler, 2011). These tools also afford personalized, typically opt-in-based interactivity that tends to be synonymous with high consumer-perceived relevance and value, thereby offering significant business opportunity (Füller, 2010; Hollebeek et al., 2017).

In this environment, interactive consumer engagement (CE) is a firm performance indicator of rising cachet (De Vries & Carlson, 2014; Kumar et al., 2010), warranting its growing research interest (Harmeling et al., 2017). CE is understood as consumer’s attachment with his/her brand and the resources spent on building association with it (Brodie et al., 2011; Hollebeek et al., 2019). In the last decade, the CE literature has made significant strides, and studies have explored the various facets of the concept (e.g., involvement), and its influence on behavior and brand loyalty (Harrigan et al., 2018; Nyadzayo et al., 2020).

However, while a range of CE’s nomological network-based associations have been examined (France et al., 2016; Junaid et al., 2020), the influence of consumer values, which reflect an individual’s belief in or ascribed importance to particular issues (e.g. purchasing sustainable products), in shaping or affecting CE remains tenuous (Etgar, 2008), thus exposing an important research gap. While, in a study, Islam et al. (2018) examined the association of value congruity and CE, they did not differentiate terminal vs. instrumental values, as undertaken in this study that thus builds on and extends prior research. Though Marbach et al. (2019) deployed individualistic values as a moderator to understand the association between consumer personality dimensions and online brand engagement, the impact of personal values on CE remains obscure, thus warranting further research in this area.

Consumer values have been argued to be crucial in the development of value-laden consumer/brand relationships. Specifically, the greater a brand’s alignment or fit with a consumer’s values, the higher the individual’s expected brand engagement (Higgins & Scholer, 2009; Islam et al., 2018), as explored empirically in this paper. We offer the following main contribution. Our analyses offer novel acumen regarding the effect of consumer values in driving their engagement with fashion brands online (Loureiro et al., 2018), thus responding to the Marketing Science Institute’s (2018) Research Priorities, which solicit further research on consumer/customer engagement. Empirical exploration of CE’s theoretical interface with consumer values is important, given the identified key role of personal values in affecting a range of consumer behavior outcomes, including engagement, as explored in this paper. Our findings matter, because if personal values, which tend to be relatively stable and difficult to change (Roccas et al., 2002), affect CE, a wealth of recommended firm investments in activities aligning with (vs. diverging from) their target customers’ predominant values would ensue.

The paper is structured as follows. The review of past literature on consumer values, engagement, and behavioral intent, is followed by development of research hypotheses. We then proceed by outlining the methodology adopted to explore the hypotheses, followed by explanation of the results. We complete the study by discussing the results and deriving key implications from the analyses.

Literature Review and Hypothesis Development

Consumer Values

Consumer values are important conscious and subconscious drivers of consumer behavior (Schiffman et al., 2003; Torelli et al., 2012), including the formation of attitudes and purchase (Rokeach, 1968, 1979). Debate however surrounds the conceptualization of values. Schwartz (1994) conceptualized values in terms of “desirable trans-situational goals that vary in importance, and serve as guiding principles in the life of a person or other social entity” (p. 21) while Rokeach (1973) views values in terms of “an enduring belief that a specific mode of conduct or end-state of existence is personally or socially preferable to an opposite or converse mode of conduct or end-state of existence” (p. 5). Overall, consumer values reflect consumers’ deeply-held, relatively stable convictions and beliefs that are expected to impact their purchase-related behaviors (Lee et al., 2011; Novak & MacEvoy, 1990).

Values also see different operationalization. For example, the List of Values (LoV) addresses the individual’s fulfilment of various roles through value. It includes nine values such as security, self-respect, self-fulfillment, warm relationships, being well-respected, and sense of accomplishment, sense of belonging, fun, enjoyment and excitement in life (Kahle et al., 1986). In the LoV method, participants are typically asked to rank their two most important values. Second, Values and Life Styles (VALS) classified people according to nine lifestyle groups. It segmented individuals as ‘sustainers, survivors, belongers, emulators, I-am-me, achievers, societally conscious, experiential, and integrated’ based on a proprietary scoring system (Kahle et al., 1986; Mitchell, 1983).

Third, Schwartz’ (1992) Values Survey uses a quasi-circumplex structure to explain the associations between 10 near-universal values. The key values include openness to change (OC) vs. conservation (CO), and self-enhancement (SE) vs. self-transcendence (ST). The OC-CO facet is an internal conflict of “intellectual and emotional interests in unpredictable and uncertain directions” versus preservation of “the status quo and the certainty it provides in relationships with close others, institutions and traditions” (Schwartz, 1992, p. 43). The SE-ST facet is a conflict situation due desire to pursue success and dominance over others, versus a concern for others’ welfare and interests (Lee et al., 2012; Munson & McQuarrie, 1988). Overall, our review shows that a range of conceptual approaches to consumer values already exist that have been successfully applied in the literature. We, further zoom in on consumers’ terminal and instrumental values, which have been taken up in current research.

Terminal and Instrumental Values

Rokeach (1968) sees ‘values’ as operating together as a system in which different values hold differing importance levels for different individuals. Rokeach (1973) distinguishes terminal values were demarcated as beliefs about preferred end-states of life (i.e., a comfortable life-style), and instrumental values, which are beliefs about preferred modes of action to achieve one’s desired end-states of life (e.g., ethical behavior). Rokeach’s hierarchical system directs behavioral choices (instrumental values) that yield to particular end-states desired over other states, in line with one’s terminal values. This model thus stresses the non-mutually exclusive nature of values, which can both be achieved simultaneously (Kautish et al., 2020a). However, in some situations, values may compete (e.g. for the individual’s scarce resources, e.g. time), requiring the person to prioritize their perceived most important value (Sundström et al., 2019). Based on their prioritization of terminal (vs. instrumental) values, consumers are expected to behave differently. For example, Allen et al. (2002) report that individuals who favor instrumental (vs. terminal) values were predisposed to extracting functional, utilitarian meanings from their brands, while those favoring terminal values focused on attributing symbolic meaning to brands.

Consumer Engagement

The literature reveals a lack of consensus on consumer engagement’s (CE’s) definition (Groeger et al., 2016). For example, while Hollebeek et al. (2019, p. 166) state that CE can be described as a consumer’s “motivationally driven, volitional investment of… operant resources (e.g. cognitive, emotional, behavioral, and social knowledge/skills), and operand resources (e.g. equipment) in brand interactions”. Researchers like Kumar et al. (2019) and Brodie et al. (2011, p. 260) opine CE as “a psychological state that occurs by virtue of interactive, cocreative customer experiences with a focal agent/object (e.g. a brand) in focal service relationships.” We deduce the following CE hallmarks from our review.

First, CE is an interactive concept that transpires within the consumer-brand interaction (Harrigan et al., 2018). Second, CE reflects consumers’ investment in their brand interactions, with greater investments denoting higher engagement (Harmeling et al., 2017; Hollebeek, 2011). Third, it is understood as a multi-faceted conception which encompasses cognitive, emotional, and behavioral magnitudes (Hollebeek et al., 2014; Vivek et al., 2012). Some scholars have focused their research on engagement behaviors, including brand-related citizenship behaviors such as advocacy or blogging (e.g. Groeger et al., 2016). Fourth, common agreement among researchers is that CE is conducive to a number of firm performance improvements, including sales growth, enhanced referrals, etc. (Brodie et al., 2011).

While several studies have linked consumer values and CE (e.g. Marbach et al., 2019), insufficient acknowledged the role of consumers’ terminal (vs. instrumental) values in driving their engagement with brands. First, consumers’ instrumental values reflect an individual’s desired mode of action, as discussed. Given CE’s interactive nature (Clark et al., 2020), consumers’ desired modus operandi will affect their resource investments and the route they take to achieve their goal, thus impacting CE (Aghekyan-Simonian et al., 2012; Gambetti & Graffigna, 2010). Second, terminal values are a consumer’s beliefs about preferred end-states (e.g. owning their desired brands). Therefore, individuals’ terminal values are likely to affect the way they go about in reaching their goal fulfillment, thus impacting the consumer’s interactive resource investment. We posit:

H1a: Terminal values positively affect consumer engagement.

H1b: Instrumental values positively affect consumer engagement.

Interface of Values, Consumer Engagement, and Behavioral Intent

Prior research demonstrates the important role of consumer values in driving their ensuing behavioral intent (e.g. purchase intent) and actual behavior in a range of contexts or sectors (Kahle et al., 1986; Kim et al., 2002; Xiao & Kim, 2009). Hence, we posit that consumer values would exert a direct effect on consumers’ behavioral intent:

H2a: Terminal values positively affect consumers’ behavioral (purchase) intent.

H2b: Instrumental values positively affect consumers’ behavioral (purchase) intent.

Prior studies also show CE’s favorable effect on consumer purchase intent (e.g. Hollebeek et al., 2014; Islam et al., 2019; Rather et al., 2019). We propose:

H3: CE positively affects consumers’ behavioral (purchase) intent.

Moreover, given CE’s nature as a consumer’s resource investment within a brand interaction (Hollebeek and Chen, 2014; Kumar et al., 2019), repeated instances or episodes of CE reveal a CE trajectory or process (Brodie et al., 2011). Since CE is very much about the interactive process, we expect consumers’ instrumental (vs. terminal) values to exert a particularly significant effect on CE. That is, with instrumental values addressing consumers’ desired mode of action, they directly relate to the notion of consumer resource investments throughout these modes of action and interactions. For the same reason, we anticipate consumers’ instrumental values, which describe their way of achieving their goals, to exert a greater effect on their behavioral intent than terminal values, which address consumers’ desired end-states. We hypothesize:

H4: Instrumental (vs. terminal) values have a greater effect on CE.

H5: Instrumental (vs. terminal) values have a greater effect on consumers’ behavioral intent.

Moderating Role of Consumer Age

Moderators systematically modify the strength or (form) of the relationship between a predictor and a criterion factor (Hair et al., 2010). Hence, we suggest a moderating role of consumer age in the relationship of CE and behavioral intent, as delineate. Our contention is rooted in the following basis: During the individual’s life-span, which likely value other persons, services and objects differently (Loureiro & Roschk, 2014). Accordingly, consumer age has been considered as a key demographic variable in consumer-research (Khan et al., 2020; Schirmer et al., 2018). Thus, consumers with different age profiles are likely to reveal varying psychological-, cognitive-, and behavioral- facets toward specific offerings that impact their consequent customer behavior (Rather & Hollebeek, 2021). Extant works claims that customer desires and responses alter throughout the life cycle (Loureiro & Roschk, 2014; Ye et al., 2018). As a result, consumer age can influence the way consumers perceive brand cues and react to marketing messages (e.g., Hervé & Mullet, 2009), generating its moderating affect. Such as aged customers are most driven by schema or heuristic-based processing (e.g., Yoon, 1997), representing their decreased likelihood to look for novel information to make decisions (Rather & Hollebeek, 2021), that can be elucidated by these customers having superior maturity and emotional control.

Furthermore, older verses (younger) consumers likely reveal elevated brand-commitment and loyalty, generating distinctive behavioral and affective responses (Homburg & Giering, 2001). Thus, consume rage has been revealed to effect the relationship between consumer behavior-related factors (Homburg & Giering, 2001; Rather & Hollebeek, 2021). Although, the role of consumers’ age on their consequent behavioral factors remains hazy, call for further investigation. Particularly, the effect of consumer age on the relationship between CE and behavioral intent remains nebulous (Khan et al., 2020; Rather & Hollebeek, 2021), as thus investigated in this research. That is to say, we presume consumers with different age profiles having different wants, preferences, or needs (Hervé & Mullet, 2009), therefore differentially effecting the path between CE and behavioral intent across age groups (Khan et al., 2020). On the basis of this justification, we suggest:

H6: Consumer age moderates the impact of CE on consumer’s behavioral intent.

Research Methodology

Data Collection and Sampling

In the present study, we concentrated on the online fashion situation, where consumers purchase fashion or apparel though e-tailing websites or portals. We chose fashion, given the centrality of fashion brands to consumers’ identity (Sprott et al., 2009) and their typical high engagement with their clothing choices, particularly in our chosen Indian context (Kautish et al., 2020b; Khare, 2014; NASSCOM, 2018). We selected younger respondents (aged 18–30), given their typical interest in fashion and their high usage of online purchase (e.g. e-commerce) channels (Ladhari et al., 2019; Pandey & Chawla, 2014). Table 14.1 outlines demographic characteristics of the study participants.

Table 14.1 Demographic respondent profile (N = 412)
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Prior to data collection, we used the a-priori sample size calculator for structural equation models (Iacobucci, 2010). For the model, which contains four latent, eighteen observed variables, and an estimated effect size of 0.3 (p = 0.5), the requisite sample size was 137 (Soper, 2020). The data was collected from students at three premier fashion schools in the metropolitan cities of New Delhi, Jaipur and Ahmedabad, in north India between November 2019 and February 2020. Each of the respondents made frequent online purchases. Before the main survey, screening questions ensured all participants were in the required age range between 18 and 30 years and should have made at least one purchase in the last month via e-commerce-based fashion sites. After distributing 1,256 questionnaires, we obtained 412 complete responses, yielding a 32.8% response rate (Hair et al., 2006). Though our use of a convenience sample has its limitations, students have been shown to offer reliable responses in e-tailing research (Kinley et al., 2010; Kautish & Rai, 2018, 2019; Kautish & Sharma, 2018).

Common Method Variance Testing

We drew on Podsakoff et al.’s (2003) suggestions to minimize the occurrence of common method variance in our data. Firstly, at the research design phase, item priming effect, consent bias in the survey instrument (i.e., “yes or no argument”), and other possible issues were smoothed out in our survey instrument. Secondly, we formally verified for common method variance by adopting Harman’s single-factor test. The results revealed that the variance explicated by the highest factor loading was 14% only; hence none of the factors exceeded the50% threshold (Malhotra et al., 2006). Consequently, common method variance was not an issue in the present research.

Measures

To establish our measures, we first generated relevant scale items from the extant literature, followed by item refinement procedures to ensure the items’ suitability in our chosen context. We then pilot-tested our items with two Marketing/Fashion Professors to further test the items’ face validity before administering the survey, which revealed no issues. We sourced the following measures from the literature. To measure terminal/instrumental values, we drew on Allen and Ng’s (1999), Kamakura and Mazzon’s (1991), and Kamakura and Novak’s (1992) scales. To gauge consumer engagement, we used Baldus et al. (2015) and Hollebeek et al. (2014) instruments. Finally, we measured behavioral intent by drawing on Goldsmith et al. (2012) and Kim and Kim (2004).

All scale items were rated on a 7-point Likert-type measures (where 7: strongly agree and 1: strongly disagree). The mean and standard deviation for terminal values ranged from 4.15 to 5.33, and 1.05 to 1.27, respectively. Likewise, the mean and standard deviation for instrumental values ranged from 2.86 to 5.28, and 1.01 to 1.34, respectively. For consumer engagement, the mean and standard deviation ranged from 4.27 to 5.65, and 1.02 to 1.21, respectively. Similarly, for behavioral intent, the mean and standard deviation ranged from 4.83 to 5.65, and 1.13 to 1.27, respectively (see Table 14.2).

Table 14.2 Constructs, items, and descriptive statistics
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Results

Anderson and Gerbing’s (1988) two-stage method for structural equation modeling (SEM) was applied (Bagozzi & Yi, 2012; Bollen and Long, 1993), which includes analysis of the measurement and the structural models (Byrne, 2010). While the measurement model is utilized to assess construct validity and model fit through confirmatory factor analysis (CFA), the structural model is utilized to evaluate the hypothesized relationships described in the conceptual model (Bollen and Long, 1993).

Measurement Model Testing Results

The measurement model with four-factors was subjected to confirmatory factor analysis by utilizing the maximum likelihood assessment (Hu & Bentler, 1998). The fit indices included: CFI = 0.95; NFI = 0.96; NNFI = 0.95; RMSEA = 0.053, revealing the acceptable model fit. While the model’s Chi-square was significant: χ2 = 268.39; df = 96 (p < 0.001). The attained χ2/df was 2.79, which is within the tolerable range of 2–5 (Bagozzi & Yi, 2012).

However, two instrumental values items, and one terminal values item, did not fulfill the minimum threshold of 0.40 on their respective standardized factor loadings (Fornell & Larcker, 1981). These items were thus eliminated from further data analysis, thus improving the model’s reliability and lowering the scale’s measurement error (Nunnally & Bernstein, 1994). The residual 15 items were tested through confirmatory factor analysis, which led to an acceptable model fit: CFI = 0.97; NFI = 0.96; NNFI = 0.97, RMSEA = 0.055; and χ2 = 216.85; df = 82 (p < 0.001; Bagozzi & Yi, 2012). The measurement model results considerably improved, as the delta Chi-square estimates between the first and second CFA models estimate was significant: ∆χ2 = 51.54; ∆df = 14 (p < 0.001). All items exceeded the minimum threshold of 0.40 for their standardized factor loadings (Hu & Bentler, 1999).

Construct Reliability and Validity Testing Results

Our constructs’ convergent validity was established by considering average variance extracted (AVE) and composite reliability (CR). Convergent validity testing indicated that all constructs’ items correlated well with each other (Hair et al., 2006). Moreover, the AVE and CR were within the tolerable limits: AVE > 0.50; CR > 0.70 and CR > AVE (Fornell & Larcker, 1981; Hair et al., 2006). Our constructs’ AVEs were calculated and matched with Fornell and Larcker’s (1981) ratio, which yielded results exceeding.50 (see Table 14.3), supporting convergent validity of the constructs. Average shared squared variance (ASV), maximum shared squared variance (MSV), and AVE was employed to test for discriminant validity (Hair et al., 2006).

Table 14.3 Measurement model
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As per Fornell and Larcker (1981), higher AVE values (vs. the shared variance amongst the squared correlations for separately construct pair) establish discriminant validity. Since the ASV and MSV values were lower than the AVEs, the constructs were shown to not correlate highly, thereby confirming discriminant validity (Table 14.3). Further, to ascertain the internal consistency or reliability of the scale items, we used reliability coefficient (Cronbach’s alpha). All Cronbach’s alpha values bettered the minimum threshold of 0.70 (Nunnally & Bernstein, 1994), thus confirming the adequate reliability of the constructs (Jöreskog, 1993).

Structural Model

The structural model was examined by using maximum likelihood assessment. The structural model results are as follows: CFI = 0.98; NFI = 0.97; NNFI = 0.97, RMSEA = 0.056 and χ2 = 220.84; df = 83 (p < 0.001, revealing acceptable model fit (Bagozzi & Yi, 2012; Byrne, 2010; also see Table 14.4). The model’s structural relationships are shown in the Fig. 14.1. As presented in the Table 14.4 and Fig. 14.1, the association of terminal values and CE was significant, leading to the acceptance of H1a. Likewise, the association of instrumental values and CE support H1b. These outcomes show that both instrumental and terminal values are key drivers of CE. Moreover, as instrumental and terminal values were evident to exert a significant influence on behavioral intent, H2a and H2bwere also accepted. As expected, CE emerged as a significant predictor of consumers’ behavioral intent, thus support H3.As the influence of instrumental values on CE exceeded that of terminal values, H4was also accepted. Instrumental (vs. terminal) values also revealed a stronger effect on consumers’ behavioral intent, thus accepting H5.

Table 14.4 Structural model
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Fig. 14.1
figure 1

(Source Authors’ data analysis)

Measures of structural equations

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Indirect/Mediating Effect Results

Mediation is used when one aims to understand how changes are transmitted from an independent variable via one or more mediators or intervening variables, which in turn affects the dependent variable (Kline, 2015; Little, 2013). Based on Fox (1985), CE’s mediating effect was measured to further insight into our model. Because the data was normalized and the conceptual model was relatively straightforward, we used Sobel’s (1982) test for indirect/mediating effects. Sobel offers a significance test that gauges the indirect influence of the independent variable on the dependent variable through the mediator for a single mediating model (MacKinnon, 2008), as is the case here. We thus employed the test to measure whether CE significantly affects the independent variables’ (i.e. terminal/instrumental values) impact on the dependent variable (e.g., behavioral intent). The Sobel test results confirm that both instrumental and terminal values had a significant influence on consumers’ behavioral intent through the mediating factor, CE (coefficient TV-CE-BI = 0.21; coefficient IV-CE-BI = 0.43) at an alpha value of 0.01. The indirect path specifies that t-value = mediating influence/standard deviation. Since the direct associations of terminal values and behavioral intent (coefficient = 0.15; t = 2.14; p < 0.05) and instrumental values and behavioral intent (coefficient = 0.25, t = 3.23, p < 0.01) were significant, respectively, CE emerges as a mediator (partial) in the association between instrumental and terminal values with behavioral intent (Hair et al., 2006).

We also measured mediation strength through Variance Accounted For (VAF) (Kline, 2015; MacKinnon et al., 2002). The attained VAF estimates exceeded the 40 percent effect for terminal values and the 60 percent effect for instrumental values, respectively, on behavioral intent, as explained by CE. In addition, as the VAF values ranged from 20–80%, these findings corroborate CE’s partially mediating effect in the association of consumers’ instrumental vs. terminal values for favorable behavioral intents. The total influence of instrumental values on behavioral intent (0.72) exceeded that of terminal values (0.37), revealing the significance of instrumental values in enhancing consumers’ behavioral intent (Sobel, 1982).

Moderating Role of Age

Notable studies have reported that socio-cultural and demographic variables (Khan et al., 2020; Schirmer et al., 2018), i.e., age can potentially affect consumers’ behavioral intent (Rather & Hollebeek, 2021). Therefore the current study operationalized age as moderator in the hypothesized model. The data analyses specified that consumer engagement was influenced by the demographic variable, viz., consumer age (β = 0.13, p > 0.05). Furthermore, consumer age (β = 0.15, p > 0.05) affect the behavioral intent for online fashion apparel purchase so H6 have empirical results for supporting the relationship and the findings are in tandem with the previous studies on consumer engagement (Hervé & Mullet, 2009; Homburg & Giering, 2001).

Discussion, Implications, and Limitations

Theoretical Contributions

This study examined the relationship between consumers’ terminal/instrumental values, engagement, and their ensuing behavioral intent in the e-commerce fashion context. Our structural equation modeling analyses confirm the proposed research hypotheses, thus suggesting the important role of consumers’ values on their engagement, in particular their instrumental (vs. terminal) values. Instrumental values, which are the consumer’s beliefs about desired modes of action in their goal fulfilment processes (Rokeach, 1973, 1979), while terminal values reflect one’s views regarding desired end-states, as discussed. In line with consumer engagement concept’s interactive and process-based characteristics (Brodie et al., 2011), our findings submit that engagement acts as an important vehicle, through which consumers’ highly latent (e.g. instrumental) values become more observable or manifest. Put differently, consumers’ core instrumental values influence their engagement with specific brands (e.g. by seeking out socially responsible brands), thus offering an important theoretical contribution (Kumar & Pansari, 2016).

We also found consumers’ terminal and instrumental values to directly affect their behavioral (purchase intent) for particular brands, in addition to the mediated path via consumer engagement. The findings substantiate the strong role of consumers’ values on consumer behavior. The indirect, mediated path via consumer engagement, in particular, suggests engagement’s capacity as a strategic opportunity for managers. That is, by designing their marketing mix to fit with or appeal to consumers’ key (e.g. instrumental) values, an important strategic opportunity exists to first capture and then capitalizes on consumer engagement. For example, brands aligned with consumers’ instrumental values (e.g. purchasing a gym membership to achieve the terminal value of getting fit), are likely to favorably impact consumer engagement and sparking its positive (vs. negative) expressions (Clark et al., 2020). In turn, once consumers are engaged, their brand-related behavioral intent rises in parallel (Rather et al., 2018).

Managerial Contributions

The current study also provides important managerial strategies for managers. First, we uncovered that consumers’ terminal and instrumental values directly affect consumers’ behavioral intent, as well as via an indirect path mediated by consumer engagement. Our results therefore suggest that appealing to consumers’ values represents a viable basis for marketing tactics and strategy (Hui et al., 2000). It is therefore critical to understand consumers’ main driving values, distinguish their core (vs. peripheral) values, and design brand-related marketing mixes in line with particular customer segments’ core (e.g. instrumental) values (Marbach et al., 2019). As marketers build their brands’ unique personality or identity (Aaker, 1997), they are advised to align their brands’ values with those of their key (prospective) customer segments, which based on our findings is expected to induce their respective engagement with particular brands. As differing values may dominate across segments, managers also need to understand their respective values-based differences to establish effective segment-specific strategies (Kautish & Rai, 2019). To incite or optimize values-based engagement, we recommend brands to offer consumers regular linking and reminders of the ways in which consumers’ personal values align with and are reflected in those of the brand (e.g. through authentic, opt-in-based content marketing deploying high brand symbolism; Cooper et al., 2019; Hollebeek & Macky, 2019).

Second, we identified consumers’ instrumental (vs. terminal) values to asymmetrically impact consumer engagement and behavioral intent, with instrumental values playing a more decisive role. To stimulate engagement’s development by drawing on consumers’ instrumental values, we recommend marketers to emphasize the nature of the customer’s journey in their brand-related marketing mix. The rationale for this assertion is that the customer journey fits with instrumental values’ process-based nature, including the brand’s modus operandi, as outlined. The customer journey comprises a series of touchpoints or engagement episodes that collectively make up the journey (Lemon & Verhoef, 2016). Herein, each of touchpoint offers an opportunity to engage consumers by appealing to their instrumental values, which may be also be linked to the achievement of their desired end-states or terminal values. Ideally, marketing strategies should be designed to attain consumers’ instrumental as well as terminal values.

Limitations and Further Research

Despite of its notable contribution to the scholarly exploration on consumer engagement, the present research has a few limitations, which should be identified for future studies. First, our data was sourced from a single country (India), by using convenience sampling, and therefore, the findings may not be generalized for other regions. Thus, further research may be conducted in different (e.g. cultural) settings, thus enhancing academic acumen with respect to the role of consumer values in shaping their engagement with brands, which is likely to vary across cultures or countries (Gupta et al., 2018; Hollebeek, 2018).

Second, though we explored the effect of consumers’ terminal/instrumental values on their ensuing engagement, future studies may like to extend our model (i.e., by incorporating additional constructs, such as consumer involvement, brand love, brand attachment, and so so). The specific constructs contained in future models should be guided by the particular perspective deployed (e.g. by adopting differing or potentially competing values paradigms, such as List of Values or Values and Life Styles, as discussed).

Third, since consumer engagement is a state-based variable that may fluctuate over time (Brodie et al., 2011; Calder et al., 2009), the model should ideally be examined in a longitudinal research design, which - unlike our cross-sectional data-affords a better ability to capture this variability (Viswanathan et al., 2017). For example, the attained results may reveal varying perceived importance levels of particular values throughout the consumer’s life-cycle, with particular values differentially affecting their engagement and behavioral intent over time.