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Article

Consumer Citizenship Behavior in Online/Offline Shopping Contexts: Differential Impact of Consumer Perceived Value and Perceived Corporate Social Responsibility

by
Qiulian Shen
1,
Yuxuan Du
2,* and
Jingxian Huang
3
1
PBC School of Finance, Tsinghua University, Beijing 100083, China
2
School of Economics and Management, Tsinghua University, Beijing 100084, China
3
Academy of Arts & Design, Tsinghua University, Beijing 100085, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(7), 2968; https://doi.org/10.3390/su16072968
Submission received: 17 February 2024 / Revised: 21 March 2024 / Accepted: 27 March 2024 / Published: 2 April 2024
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Abstract

:
The digital era has profoundly reshaped consumer behavior, with social media and e-commerce platforms revolutionizing shopping experiences. This study integrates interviews with questionnaire survey to investigate the nuanced differences in consumer citizenship behavior (CCB) across online and offline shopping contexts, as well as the influence of consumer perceived value (CPV) and consumer perceived corporate social responsibility (CPCSR). Through the use of multiple regression and permutation tests to analyze the interplay between consumer perception and CCB, the study reveals the following: (1) CPV positively influences all four dimensions of CCB, while CPCSR significantly impacts only recommendation and feedback; (2) the influence of CPV on dimensions other than recommendation and of CPCSR on dimensions other than helping varies significantly between online and offline contexts. These results enhance our understanding of consumer behavior and offer actionable insights for businesses to build and enhance consumer connections in the digital age.

1. Introduction

The rise of digital platforms and social media has revolutionized the retail landscape, offering consumers novel pathways to shop and altering their purchasing dynamics. This shift has not only provided new avenues for consumers to share and endorse products but also to advocate for and engage with brands that align with their values, including sustainability. Engaging actively on these platforms, consumers play a critical role in amplifying brands that prioritize sustainable practices, influencing both corporate reputation and market trajectory. Their comprehensive feedback and enhanced interactions among peers are instrumental in safeguarding corporate reputation and swaying the purchase decisions of fellow shoppers, thereby contributing to the long-term sustainable growth of businesses [1,2]. The acts of sharing, recommending, and feedback provision on digital mediums significantly bolsters their influence on businesses’ long-term market trajectories and sustainable growth [3,4].
While consumer citizenship behavior (CCB) within physical retail environments retains its relevance, the expansive and interactive nature of online platforms enables consumers to exert a more potent influence on others’ buying choices, particularly in promoting sustainable consumption patterns. Understanding the differences between these environments is crucial for crafting effective marketing strategies, as it allows businesses to better comprehend and meet consumer needs and expectations across various shopping contexts. This understanding also sheds light on the complexity of consumer behavior and how to stimulate and sustain consumer citizenship behaviors in different retail settings. This influence profoundly shapes corporate market strategies, as companies increasingly recognize the value of aligning their practices with sustainable development goals to resonate with conscientious consumers. The distinct dynamics of online and offline environments necessitate a tailored approach to engaging consumers, underscoring the importance of understanding the nuanced interplay between consumer perceptions and behaviors in these settings. Such an understanding is vital for harnessing the full potential of CCB to enhance brand equity, reputation, and drive sustainable growth. Consequently, studying the diversity of CCB expressions and their potential motivations, as well as providing actionable insights for businesses to effectively address the complexity of modern consumer behavior across various shopping contexts, has become an urgent academic endeavor.
In the scholarly discourse surrounding CCB, the constructs of consumer perceived (product) value (CPV) and perceived corporate social responsibility (CPCSR) have been pivotal factors, identified as ‘tangible’ and ‘intangible’, respectively, that are intricately linked with consumer behavioral dynamics. These constructs gain additional significance when considering the role of consumers as agents of change in sustainable development and corporate ethical practices. While CCB has traditionally been linked to consumer actions that benefit the marketplace and the community, the inclusion of CPCSR brings forth the aspect of consumers advocating for and rewarding businesses that demonstrate a commitment to social and environmental responsibility. This expanded context enriches our understanding of CCB and CPCSR, highlighting their interplay in fostering a sustainable consumption culture.
The literature has to date delved into the mechanisms by which these perceptions influence CCB, mediated through constructs such as satisfaction and loyalty [5], while other inquiries have positioned these perceptions as intermediaries elucidating the nuanced interplay between consumer engagement, product quality, and citizenship behaviors [6,7]. Despite these advances, the predominant analytical perspective has been one of a macroscopic nature, often overlooking the granular, multidimensional elements inherent within these relationships. There remains a gap in the comprehensive examination of these constructs within a cohesive theoretical framework that encapsulates the broader social and ethical implications of consumer behavior. Further complicating the analysis is the distinction between online and offline retail contexts, which introduces variability in consumer perceptions and behavioral manifestations, thereby amplifying the complexity inherent within the dimensions of CCB. Addressing the influences of CPV and CPCSR on the multifaceted dimensions of CCB, particularly across divergent shopping contexts, emerges as an imperative, yet challenging, frontier in contemporary research.
In this study, we adopt a nuanced approach through case analysis and empirical methods to juxtapose CCB across the digital and physical shopping landscapes. Our investigation meticulously articulates the varied dimensions of such behaviors—encompassing recommendation, feedback, helping, and tolerance—while weaving together “tangible factors” like CPV, which capture quantifiable attributes such as price and quality, with “intangible factors” such as CPCSR, embracing more subjective elements like corporate culture and social engagement. This endeavor constructs an integrative framework that bridges consumer perceptions with citizenship behaviors. Initially, we leverage case analysis to systematically explore the diverse manifestations of CCB within distinct shopping contexts. This exploration challenges traditional research paradigms by disaggregating CCB into its constituent dimensions for a granular examination. Furthermore, our study advances beyond the confines of singular analytical CPV and CPCSR. Instead, we consider these perceptions as interconnected aspects of consumer perceptions, facilitating a comprehensive analysis. Subsequently, we delve into the nuanced impacts of consumer perceptions on behaviors such as recommendation, feedback, helping, and tolerance, across contrasting online and offline contexts. This effort aims to elucidate the foundational causes and conditions, thereby deepening our understanding of CCB.

2. Literature Review

2.1. Consumer Citizenship Behavior (CCB)

Groth (2005) [8] defined CCB as a voluntary and discretionary activity that positively impacts the service organization. Nagy and Marzouk (2018) [9] elaborated on this by detailing actions customers take, such as mutual helping, feedback to businesses, product promotion, and support during challenging times. CCB comprises four dimensions: recommendation, feedback, helping, and tolerance. Recommendation entails consumers actively endorsing products to peers, evident in word-of-mouth, social media, or online reviews. Feedback includes consumers’ spontaneous suggestions about products and services, aiming to bridge effective company–consumer communication and facilitate company improvements. This feedback is especially pivotal in e-commerce, guiding potential buyers and offering companies market insights. Helping captures consumers aiding one another, often rooted in in-depth product knowledge. Positive interactions between consumers [5], benefit companies by facilitating information flow. Tolerance, on the other hand, is consumers’ patience towards product or service defects, reflecting their willingness to forgive mistakes and provide chances for improvement. Recognizing the distinctive characteristics and significance of each dimension sets the stage for understanding their roles in online and offline contexts. While all four dimensions have consumers as key players, they serve different objectives. Recommendation and helping target consumer–consumer interactions, whereas feedback and tolerance center on consumer–business relations. In the realm of online shopping, these behaviors become more intricate.

2.2. Consumer Perception

Consumer perceptions are intricately formed through interactions with products and services across various touchpoints, embodying the psychological imprints and assessments developed through experiences, utilization, and consumption phases [10]. This construct is inherently multidimensional, capturing elements ranging from product quality and pricing strategies to the overarching corporate and brand image, service excellence, and the totality of the purchase and user experience [11]. Influenced by an array of determinants such as individual preferences, cultural environments, and socioeconomic factors, consumer perceptions stand at the confluence of complex influences [12].

2.2.1. Consumer Perceived Value (CPV)

Within consumer perceptions’ intricate framework, the dimension of CPV is highlighted as a critical facet, representing consumers’ thorough evaluation of products or services. This evaluation predicates on the comparative analysis of benefits against the costs—monetary, temporal, and effort-based [13]. This evaluation extends beyond basic functionalities to include considerations of price, quality, and convenience, offering a comprehensive appraisal [14]. The nexus between CPV and citizenship behaviors has attracted considerable scholarly interest. Kim and Tang (2020) [5] illuminate the transformative influence of citizenship behaviors on shaping CPV, whereas Ru and Jantan (2023) [15] probe into the catalytic role of CPV in fostering CCB.

2.2.2. Consumer Perceived Corporate Social Responsibility (CPCSR)

CPCSR refers to the multidimensional consumer perceptions of a firm’s commitment to social responsibility (encompassing economic development, social equity, and environmental protection responsibilities), which is pivotal for the success of CSR initiatives [16]. As Vitell (2014) [17] posits, the effectiveness of CSR strategies is contingent upon consumer support, suggesting that CPCSR encompasses not just the recognition of CSR efforts but also the consumer’s willingness to engage in behaviors that uphold social responsibility [18,19]. This cognizance bifurcates into evaluating CSR footprint within its sector [20] and gauging the extent of consumer awareness regarding CSR initiatives [21], indicative of their sensitivity towards responsible corporate conduct.
Nonetheless, the integrity of CPCSR is not always guaranteed. The complexity of consumer perceptions is further compounded by the phenomenon of greenwashing, where companies may present an image of environmental and social responsibility without backing it up with substantive actions [22]. This can lead to consumer confusion and mistrust, potentially undermining the very outcomes that CSR initiatives aim to achieve. Therefore, a deeper and more discerning comprehension of CPCSR by consumers is essential for fostering ethical consumption patterns and for the effectiveness of CSR strategies.
While direct inquiries linking CPCSR with CCB are nascent, a burgeoning body of the literature examines the dynamics between CSR engagements and citizenship behaviors [6,7], underscoring the significance of consumer perceptions in shaping ethical consumption patterns. Through an investigation into how consumers perceive both product value and CSR initiatives, and the synergistic influence of these perceptions on CCB, this study endeavors to enrich scholarly discourse with nuanced insights. These findings hold significant implications for both theoretical advancements and practical applications within the realm of consumer behavior, particularly in understanding how CPCSR can influence and enhance CSR outcomes.

2.3. Online versus Offline Shopping Contexts

The scholarly landscape has recently intensified its focus on delineating the unique characteristics and impacts inherent to online and offline shopping contexts. Investigations into these realms reveal that online contexts are typified by interactions facilitated through digital interfaces, encompassing a broad spectrum from e-commerce platforms to social networks, where engagements between consumers and brands are mediated by digital connectivity [23,24]. In juxtaposition, offline contexts are anchored in the tangible world of physical stores and service centers, where consumer behavior unfolds through direct, in-person exchanges [25]. While the existing literature extends into various applications of these environments—ranging from nuances in consumer food selection [26] and organizational purchasing behaviors [27] to the crafting of marketing strategies [28] and shaping of purchasing intentions [29]—the realm of CCB remains comparatively underexamined. Notably, there exists a paucity of research delving into the intricate variations and interplays among factors influenced by the dichotomy of shopping contexts.

3. Hypotheses

We categorize CCB into four primary dimensions: recommendation, feedback, helping, and tolerance [30]. Recommendation behaviors involve consumers actively promoting products or services to potential buyers, often through verbal endorsements, social media shares, or online reviews, marking a direct expression of product or service satisfaction [31]. Feedback behaviors encompass the voluntary suggestions provided by consumers regarding corporate offerings, aimed at enhancing communication between firms and consumers and guiding corporate improvements [32,33]. Such feedback is especially pivotal in e-commerce environments, influencing other potential consumers’ purchase decisions and offering firms invaluable market insights [34]. helping behaviors, defined as the voluntary aid consumers provide each other based on non-economic motives [35,36], typically occur when a consumer with extensive product or service knowledge aids others seeking advice. Kim and Tang (2020) [5] highlight that these positive consumer-to-consumer interactions not only benefit firms but also promote the efficient flow of information. Lastly, tolerance behaviors are characterized by consumers’ patience and leniency towards product or service shortcomings [37], often manifesting as forgiveness for corporate errors, opting against complaints, or allowing firms opportunities for correction. We propose theoretical hypotheses from the perspective of contrasting online and offline contexts, focusing on the differential impacts of CPV and CPCSR on the dimensions of CCB, including feedback, sharing, recommendation, and tolerance behaviors.

3.1. Consumer Perception and Recommendation Behavior

The interplay between CPV and CPCSR significantly catalyzes recommendation behaviors. Syah and Olivia (2022) [38] elucidate that recommendation behaviors are notably more vigorous when CPV surpasses anticipated expectations. In a parallel vein, Karaosmanoglu et al. (2016) [39] identified a predisposition among consumers to advocate for companies that demonstrate exemplary CSR initiatives, suggesting that enhanced perceptions of CSR directly amplify the propensity to recommend.
Within the realm of online consumer engagements, the dynamics influencing these perceptions and subsequent recommendation behaviors exhibit additional complexity. De Canio et al. (2021) [40] highlight that the advent of live-stream shopping enriches consumer experiences by augmenting both pleasure and utilitarian values, thus elevating overall CPV. Moreover, the facilitative role of social media platforms in streamlining word-of-mouth dissemination and online sharing significantly strengthens consumer recommendation behaviors and intentions [41]. Concurrently, the accessibility and efficiency of social media in disseminating CSR information bolster the likelihood of consumers endorsing firms recognized for their social responsibility [42]. Accordingly, we articulate the following hypotheses:
Hypothesis 1 (H1).
Consumer perception positively influences recommendation behavior, with this influence being stronger in online shopping contexts compared to offline.
H1a. 
CPV positively influences recommendation behavior.
H1b. 
CPCSR positively influences recommendation behavior.
H1c. 
The positive influence of CPV on recommendation behavior is stronger in online shopping contexts compared to offline.
H1d. 
The positive influence of CPCSR on recommendation behavior is stronger in online shopping contexts compared to offline.

3.2. Consumer Perception and Feedback Behavior

Building upon the foundational insights of Patterson and Spreng (1997) [43], who established CPV as a pivotal determinant in the consumption journey, Li and Mao (2015) [44] further elucidate that elevated perceptions of value significantly bolster consumer engagement with brands, thereby catalyzing the provision of suggestions for product enhancements. Within the scope of CPCSR, Kim et al. (2020) [45] posit that consumers’ perceptions of a firm’s commitment to ethical practices, such as equitable pricing, employment generation, and environmental stewardship, positively influences their CSR awareness, subsequently motivating more constructive feedback. Echoing this sentiment, Yu et al. (2021) [46] suggest that consumers predisposed to view a company’s social responsibility efforts favorably are more likely to contribute valuable feedback regarding the company’s products and strategic directions.
In the digital realm, online platforms stand out for their unparalleled transparency and symmetry of information, making them critical junctures for fostering connections, dialogues, and interactions between businesses and consumers. These platforms offer a conducive environment for instant and direct feedback from consumers, which not only influences the purchasing decisions of others but also prompts companies to refine their offerings [44]. Delving deeper, Ebrahimi et al. (2022) [47] assert that feedback within online community platforms sponsored by businesses has transcended mere evaluations of product or service quality, evolving into a collaborative process of value co-creation and brand co-development, markedly elevating the feedback’s quality, frequency, and depth. Facilitated by the affordances of multimedia content, social media marketing, and virtual interactions, an enhanced CPV leads to a more dynamic feedback inclination in online contexts, favored by the convenience and anonymity afforded by the digital landscape. Similarly, a heightened awareness of CSR not only forges stronger trust and emotional bonds with the brand but also spurs active community engagement, thereby promoting a culture of positive feedback and interaction [48]. Therefore, we articulate the ensuing research hypotheses:
Hypothesis 2 (H2). 
Consumer perception positively influences feedback behavior, with this influence being stronger in online shopping contexts compared to offline.
H2a. 
CPV positively influences feedback behavior.
H2b. 
CPCSR positively influences feedback behavior.
H2c. 
The positive influence of CPV on feedback behavior is stronger in online shopping contexts compared to offline.
H2d. 
The positive influence of CPCSR on feedback behavior is stronger in online shopping contexts compared to offline.

3.3. Consumer Perception and Helping Behavior

Itani et al. (2019) [49] elucidates that CPV not only deepens consumers’ bond with products but also catalyzes interaction and mutual support among them. Similarly, Khawaja et al. (2021) [50] reveal that consumers are more likely to gravitate towards products from companies whose CSR activities align closely with their personal values, encouraging active participation in community support, thereby fostering a culture of collaboration among consumers.
In the contemporary digital landscape, social media platforms serve as dynamic arenas for efficient consumer communication and collaboration [51]. Through the exchange of purchase experiences and provision of advice on various online communities and social networks, consumers not only facilitate information sharing but also contribute to elevating brand and product recognition and trust. Kim et al. (2020) [45] found that an enhanced perception of value towards specific companies and products on social media significantly encourages consumer interactions within online communities. Further, Khawaja et al. (2021) [50] emphasize that consumers show a preference for sharing and recommending products of high quality from companies with a strong record of social responsibility. Companies with elevated CSR commitments are more attentive to the diverse needs of their consumers, thereby sharpening their consumer focus and management strategies. Effective management of online communities further stimulates a vibrant culture of communication and support among its members. Accordingly, we propose the following hypotheses:
Hypothesis 3 (H3).
Consumer perception positively influences helping behavior, with this influence being stronger in online shopping contexts compared to offline.
H3a. 
CPV positively influences helping behavior.
H3b. 
CPCSR positively influences helping behavior.
H3c. 
The positive influence of CPV on helping behavior is stronger in online shopping contexts compared to offline.
H3d. 
The positive influence of CPCSR on helping behavior is stronger in online shopping contexts compared to offline.

3.4. Consumer Perception and Tolerance Behavior

Research indicates that a consumer’s high perceived value of a product or service positively influences their satisfaction and loyalty towards it, thereby enhancing their tolerance for the product or service [52]. Furthermore, a company’s social responsibility initiatives are recognized as a potent factor in strengthening consumer satisfaction, loyalty, and, consequently, tolerance towards products and the company itself [53].
Within the online shopping environment, CPVs are largely informed by product descriptions, visual imagery, and reviews from other consumers. A disparity between these informational cues and the actual shopping experience can precipitately diminish consumer satisfaction and tolerance levels [54]. Regarding corporate social responsibility, while social media and digital marketing strategies enable firms to disseminate their CSR activities more effectively amongst a broad consumer base, thereby enhancing brand image [53], the online environment also renders consumers more susceptible to encountering negative information about companies. Such information can rapidly circulate online, leading to decreased consumer tolerance. Notably, the online shopping context, characterized by a lack of direct interpersonal interaction and real-time feedback, along with increased information complexity, inherently results in lower consumer tolerance for products and services compared to offline contexts [55]. Based on these insights, the following hypotheses are proposed:
Hypothesis 4 (H4). 
Consumer perception positively influences tolerance behavior, with this influence being weaker in online shopping contexts compared to offline.
H4a. 
CPV positively influences tolerance behavior.
H4b. 
CPCSR positively influences tolerance behavior.
H4c. 
The positive influence of CPV on tolerance behavior is stronger in online shopping contexts compared to offline.
H4d. 
The positive influence of CPCSR on tolerance behavior is stronger in online shopping contexts compared to offline.

4. Methodology

4.1. Sample Characteristics and Data Acquisition

The survey of this study is structured into three phases. The first phase of the study involved conducting detailed interviews with 200 Chinese consumers, half from the Douyin platform and half from local shopping malls. Each interview lasted between 10 and 15 min and was conducted over a two-week period from 10 to 23 April 2023. From these interviews, we collected roughly 167,000 characters from online consumers and 195,000 from offline shoppers, totaling about 362,000 characters. Building on the collected text data, we meticulously summarized, encoded, deduplicated, and merged the information to pinpoint the distinct differences across the four dimensions of CCB—recommendation, feedback, help, and tolerance—within online and offline shopping contexts, as detailed in Table 1.
Upon completing the initial phase, we employed a scenario experiment approach to immerse the aforementioned 200 participants into purchasing scenarios of online live-streaming shopping (Scenario A) and offline brick-and-mortar shopping (Scenario B). During the questionnaire collection from the 200 participants, we assessed their overall attitudes towards CSR to gauge their pre-existing understanding of what companies should or should not do in terms of their responsibilities. We excluded the questionnaires completed by participants who had no knowledge of CSR, ensuring that the remaining sample comprised individuals with a foundational understanding of CSR. This approach enhanced the relevance and reliability of their responses to the online and offline scenarios.
Meanwhile, to avoid the inefficiency and potential bias caused by repeated depth interviews and questionnaire surveys on the same sample, and to improve the reliability and generalization ability of the model, we chose to increase the sample size through re-sampling. From 29 to 30 April 2023, we served as staff members in the 112th anniversary celebration of our university. Following one of the lectures, we distributed paper copies of a survey questionnaire to 400 alumni who had gathered at the venue. Despite varying educational backgrounds and industries, these participants possessed a high level of awareness, strong learning abilities, and extensive knowledge, leading us to believe that they have a certain understanding and awareness of CSR.
Drawing on the methodology of Lee and Cude (2012) [56], participants were first asked to write down the name of a familiar home appliance brand before being introduced to the following premise: “Imagine the home appliance brand you just mentioned is now offering a new product purchasing experience. Below are two potential shopping scenarios you might encounter”. Subsequently, participants were presented with detailed scenario descriptions, with each scenario group receiving a distinct narrative.
Scenario A was described as follows: “You unlock your smartphone and navigate to the familiar Douyin e-commerce platform. Today, the spokesperson for your favored home appliance brand is conducting a live broadcast. During the session, the spokesperson thoroughly discusses the brand’s corporate social responsibility initiatives, such as their commitment to environmental protection, energy conservation, and community support. Following this, the latest home appliance product is unveiled, showcasing its energy efficiency, smart features, and user-friendly design. During the live interaction, you also have the opportunity to ask questions and receive immediate responses”.
Scenario B was depicted as: “You enter the physical store of the home appliance brand, and the first thing that catches your eye is a display detailing the brand’s corporate social responsibility efforts. These displays outline the brand’s endeavors in environmental protection, energy conservation, and community support. Moving forward, a sales associate warmly approaches you to demonstrate the latest home appliance products. You personally test the products, experiencing their efficiency and comfortable usability firsthand. The sales associate further explains the various functionalities of the appliances and answers all your questions”.
By simulating the consumer experience in typical online live-streaming and offline physical store shopping contexts, this approach aimed to create a realistic consumption backdrop, thereby enabling participants to more accurately reflect on their perceptions of product value, corporate social responsibility, and CCB.
In the third phase of this study, we advanced our investigation by deploying a questionnaire survey, grounded in scales validated by prior research. The process commenced with the translation of the original English questionnaire into Chinese, followed by a meticulous back-translation conducted by two bilingual experts. This bilingual scrutiny facilitated an evaluation by a panel of three experts specializing in corporate social responsibility and marketing, who rigorously assessed the back-translated questionnaire for face validity.
The survey implemented a 5-point Likert scale, allowing responses to span from “Strongly Disagree” to “Strongly Agree”. To assess Consumer Perceived Value (CPV), we adopted three items from the scales developed by Loureiro et al. (2012) [57] and Ryu et al. (2008) [58]. For evaluating Consumer Perceived Corporate Social Responsibility (CPCSR), four items from Ailawadi et al. (2014) [59] were utilized. Furthermore, CCB was measured using 12 items based on the framework provided by Yi and Gong (2013) [30], with an average of three items per sub-dimension.
Acknowledging the potential influence of demographic characteristics on behavioral variables [60], we incorporated age, gender, and educational level as covariates in our hypothesis testing. The survey participants were asked to respond to questions based on their experiences from the preceding scenario experiments. We collected 150 valid questionnaires from a pool of 200 interviewees and 196 valid questionnaires from 400 alumni who took part in the school’s anniversary event, yielding a total of 346 responses and an effective response rate of 57.7% (including 182 answers for scenario A and 164 answers for scenario B). The demographic profile of participants revealed a predominance of male respondents, approximately 62.7%, with an age distribution between 21 and 61 years (mean age M = 38.20, SD = 11.19). All respondents possessed at least an undergraduate degree, with 46% holding a graduate degree or higher, ensuring a well-educated cohort for our analysis.

4.2. Reliability, Validity, and Model Fit

Before testing the hypotheses, the reliability and validity of the measurement scales were systematically analyzed using SPSS 24. Specifically, for CPV, α = 0.875, CR = 0.849, and AVE = 0.656; for CPCSR, α = 0.859, CR = 0.816, and AVE = 0.628; for CCB, the recommendation had α = 0.832, CR = 0.824, and AVE = 0.609, feedback had α = 0.834, CR = 0.831, and AVE = 0.623, helping had α = 0.887, CR = 0.898, and AVE = 0.688, and tolerance had α = 0.801, CR = 0.757, and AVE = 0.535. It can be observed that the Cronbach α of all measures is greater than 0.80, and all the coefficients of the indicators are significant; the composite reliability (CR) is greater than 0.60, and the average variance extracted (AVE) is greater than 0.50, indicating good internal consistency [61]. Therefore, the findings suggest that the constructs exhibit good convergent validity.
As evidenced by the correlation analysis results (Table 2), significant correlations exist between the constructs within the research model, and the standardized correlation coefficients among the constructs are all less than the square root of the AVE of each variable. This indicates that the constructs demonstrate satisfactory discriminant validity in the scale. Concurrently, fit indices from confirmatory factor analysis (CFA) indicate that the measurement model fits the data well, with χ2/df = 1.837 < 3, RMSEA = 0.078, SRMR = 0.057, CFI = 0.918, GFI = 0.956.

5. Results

This study examined the relationships between CPV, CPCSR, and the quartet of sub-dimensions constituting CCB through multiple regression analysis.
As delineated in Table 3, the influence of these perceptual constructs on recommendation behavior within the realm of consumer citizenship actions emerged as paramount, boasting an explanatory power (R2) of 0.358. This revelation underscores that both CPV and CPCSR exert a significant and direct impact on recommendation behavior, the most dynamically affected dimension among the sub-dimensions of CCB.
Intriguingly, CPV’s impact coefficients consistently outstripped those of CPCSR across the board, suggesting CPV’s pivotal role in fostering CCB. This implies that the essence of products and services is crucial for enterprises, as consumer satisfaction with the product is a prerequisite for their willingness to engage in recommendation and sharing on behalf of the company.
More precisely, regarding recommendation behavior, the standardized coefficients of CPV and CPCSR for recommendation behavior stood at 0.450 (p < 0.001) and 0.231 (p < 0.01), respectively, both exhibiting significant positive effects. Thus, H1a and H1b were supported.
Regarding feedback behavior, the standardized coefficients were 0.118 (p < 0.001) for CPV and 0.182 (p < 0.05) for CPCSR, both exhibiting significant positive effects. Thus, H2a and H2b were supported.
Regarding helping behavior, the standardized coefficients were 0.294 (p < 0.01) for CPV, exhibiting a significant positive effect, whereas CPCSR’s coefficient of 0.150 (p = 0.08) did not reach significance. Thus, H3a was supported, while H3b was not supported.
Regarding tolerance behavior, the standardized coefficients were 0.339 (p < 0.001) for CPV, exhibiting a significant positive effect, while CPCSR’s coefficient of 0.080 (p = 0.35) was not significant. Thus, H4a was supported, while H4b was not supported.
This nuanced analysis sheds light on the differential and significant roles CPV and CPCSR play within the spectrum of CCB, with CPV emerging as a more critical driver in promoting consumer recommendation and engagement.
Building on this, we utilized the permutation test method [62,63,64] to conduct a difference analysis on the group coefficients for online and offline scenarios. The permutation test method was chosen for its ability to provide robust statistical inference without relying on the assumptions of normality, which is particularly beneficial given the complex nature of consumer behavior data and the potential non-normal distribution of the variables involved in the study. To verify the difference in performance between online and offline in the relationship between CPV, CPCSR, and the four sub-dimensions of CCB, the t-test formula was introduced here to compare the differences between the two:
t = b 1 − b 2 S S E 1 + S S E 2 n 1 + n 2 − 4 × 1 S S 1 + 1 S S 2
In Formula (1), where b 1 is the regression coefficient for Model 1, b 2 is the regression coefficient for Model 2, S S E 1 and S S E 2 are the sum of squared residuals for the two regression equations, respectively. n 1 and n 2 are the sample sizes for the two groups, and S S 1 and S S 2 are the sum of squared deviations of the independent variable in the two groups, respectively.
This investigation delves into the differential impacts of CPV and CPCSR on various facets of CCB, with a keen focus on recommendation, feedback, helping, and tolerance behaviors across online and offline contexts (Figure 1).
For recommendation behavior, CPV exerts a substantial and equally positive effect in both contexts, as evidenced by impact coefficients of 0.452 (p < 0.001) for online and 0.453 (p < 0.001) for offline contexts, revealing no significant variance between them (t = −0.013). This parity suggests that CPV’s capacity to enhance recommendation behavior remains consistent across virtual and physical platforms. Conversely, CPCSR’s influence on recommendation is markedly more potent online (0.367, p < 0.05) than offline (0.202, p < 0.05), with a significant disparity observed (t = 2.157*), underscoring the enhanced effectiveness of CPCSR in digital environments. Thus, H1c was not supported, while H1d was supported.
When examining feedback behavior, CPV’s positive influence is significantly amplified online (0.435, p < 0.01) compared to offline (0.289, p < 0.01) contexts (t = 1.908*), indicating a stronger digital propensity for CPV-driven feedback. CPCSR’s impact mirrors this pattern, showcasing significant positivity exclusively in online contexts (0.275, p < 0.05) and discerning a notable contrast with its offline counterpart (t = 2.314*). These findings affirm that both CPV and CPCSR more effectively stimulate feedback behavior online. Thus, H2c and H2d were supported.
In the context of helping behavior, CPV significantly promotes such actions predominantly online (0.372, p < 0.01), with a notable difference favoring the digital domain over the offline sphere (t = 1.778*). This accentuates the pronounced role of CPV in online supportiveness among consumers. However, CPCSR does not manifest a significant effect in either realm for aiding behavior, nor does it exhibit a discernible difference between contexts (t = 0.458), suggesting a uniform, albeit muted, influence of CPCSR on consumer helping across platforms. Thus, H3c was supported, while H3d was not supported.
Regarding tolerance behavior, CPV demonstrates a stronger offline enhancement (0.351, p < 0.01) compared to online (0.180, p < 0.05), with a significant inclination towards physical environments (t = −2.235*), suggesting CPV’s influence on tolerance is more pronounced offline. For CPCSR, a positive effect emerges solely in offline contexts (0.146, p < 0.05), revealing a substantial gap favoring offline over online contexts (t = −2.431*). This delineates a more substantial offline effectiveness of CPCSR in fostering consumer tolerance. Thus, H4c and H4d were supported.
These analyses illuminate the nuanced roles of CPV and CPCSR across consumer behaviors in digital versus physical retail landscapes, offering insightful implications for strategic engagement and marketing practices.

6. Discussion

6.1. The Impact of Consumer Perception on CCB

The study reveals that CPVs exert a substantial influence on the quartet of CCB: recommending, feedback, helping and tolerance.
In a departure from preliminary assumptions, CPCSRs manifest a notable effect on the behaviors of recommendation and feedback, while their impact on helping and tolerance appears to be minimal.
This results is consistent with the viewpoint of Deci and Ryan (1985) [65], which suggests that behaviors with lower psychological barriers, such as recommending and providing feedback, are more likely to be performed for immediate psychological or social rewards. The experimental data indicates that participants were more inclined to engage in behaviors such as recommending and providing feedback because these actions not only align with the expectations established by the corporation but also reflect a personal commitment to responsibility, which in turn leads to enhanced public recognition [66].
On the other hand, the less significant effect of CPCSR on helping and tolerance behaviors may be attributed to the higher level of moral reflection and altruistic intent required for these actions [67]. Our analysis of the data revealed that these behaviors are more closely tied to individual cultural and habitual factors, which are not solely influenced by corporate actions. This nuanced finding highlights the complex interplay between CSR initiatives and the varied consumer responses they elicit. Further research is needed to explore the interdependencies between cultural backgrounds, habitual behaviors, and the manifestation of CCB in different consumer segments.
It is worth mentioning that the multidimensional nature of CPCSR is pivotal in shaping consumer perceptions, encompassing a range of attributes such as energy conservation and environmental protection. Although the present study does not disaggregate these specific aspects of CSR, it acknowledges their integral role in the broader construct of corporate responsibility. Energy conservation, for instance, is often associated with cost savings for consumers, yet it is not solely defined by its environmental benefits. This nuanced understanding of CSR dimensions underscores the complexity of consumer preferences and aligns with the study’s aim to explore the general influence of CPCSR on CCB without focusing on individual components.

6.2. The Varying Impact of Online versus Offline Shopping Contexts

6.2.1. The Impact towards Recommendation Behaviors

Regarding the recommendation behaviors within online and offline contexts, the data analysis reveals that CPV significantly and positively impacts both scenarios, with no substantial difference detected between them. This uniformity might be linked to a heightened CPV among consumers and the impact of brand consistency, which together significantly drive recommendation behaviors across various contexts [68].
On the other hand, CPCSRs positively impact recommendation behaviors, with a stronger effect observed in online contexts. This could be attributed to the convenience of information sharing and the expansive functionalities of social media platforms, which facilitate easier consumer recommendations based on a company’s CSR initiatives [69]. The online environment’s ability to facilitate rapid and widespread information sharing means that when consumers perceive a company as socially responsible, their positive endorsements can reach a larger audience, thereby significantly boosting the likelihood of others engaging in similar recommendation behaviors [34]. This digital word-of-mouth effect is particularly potent as it transcends geographical boundaries and taps into the collective sentiment of online communities, making the impact of CPCSR on recommendation behaviors more pronounced in the virtual realm than in face-to-face interactions typical of offline contexts.

6.2.2. The Impact towards Feedback Behaviors

Regarding the feedback behaviors within online and offline contexts, CPV significantly impacts online feedback activities more than those in offline contexts, consistent with the initial hypothesis. Chevalier and Mayzlin (2006) [34] observed that online word of mouth significantly influences sales, particularly for experience goods. Consumers often rely on online reviews and ratings to make informed purchasing decisions for experience goods, where the pre-purchase uncertainty is high, and the value of peer opinions becomes particularly crucial in shaping their perceptions and decision-making processes.
On the other hand, CPCSRs exhibit a notable positive impact on feedback behavior exclusively in online contexts. The relative anonymity afforded by online spaces not only promotes the wider dissemination of CSR [70], but also facilitates community-driven resonance and collective action towards CSR initiatives [32]. This environment markedly encourages consumers to provide positive feedback, thereby strengthening their inclination to participate in feedback activities. In contrast, offline contexts are characterized by direct interactions between companies and consumers, entailing a myriad of complex social and psychological dynamics. Consumers may be more conservative in face-to-face scenarios, showing reluctance to explicitly express their views on CSR activities [71].

6.2.3. The Impact on Helping Behaviors

Regarding helping behaviors within online and offline contexts, the impact of CPV on these behaviors exhibits a notable difference between the two scenarios. Notably, these perceptions exert a pronounced positive effect on such behaviors in online contexts, a phenomenon not equally observed in offline contexts. This variation primarily arises from the intrinsic differences in social interaction that online and offline contexts afford [72]. Online platforms, with their expansive social networks and community resources, significantly ease the sharing and exchange of shopping experiences [73]. Particularly on digital social platforms, engaging in acts of helping, such as sharing products or posting reviews, becomes effortlessly facilitated [74]. In contrast, offline contexts, constrained by the limitations of physical space and immediate social circles, see a markedly reduced impact on helping behaviors.
On the other hand, CPCSRs exhibit no significant effect on helping behaviors in either context, suggesting that these behaviors are more influenced by individual value systems and social norms [75]. In essence, when contemplating engaging in acts of helping, consumers appear to prioritize factors related to personal interests and social connections, with CSR considerations taking a backseat. This nuanced understanding underscores the complexity of consumer behavior, highlighting the multifaceted factors that influence the propensity to help others within the consumer domain.

6.2.4. The Impact towards Tolerance Behaviors

In both online and offline contexts, CPV consistently exhibits a positive influence on tolerance behaviors, with this effect being significantly more pronounced in offline contexts than online, aligning with initial hypotheses. This heightened effect in offline settings can be attributed to the enriched customer experience that includes personal interactions and immediate product handling, which fosters a deeper emotional connection and satisfaction with the brand [76]. Such direct engagement often leads to a greater willingness among consumers to overlook minor issues, as their overall experience is shaped by a combination of tangible and intangible elements that go beyond the product itself. In contrast, the online environment, characterized by a lack of physical presence and delayed gratification, may result in consumers having lower tolerance thresholds, as the absence of direct interaction can lead to increased uncertainty and perceived risk [52].
On the other hand, the influence of CPCSR on tolerance behaviors diverges from expectations: it is significant in offline shopping contexts but not in online contexts. This discrepancy may stem from the distinct dynamics of online versus offline contexts, direct social interactions foster emotional connections and rapport, thereby amplifying the impact of CPCSR on consumer tolerance behaviors [77]. Conversely, in online shopping contexts, the inherent uncertainties and perceived transaction risks, diminish the influence of CPCSR on tolerance behaviors [78]. In such contexts, the absence of tangible social interactions and emotional bonds leads consumers to prioritize the direct value of products or services, relegating CSR considerations to a secondary position, in their examination of the relationship between corporate social performance and consumer behavior [79].

7. Conclusions, Implications and Limitations

7.1. Conclusions

This study investigates the differential tendencies of CPV and CPCSR in shaping CCB across online and offline shopping contexts. Our results reveal that CPV exhibits a significant tendency to influence all dimensions of CCB in the online setting, while its effect on the helping behavior is less pronounced in offline contexts. Meanwhile, CPCSR demonstrates a notable tendency to affect recommendation and feedback behaviors in online environments and recommendation and tolerance behaviors in offline settings. These tendencies suggest the context-dependent influence of CPV and CPCSR on CCB, which is worthy of theoretical consideration.
The tendencies observed in our study can be interpreted through established consumer behavior theories. Information search theory suggests that the online environment, with its wealth of accessible product information and user feedback, enhances the influence of CPV on consumer actions. Social exchange theory emphasizes the importance of direct interpersonal interactions in offline settings, which may strengthen the impact of CPCSR on consumer behaviors such as recommendations and tolerance.
Moreover, perceived value theory highlights the comprehensive evaluation of product value by consumers, which may vary in emphasis between online and offline environments. In online contexts, the focus may be more on functional and pricing aspects, while offline contexts may place greater importance on experiential and service quality dimensions. The technology acceptance model complements this view, indicating that the perceived usefulness and ease of use of online platforms can significantly shape consumer behaviors.
These findings not only contribute novel insights into consumer behaviors across varying shopping contexts but also provide empirical support for the application of consumer and social development theories.

7.2. Implications

7.2.1. Theoretical Implications

Our study combined consumer perceptions of both tangible (product value) and intangible (CSR efforts) elements associated with firms, analyzing them as a unified whole. This approach significantly advances beyond the scope of traditional research paradigms, which have typically isolated CPV [5,39] or CPCSR [46,48] as independent variables in the exploration of CCB. Thus, our research not only expands the boundaries of consumer perception theory but also deepens our understanding of the complexities inherent in consumer perceptions.
Our research provides a detailed analysis of CCB within both online and offline contexts. Contrary to previous studies that have viewed CCB as an overall concept [6,7] our study dissects its dimensions for individual examination. Moreover, it elaborates on the specific manifestations and nuances of these dimensions across different shopping scenarios, thereby enriching the discourse on CCB.
Methodologically, by employing case analysis, our research explores the subtleties and distinctions of CCB and their dimensions in both online and offline contexts. By designing experimental scenarios, we have further delved into the investigation, conducting a comparative analysis of CCB from a multidimensional standpoint. This methodological approach surpasses the traditional online versus offline comparative research models [25,28,29], providing a more comprehensive and in-depth understanding of the complexity of CCB. Consequently, our study offers new perspectives on and pathways toward future research in this domain.

7.2.2. Practical Implications

To foster consumer engagement and promote behaviors such as recommendation, feedback, helping, and tolerance, businesses are advised to implement a comprehensive strategy that addresses several key areas. First, enhancing the quality of products and services is crucial to elevate CPV, thereby encouraging them to make recommendations. Additionally, in the digital domain, the creation of meaningful content—such as product tutorials and updates on industry trends—is essential for strengthening a brand’s presence and influence on social media platforms. Providing exemplary customer service, swiftly addressing inquiries, and resolving issues, while simultaneously highlighting CSR initiatives, can significantly improve CPCSR, motivating them to endorse the brand further.
Moreover, reinforcing connections with consumers through regular, personalized interactions, coupled with the development of a membership system offering exclusive benefits and services to loyal customers, can enhance the feedback mechanism. Leveraging social media and online communities to cultivate a supportive, mutually beneficial corporate culture encourages consumer-to-consumer help and dialogue. Lastly, beyond merely improving product and service standards, businesses should engage in consumer education initiatives to foster a resilient mindset, aiming to reduce perceived transaction risks and uncertainties in online contexts, thereby increasing tolerance. Proactive market research to anticipate and address potential issues is also imperative.

7.3. Limitations

This study encounters several constraints. Firstly, the research scope, limited to the consumer segment purchasing household appliances, narrows its universality. Future inquiries should broaden their scope to encompass a diverse spectrum of products and services, thereby enhancing the findings’ applicability. Secondly, the study does not directly explore the O2O (online-to-offline) model, which represents an area of potential growth for future research. Investigating the integration and synchronicity of online and offline shopping experiences, including the role of O2O, could provide valuable insights into the evolving retail landscape. Thirdly, the study’s scenarios may have limited the scope of consumers’ perceived preferences for CSR by focusing on ‘energy conservation’. This specific attribute, while relevant, may not capture the full range of consumers’ attitudes towards other CSR dimensions, such as environmental protection or social welfare. Future research should consider a broader array of CSR aspects to more comprehensively understand how they individually influence consumer preferences and decision-making. Furthermore, the influence of cultural backgrounds on the study’s global relevance cannot be overlooked. Future work is warranted to undertake cross-cultural analyses and integrate a more comprehensive set of determinants. Finally, the reliance on cross-sectional survey techniques constrains our ability to delineate causality with certainty. Addressing this, subsequent research could leverage longitudinal approaches to solidify the causal evidence or employ advanced methodologies such as big data analytics and artificial intelligence to refine and deepen the analytical framework.

Author Contributions

Conceptualization, Y.D.; validation, Q.S. and J.H.; formal analysis, Y.D.; investigation, Q.S. and Y.D.; data curation, Y.D.; writing—original draft, Q.S. and Y.D.; writing—review and editing, Y.D.; funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (NSFC), (Grant No. 71972114).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions regarding privacy.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Natarajan, T.; Ramanan, D.V.; Jayapal, J. Does Pickup Service Quality Explain Buy Online Pickup In-Store Service User’s Citizenship Behavior? Moderating Role of Product Categories and Gender. TQM J. 2023. ahead-of-print. [Google Scholar] [CrossRef]
  2. Luo, X.; Tong, S.; Fang, Z.; Qu, Z. Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Mark. Sci. 2019, 38, 937–947. [Google Scholar] [CrossRef]
  3. Chan, K.W.; Gong, T.; Zhang, R.; Zhou, M. Do Employee Citizenship Behaviors Lead to Customer Citizenship Behaviors? The Roles of Dual Identification and Service Climate. J. Serv. Res. 2017, 20, 28–59. [Google Scholar] [CrossRef]
  4. Rather, R.A. Customer Experience and Engagement in Tourism Destinations: The Experiential Marketing Perspective. J. Travel Tour. Mark. 2020, 37, 15–32. [Google Scholar] [CrossRef]
  5. Kim, E.; Tang, L.R. The Role of Customer Behavior in Forming Perceived Value at Restaurants: A Multidimensional Approach. Int. J. Hosp. Manag. 2020, 87, 102–135. [Google Scholar] [CrossRef]
  6. Aljarah, A. The Nexus between Corporate Social Responsibility and Target-Based Customer Citizenship Behavior. J. Sustain. Tour. 2020, 28, 2044–2063. [Google Scholar] [CrossRef]
  7. Fatma, M.; Khan, I. Impact of CSR on Customer Citizenship Behavior: Mediating the Role of Customer Engagement. Sustainability 2023, 15, 5802. [Google Scholar] [CrossRef]
  8. Groth, M. Customers as Good Soldiers: Examining Citizenship Behaviors in Internet Service Deliveries. J. Manag. 2015, 31, 421–458. [Google Scholar] [CrossRef]
  9. Nagy, E.S.A.; Marzouk, W.G. Factors Affecting Customer Citizenship Behavior: A Model of University Students. Int. J. Mark. Stud. 2018, 10, 54–70. [Google Scholar] [CrossRef]
  10. Neumann, H.L.; Martinez, L.M.; Martinez, L.F. Sustainability Efforts in the Fast Fashion Industry: Consumer Perception, Trust and Purchase Intention. Sustain. Account. Manag. Policy J. 2020, 12, 571–590. [Google Scholar] [CrossRef]
  11. Kotler, P.; Keller, K.L. Marketing Management, 15th ed.; Pearson Education Limited: Harlow, UK, 2016. [Google Scholar]
  12. Solomon, M. Consumer Behavior: Buying, Having, and Being, 12th ed.; Pearson: Harlow, UK, 2016. [Google Scholar]
  13. Gale, B.T. Managing Customer Value; Free Press: New York, NY, USA, 1994. [Google Scholar]
  14. Woodruff, R.B. Customer Value: The Next Source for Competitive Advantage. J. Acad. Mark. Sci. 1997, 25, 139–153. [Google Scholar] [CrossRef]
  15. Ru, D.; Jantan, A.H.B. The Intervening Role of Customer Loyalty and Self-Efficacy on the Relationship Between Customer Satisfaction, Customer Trust, Customer Commitment, Customer Value and Customer Citizenship Behavior in Hospitality Industry in Guangdong, China. J. Int. Bus. Manag. 2023, 6, 1–12. [Google Scholar]
  16. Alvarado-Herrera, A.; Bigne, E.; Aldas-Manzano, J.; Curras-Perez, R. A Scale for Measuring Consumer Perceptions of Corporate Social Responsibility Following the Sustainable Development Paradigm. J. Bus. Ethics 2017, 140, 243–262. [Google Scholar] [CrossRef]
  17. Vitell, S.J. A Case for Consumer Social Responsibility (CnSR): Including a Selected Review of Consumer Ethics/Social Responsibility Research. J. Bus. Ethics 2015, 130, 767–774. [Google Scholar] [CrossRef]
  18. Öberseder, M.; Schlegelmilch, B.B.; Murphy, P.E.; Gruber, V. Consumers’ Perceptions of Corporate Social Responsibility: Scale Development and Validation. J. Bus. Ethics 2014, 124, 101–115. [Google Scholar] [CrossRef]
  19. Ghanbarpour, T.; Gustafsson, A. How Do Corporate Social Responsibility (CSR) and Innovativeness Increase Financial Gains? A Customer Perspective Analysis. J. Bus. Res. 2022, 140, 471–493. [Google Scholar] [CrossRef]
  20. Handelman, J.M.; Arnold, S.J. The Role of Marketing Actions with a Social Dimension: Appeals to the Institutional Environment. J. Mark. 1999, 63, 187–218. [Google Scholar] [CrossRef]
  21. Zhang, F.; Li, N. The Impact of CSR on the Performance of a Dual-Channel Closed-Loop Supply Chain under Two Carbon Regulatory Policies. Sustainability 2022, 14, 3021. [Google Scholar] [CrossRef]
  22. Jakubczak, A.; Gotowska, M. Green Consumerism vs. Greenwashing. Eur. Res. Stud. J. 2020, 23, 167–181. [Google Scholar] [CrossRef]
  23. Liu, L.; Feng, L.; Xu, B.; Deng, W. Operation Strategies for an Omni-channel Supply Chain: Who is Better Off Taking on the Online Channel and Offline Service? Electron. Commer. Res. Appl. 2020, 39, 100918. [Google Scholar] [CrossRef]
  24. Homburg, C.; Lauer, K.; Vomberg, A. The Multichannel Pricing Dilemma: Do Consumers Accept Higher Offline than Online Prices? Int. J. Res. Mark. 2019, 36, 597–612. [Google Scholar] [CrossRef]
  25. Ratchford, B.; Soysal, G.; Zentner, A.; Gauri, D.K. Online and Offline Retailing: What We Know and Directions for Future Research. J. Retail. 2022, 98, 152–177. [Google Scholar] [CrossRef]
  26. Wang, O.; Somogyi, S.; Charlebois, S. Food Choice in the E-commerce Era: A Comparison between Business-to-consumer (B2C), Online-to-offline (O2O) and New Retail. Br. Food J. 2020, 122, 1215–1237. [Google Scholar] [CrossRef]
  27. Siragusa, C.; Tumino, A. E-grocery: Comparing the Environmental Impacts of the Online and Offline Purchasing Processes. Int. J. Logist. Res. App. 2022, 25, 1164–1190. [Google Scholar] [CrossRef]
  28. Dai, H.; Chan, C.; Mogilner, C. People Rely Less on Consumer Reviews for Experiential than Material Purchases. J. Consum. Res. 2020, 46, 1052–1075. [Google Scholar] [CrossRef]
  29. Savila, I.D.; Wathoni, R.N.; Santoso, A.S. The Role of Multichannel Integration, Trust and Offline-to-online Customer Loyalty towards Repurchase Intention: An Empirical Study in Online-to-offline (O2O) E-commerce. Procedia Comput. Sci. 2019, 161, 859–866. [Google Scholar] [CrossRef]
  30. Yi, Y.; Gong, T. Customer Value Co-creation Behavior: Scale Development and Validation. J. Bus. Res. 2013, 66, 1279–1284. [Google Scholar] [CrossRef]
  31. Cheung, C.M.; Lee, M.K. What Drives Consumers to Spread Electronic Word of Mouth in Online Consumer-opinion Platforms. Decis. Support Syst. 2012, 53, 218–225. [Google Scholar] [CrossRef]
  32. Hennig-Thurau, T.; Gwinner, K.P.; Walsh, G.; Gremler, D.D. Electronic Word-of-mouth via Consumer-opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet? J. Interact. Mark. 2004, 18, 38–52. [Google Scholar] [CrossRef]
  33. Istanbulluoglu, D.; Leek, S.; Szmigin, I.T. Beyond Exit and Voice: Developing an Integrated Taxonomy of Consumer Complaining Behaviour. Eur. J. Mark. 2017, 51, 1109–1128. [Google Scholar] [CrossRef]
  34. Chevalier, J.A.; Mayzlin, D. The Effect of Word of Mouth on Sales: Online Book Reviews. J. Mark. Res. 2006, 43, 345–354. [Google Scholar] [CrossRef]
  35. Choi, L.; Lotz, S.L. Exploring Antecedents of Customer Citizenship Behaviors in Services. Serv. Ind. J. 2018, 38, 607–628. [Google Scholar] [CrossRef]
  36. Liao, J.; Wang, W.; Du, P.; Filieri, R. Impact of Brand Community Supportive Climates on Consumer-to-consumer Helping Behavior. J. Res. Interact. Mark. 2023, 17, 434–452. [Google Scholar] [CrossRef]
  37. Kim, S.-J.; Hyun, B.-H. Effects of Psychological Variables on the Relationship between Customer Participation Behavior and Repurchase Intention: Customer Tolerance and Relationship Commitment. Economies 2022, 10, 305. [Google Scholar] [CrossRef]
  38. Syah, T.Y.R.; Olivia, D. Enhancing Patronage Intention on Online Fashion Industry in Indonesia: The Role of Value Co-Creation, Brand Image, and E-Service Quality. Cogent Bus. Manag. 2022, 9, 2065790. [Google Scholar] [CrossRef]
  39. Karaosmanoglu, E.; Altinigne, N.; Isiksal, D.G. CSR Motivation and Customer Extra-Role Behavior: Moderation of Ethical Corporate Identity. J. Bus. Res. 2016, 69, 4161–4167. [Google Scholar] [CrossRef]
  40. De Canio, F.; Fuentes-Blasco, M.; Martinelli, E. Extrinsic Motivations Behind Mobile Shopping: What Drives Regular and Occasional Shoppers? Int. J. Retail. Distrib. Manag. 2022, 50, 962–980. [Google Scholar] [CrossRef]
  41. Xu, J.; Du, H.S.; Shen, K.N.; Zhang, D. How Gamification Drives Consumer Citizenship Behaviour: The Role of Perceived Gamification Affordances. Int. J. Inf. Manag. 2022, 64, 102127. [Google Scholar] [CrossRef]
  42. Oke, A.; McKenzie, K.; Osobajo, O.; Lawani, A. Effects of Millennials Willingness to Pay on Buying Behaviour at Ethical and Socially Responsible Restaurants: Serial Mediation Analysis. Int. J. Hosp. Manag. 2023, 113, 103507. [Google Scholar] [CrossRef]
  43. Patterson, P.G.; Spreng, R.A. Modelling the Relationship between Perceived Value, Satisfaction, and Repurchase Intentions in a Business-to-Business Service Context: An Empirical Examination. Int. J. Serv. Ind. Manag. 1997, 8, 414–434. [Google Scholar] [CrossRef]
  44. Li, M.; Mao, J. Hedonic or Utilitarian? Exploring the Impact of Communication Style Alignment on User’s Perception of Virtual Health Advisory Services. J. Interact. Mark. 2015, 35, 229–243. [Google Scholar] [CrossRef]
  45. Kim, M.; Yin, X.; Lee, G. The Effect of CSR on Corporate Image, Customer Citizenship Behaviors, and Customers’ Long-Term Relationship Orientation. Int. J. Hosp. Manag. 2020, 88, 102–120. [Google Scholar] [CrossRef]
  46. Yu, W.; Han, X.; Ding, L.; He, M. Organic Food Corporate Image and Customer Co-developing Behavior: The Mediating Role of Consumer Trust and Purchase Intention. J. Retail. Consum. Serv. 2021, 59, 102377. [Google Scholar] [CrossRef]
  47. Ebrahimi, P.; Khajeheian, D.; Soleimani, M.; Gholampour, A.; Fekete-Farkas, M. User Engagement in Social Network Platforms: What Key Strategic Factors Determine Online Consumer Purchase Behaviour? Econ. Res.-Ekon. Istraživanja 2023, 36, 2106264. [Google Scholar] [CrossRef]
  48. Men, L.R.; Tsai, W.H.S. Beyond liking or Following: Understanding Public Engagement on Social Networking Sites in China. Public Relat. Rev. 2013, 39, 13–22. [Google Scholar] [CrossRef]
  49. Itani, O.S.; Kassar, A.N.; Loureiro, S.M.C. Value Get, Value Give: The Relationships Among Perceived Value, Relationship Quality, Customer Engagement, and Value Consciousness. Int. J. Hosp. Manag. 2019, 80, 78–90. [Google Scholar] [CrossRef]
  50. Khawaja, L.; Ali, A.; Mostapha, N. The Mediating Effect of Customer Satisfaction in Relationship with Service Quality, Corporate Social Responsibility, Perceived Quality and Brand Loyalty. Manag. Sci. Lett. 2021, 11, 763–772. [Google Scholar] [CrossRef]
  51. Godey, B.; Manthiou, A.; Pederzoli, D.; Rokka, J.; Aiello, G.; Donvito, R.; Singh, R. Social Media Marketing Efforts of Luxury Brands: Influence on Brand Equity and Consumer Behavior. J. Bus. Res. 2016, 69, 5833–5841. [Google Scholar] [CrossRef]
  52. Wu, L. Satisfaction, Inertia, and Customer Loyalty in the Varying Levels of the Zone of Tolerance and Alternative Attractiveness. J. Serv. Mark. 2011, 25, 310–322. [Google Scholar] [CrossRef]
  53. Connor, P.O.; Assaker, G. Examining the Antecedents and Effects of Hotel Corporate Reputation on Customers’ Loyalty and Citizenship Behavior: An Integrated Framework. J. Hosp. Mark. Manag. 2022, 31, 640–661. [Google Scholar]
  54. Wang, D.; Weisstein, F.L.; Duan, S.; Choi, P. Impact of Ambivalent Attitudes on Green Purchase Intentions: The Role of Negative Moods. Int. J. Consum. Stud. 2022, 46, 182–199. [Google Scholar] [CrossRef]
  55. Filho, E.P.; Añez, M.E.M. Why Are You So Tolerant? Towards the Relationship between Consumer Expectations and Level of Involvement. J. Retail. Consum. Serv. 2021, 60, 102467. [Google Scholar] [CrossRef]
  56. Lee, S.; Cude, B.J. Consumer Complaint Channel Choice in Online and Offline Purchases. Int. J. Consum. Stud. 2012, 36, 90–96. [Google Scholar] [CrossRef]
  57. Loureiro, S.M.; Sardinha, I.M.D.; Reijnders, L. The Effect of Corporate Social Responsibility on Consumer Satisfaction and Perceived Value: The Case of the Automobile Industry Sector in Portugal. J. Clean. Prod. 2012, 37, 172–178. [Google Scholar] [CrossRef]
  58. Ryu, K.; Han, H.; Kim, T.H. The Relationships among Overall Quick-casual Restaurant Image, Perceived Value, Customer Satisfaction, and Behavioral Intentions. Int. J. Hosp. Manag. 2008, 27, 459–469. [Google Scholar] [CrossRef]
  59. Ailawadi, K.L.; Neslin, S.A.; Luan, Y.J.; Taylor, G.A. Does Retailer CSR Enhance Behavioral Loyalty? A Case for Benefit Segmentation. Int. J. Res. Mark. 2014, 31, 156–167. [Google Scholar] [CrossRef]
  60. Hur, W.M.; Kim, H.; Kim, H.K. Does Customer Engagement in Corporate Social Responsibility Initiatives Lead to Customer Citizenship Behaviour? The Mediating Roles of Customer-Company Identification and Affective Commitment. Corp. Soc. Responsib. Environ. Manag. 2018, 25, 1258–1269. [Google Scholar] [CrossRef]
  61. Fornell, C.; Larcker, D.F. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  62. Fisher, R.A. The Design of Experiments; Oliver & Boyd: Edinburgh, UK, 1935. [Google Scholar]
  63. Pitman, E.J.G. Significance Tests which may be Applied to Samples from Any Populations II: The Correlation Coefficient. J. R. Stat. Soc. Ser. B 1937, 4, 225–232. [Google Scholar] [CrossRef]
  64. Pitman, E.J.G. Significance Tests which may be Applied to Samples from Any Populations III: The Analysis of Variance Test. Biometrika 1938, 29, 322–335. [Google Scholar]
  65. Deci, E.L.; Ryan, R.M. The General Causality Orientations Scale: Self-determination in Personality. J. Res. Personal. 1985, 19, 109–134. [Google Scholar] [CrossRef]
  66. Bagozzi, R.P. On the Concept of Intentional Social Action in Consumer Behavior. J. Consum. Res. 2000, 27, 388–396. [Google Scholar] [CrossRef]
  67. Batson, C.D.; Shaw, L.L. Evidence for Altruism: Toward a Pluralism of Prosocial Motives. Psychol. Inq. 1991, 2, 107–122. [Google Scholar] [CrossRef]
  68. Keller, K.L. Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. J. Mark 1993, 57, 1–22. [Google Scholar] [CrossRef]
  69. Sen, S.; Bhattacharya, C.B. Does Doing Good Always Lead to Doing Better? Consumer Reactions to Corporate Social Responsibility. J. Mark. Res. 2001, 38, 225–243. [Google Scholar] [CrossRef]
  70. Godes, D.; Mayzlin, D. Using Online Conversations to Study Word-of-Mouth Communication. Mark. Sci. 2004, 23, 545–560. [Google Scholar] [CrossRef]
  71. Dellaert, B.G.C.; Arentze, T.A.; Timmermans, H.J. Shopping Context and Consumers’ Mental Representation of Complex Shopping Trip Decision Problems. J. Retail. 2008, 84, 219–232. [Google Scholar] [CrossRef]
  72. Putnam, R.D. Bowling Alone: The Collapse and Revival of American Community; Simon & Schuster: New York, NY, USA, 2000. [Google Scholar]
  73. Bagozzi, R.P.; Lee, K.-H. Multiple Routes for Social Influence: The Role of Compliance, Internalization, and Social Identity. Soc. Psychol. Quart. 2002, 65, 226–247. [Google Scholar] [CrossRef]
  74. Hamari, J.; Lehdonvirta, V. Game Design as Marketing: How Game Mechanics Create Demand. Int. J. Bus. Sci. Appl. Manag. 2010, 3, 34–49. [Google Scholar]
  75. Smith, M.A.; Fischer, E.; Yongjian, C. How Does Brand-related User-generated Content Differ Across YouTube, Facebook, and Twitter? J. Interact. Mark. 2012, 26, 102–113. [Google Scholar] [CrossRef]
  76. Lemon, K.N.; Verhoef, P.C. Understanding Customer Experience throughout the Customer Journey. J. Mark. 2016, 80, 69–96. [Google Scholar] [CrossRef]
  77. Susskind, A.M.; Kacmar, K.M. Customer Loyalty During a Service Failure and Recovery: The Role of Affect and Attribution. J. Bus. Psychol. 2009, 24, 67–81. [Google Scholar]
  78. White, K.; Dahl, D.W. To Be Pragmatic or Restrained? The Impact of Inclination and Opportunity on the Use of Hedonic and Utilitarian Strategies in Online Shopping. J. Retail. 2007, 83, 155–172. [Google Scholar]
  79. Sen, S.; Bhattacharya, C.B. Does It Pay to be Good? A Meta-analysis and Examination of the Relationship between Corporate Social Performance and Financial Performance. J. Mark. 2001, 65, 56–66. [Google Scholar]
Sustainability 16 02968 g001
Figure 1. Research Model Diagram (Online vs. Offline). Notes: Obs (Online) = 182, Obs (Offline) = 164; The values within the parentheses are t-values, and the asterisk (“*”) in the superscript indicates the level of significance for the differences in path coefficients between different groups (online and offline shopping scenarios) in the multi-group analysis; * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 1. Research Model Diagram (Online vs. Offline). Notes: Obs (Online) = 182, Obs (Offline) = 164; The values within the parentheses are t-values, and the asterisk (“*”) in the superscript indicates the level of significance for the differences in path coefficients between different groups (online and offline shopping scenarios) in the multi-group analysis; * p < 0.05, ** p < 0.01, *** p < 0.001.
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Table 1. Dimensions and manifestations of CCB.
Table 1. Dimensions and manifestations of CCB.
DimensionsManifestations
OnlineOffline
Recommendation
  • Use social media, forums, blogs, and other online platforms to share and recommend products or services.
  • Use online rating systems and scoring to express recommendation intentions.
  • Create and share content related to products/services.
  • Recommend through word-of-mouth and face-to-face sharing.
  • Possibly recommend products at offline events or social occasions.
  • Share using physical flyers, cards, etc.
Feedback
  • Provide comments, suggestions, or evaluations online.
  • Participate in online surveys or provide feedback on social media.
  • Provide feedback using email or customer service systems.
  • Provide feedback face-to-face to sales or service staff.
  • Fill out offline feedback forms or provide feedback by phone.
Helping
  • Answer questions and provide help through online communities or forums.
  • Share experiences and tutorials to assist other consumers.
  • Participate in brand communities, user groups, etc., to provide suggestions for others.
  • Assist or seek help in person at stores or service centers.
  • Provide help through face-to-face interactions.
Tolerance
  • Adopt a tolerant attitude when encountering online shopping or service issues.
  • Demonstrate understanding and tolerance for technical failures or other issues on online platforms.
  • Demonstrate understanding and tolerance for technical failures or other issues on online platforms.
  • Wait moderately for online service responses or solutions.
  • Show tolerance and understanding when facing problems in physical stores.
  • Adopt an inclusive attitude towards issues with offline services or products.
  • Keep an open mind towards waiting and minor errors.
Table 2. Correlation Analysis.
Table 2. Correlation Analysis.
MS.D.1.2.3.4.5.6.
1. CPV3.8620.680(0.810)
2. CPCSR3.7650.7030.628 **(0.792)
3. Recommendation3.3670.8990.565 **0.488 **(0.780)
4. Feedback3.3130.9670.427 **0.373 **0.629 **(0.789)
5. Helping3.4060.9440.353 **0.304 **0.563 **0.738 **(0.829)
6. Tolerance3.1870.8240.382 **0.286 **0.317 **0.324 **0.374 **(0.731)
Notes: Obs (Overall) = 346; ** p < 0.01; The square root of AVE is inside the diagonal brackets.
Table 3. Consumer perception on the influence of various dimensions of CCB.
Table 3. Consumer perception on the influence of various dimensions of CCB.
ModelIndependent VariableExplanatory VariableCoefficientst-ValueVIFF-ValueR2
BetaS.D.
1RecommendationCPV0.4500.0986.054 ***1.67937.1920.358
CPCSR0.2310.0953.132 **1.657
2FeedbackCPV0.3380.1184.081 ***1.67917.3820.201
CPCSR0.1820.1132.206 *1.657
3HelpingCPV0.2940.1193.431 **1.67912.2840.148
CPCSR0.1500.1141.7591.657
4ToleranceCPV0.3390.1043.939 ***1.67911.3540.137
CPCSR0.0800.1000.9381.657
Notes: Obs (Overall) = 346; * p < 0.05, ** p < 0.01, *** p < 0.001.
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MDPI and ACS Style

Shen, Q.; Du, Y.; Huang, J. Consumer Citizenship Behavior in Online/Offline Shopping Contexts: Differential Impact of Consumer Perceived Value and Perceived Corporate Social Responsibility. Sustainability 2024, 16, 2968. https://doi.org/10.3390/su16072968

AMA Style

Shen Q, Du Y, Huang J. Consumer Citizenship Behavior in Online/Offline Shopping Contexts: Differential Impact of Consumer Perceived Value and Perceived Corporate Social Responsibility. Sustainability. 2024; 16(7):2968. https://doi.org/10.3390/su16072968

Chicago/Turabian Style

Shen, Qiulian, Yuxuan Du, and Jingxian Huang. 2024. "Consumer Citizenship Behavior in Online/Offline Shopping Contexts: Differential Impact of Consumer Perceived Value and Perceived Corporate Social Responsibility" Sustainability 16, no. 7: 2968. https://doi.org/10.3390/su16072968

APA Style

Shen, Q., Du, Y., & Huang, J. (2024). Consumer Citizenship Behavior in Online/Offline Shopping Contexts: Differential Impact of Consumer Perceived Value and Perceived Corporate Social Responsibility. Sustainability, 16(7), 2968. https://doi.org/10.3390/su16072968

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