물리적 매장이 시장에서 살아남는 방법: 소비자의 온라인 채널에서 오프라인 채널로의 전환행동에 관한 연구

How do Physical Stores Survive in the Market: An Investigation into Consumer Switching Behavior from the Online to the Offline Channel

Abstract

온라인 판매가 무서운 성장세를 보여주고 있음에도 불구하고, 소비자 채널 전환행동 영역에서 오프라인 밴드왜건이 여전히 많은 주목을 받고 있다. 소비자의 오프라인에서 온라인 소매업자로의 전환행동을 탐색하는 기존연구와 달리, 본 연구는 push-pull-mooring 모델을 기반으로 소비자가 온라인에서 오프라인 채널로 전환하는 이유와 시점에 중점을 두어 살펴본다. 따라서, 본 연구는 구조방정식 모델과 SPSS를 이용하여 설립된 연구가설을 검증하였다. 그 결과는 예상대로 push 요인(지각된 위험과 불만족)과 pull 요인(대안매력과 지각된 주인의식)이 모두 소비자의 온라인에서 오프라인 채널로의 전환의도에 긍정적 영향을 미치는 것으로 나타났다. 또한, push 요인과 전환비용간의 상호작용 및 pull 요인과 주관적 규범간의 상호작용을 제외하고, 모든 push 요인과 mooring 요인(전환비용, 다양성 추구 성향과 주관적 규범)간의 상호작용 및 pull 요인과 mooring 요인간의 상호작용이 검증되었다. 마지막으로 시사점과 한계점을 더불어 제시하였다.

Despite an impressive growth of online sales, the bricks-and-mortar bandwagon still remain high-profile in the realm of consumer channel switching behavior. Different from the existing research exploring the consumer switching behavior from the offline to the online retailer, this study is an effort to investigate why and when do consumers switch from the online to the offline channel by applying the push-pull-mooring framework. Thus, structural equation modeling and SPSS were used to test the established hypotheses. The results, as expected, show that both push factors (i.e., perceived risk and dissatisfaction) and pull factors (alternative attractiveness and perceived ownership) are positively related to a consumer's intention to switch from the online to the offline channel. Moreover, all of expected interactions between push factors and mooring factors (i.e., switching costs, variety seeking, and subjective norms), and between pull factors and mooring factors are supported, except for the interactions between push factors and switching costs as well as between pull factors and subjective norms. Finally, implications and limitations are discussed.

I. Introduction

With the flourishing advances of the internet and information technologies, a dramatic increase in online sales has been witnessing over the last decade. Consistent with the report presented by the U.S. Commerce Department, a significant growth of online sales in U.S. has been pinpointed, and the ratio of e-commerce sales to total sales grew from 6.3% in 2011 to 11% in 2016[1]. Such a phenomenon illuminates the challenges faced by traditional retailing industry in terms of cultivating consumers’ loyalty and maintaining their retention. In this vein, to survive in today’s marketplace, offline retailers should incorporate more specific considerations into their current marketing activities, for instance, the customization for targeted segments, the choice of product categories, and the marketing strategies establishment[1][2].

Despite the prevalence of e-commerce, some particular phenomenon has still highlighted the necessity of opening bricks-and-mortar stores in a multichannel environment. It is reported that a large volume of companies have jumped on the bricks-and-mortar bandwagon, such as Dell Computer, City Sports, Performance Bicycle, and Soft Surroundings[3]. Particularly, as one of the largest e-commerce platforms all over the world, Amazon is preparing for its first offline store[3]. Furthermore, despite a wide variety of advantages associated with online retail sales, such as convenience, shared shopping experience, probably a lower price, and rich information[4], consumers might still place a greater value on the option of traditional offline shopping. This may be due to that consumers encourage a greater reliance on a series of physical assets when making a purchasing decision, such as the possibility to touch, smell, or fit [1] and the personalized customization service[2]. In other words, consumers’ assessment of products is more feasible in traditional physical stores, rather than in digital platforms, which may discourage them from online shopping.

Given the above discussions, it seems that understanding why and when do consumers switch from the online to the offline channel is necessary. However, contemporary discourses remain equivocal on the consumer switching behavior from the online to the offline retailer. Responding to these deficiencies in the existing literature, this study develops a comprehensive model by adopting the push-pull-mooring (PPM) framework to explore the consumer switching behavior from the online to the offline channel. Accordingly, several research objectives are proposed. First, based on the previous research, this study is expected to identify the presumable push, pull, and mooring factors shaping the consumer switching behavior from the online to the offline channel. Second, drawing from the PPM paradigm, this study aims to develop a research model to investigate the consumer switching behavior from the online to the offline channel. Finally, this study attempts to highlight the importance of opening physical stores in a multichannel environment and draw implications for both online and offline retailers. Additionally, this study proceeds as follows: we start by reviewing the extant literature and establishing a conceptual framework; we subsequently elaborate on the hypotheses testing results and relevant statistics; and finally, we discuss the implications and limitations.

Ⅱ. Literature Review and Hypothesis Development

1. The Push-Pull-Mooring Framework

In human migration literature, the push-pull model is extensively considered as a vital theoretical lens for exploring individuals’ migration from an original location to a certain destination for a specific period of time[5]. In accordance with the push-pull paradigm of migration, the push factors are usually regarded as the factors at the original place that compel (push) an individual to leave, which have a negative influence on his/her persistent retention; In contrast, the pull factors refer to the attributes of a specific destination that draw (pull) a prospective individual toward it, which have a positive effect on his/her immediate migration[5][6]. Nonetheless, some other researchers point out that individuals may still not make a migration decision even when push and pull factors are strong enough[7], which is due to that personality traits or contextual constraints may force them to preserve a preexisting state[8]. In recognition of this, the intervening obstacles (i.e., mooring factors) relative to psychological, social, and cultural contexts are integrated into the push-pull model to provide more comprehensive insights into the migration phenomenon[5][9]. The mooring factors are characterized as the supplementary factors that motivate or inhibit individuals’ actual migration decisions, which are usually associated with cultural, economic, and spatial issues[6][10].

The push-pull-mooring (PPM) framework was originally proposed to explain human migration behavior[7]. However, more recently, the PPM framework has been adopted in various academic settings, such as consumer switching behavior across the multiple channel and consumer post-adoption behavior in the domain of information system[8][11]. In mobile instant messaging (MIM) context, reference[6] identified two push factors (i.e., fatigue and dissatisfaction), two pull factors (i.e., alternative attractiveness and subjective norms), and three mooring factors (i.e., habit, affective commitment, and switching costs), respectively. Prior research findings also confirmed the positive association between several factors (i.e., information searching behavior, perceived value, and attractiveness) and consumers’ intentions to switch from the offline to the online channel[9]. Additionally, the significant moderating effects of both self-efficacy and switching costs on the relationship among push factors, pull factors, and switching intentions were empirically verified[9]. Despite some investigation into the consumer switching behavior from the physical stores to the online retailers, yet, there is a dearth of scholarly research on the consumer switching behavior from the online to the offline channel. Accordingly, in the current study, the PPM framework is applied as the primary theoretical underpinning to establish a research model and explore several factors that may influence consumer switching behaviors from the online to the offline channel.

2. Push Factors

Consistent with the current research stream, the push factors are defined as the negative factors that oblige individuals away from an original service provider[6]. From a relationship marketing perspective, the existing literature has encouraged greater focus on consumer switching behaviors and has identified a tremendous range of push factors that may influence consumers’ intentions to switch from one service provider to another, including perceived risk, perceived satisfaction, perceived quality, and showrooming effects[5][8]. Among these factors, there is a considerable overlap between the concept of perceived risk and the notion of perceived quality, therefore, the current study conceptually excludes the construct of perceived quality. Moreover, the showrooming effect is considered as a primary construct to investigate the switching behavior from the offline to the online channel, which is apparent inconformity with the context of this study and is thus eliminated. Given the above discussion, we propose two push factors: perceived risk and perceived dissatisfaction.

2.1 Perceived Risk

Parallel to previous studies, perceived risk refers to individuals’ perceptions of the uncertainty and the magnitude of potentially undesirable consequences when engaging with service providers[12][13]. Perceived risk is conceptually disaggregated into three sub-concepts: financial risk, performance risk, and psychological risk[14]. Financial risk, here, refers to the possible monetary loss that consumers may experience in an actual transaction[14]. Similarly, performance risk is seen as the uncertainty on whether the purchased goods can work properly and lastingly[15]. More specifically, performance risk involves several aspects of the possible undesirable consequences, including product malfunctioning, inadequate quality, and incorrect performance[15]. Finally, psychological risk is characterized as the possibility that purchased products are detrimental to individuals’ health or that their perceptions of service providers cannot meet their expectations[14].

Perceived risk has been considered as a predominant factor that may diminish consumers’ intentions for online shopping by numerous marketing scholars. In line with extant studies, consumers generally perceive online purchasing behaviors as being riskier than offline shopping behaviors, which may refrain themselves from purchasing merchandise from an online retailer[16]. By the same token, the theory of reasonable action (TRA) highlights the negative association between individuals’ risk perceptions and their willingness to purchase goods from the online channel[13]. Furthermore, prior research findings indicate that directly seeing or touching a product is conducive to yielding a favorable evaluation of the product and reducing the perceived risk or uncertainty about it [17]. A higher level of perceived risk of online shopping may augment the transaction costs; this, in turn, leads to a diminished willingness to purchase products from online retailers[17]. In other words, the perceived risk of online purchases is considered as a dominant push factor causing a reduce in the frequence of consumer online purchase behaviors, which further compels consumers away from the online channel. On the strength of the above arguments, this study postulates that an individual’s perception of risk has a positive effect on his/her switching behaviors from the online to the offline channel. Accordingly, the following hypothesis is advanced:

H1a. Perceived risk of online channel is positively associated with intentions to switch from the online to the offline channel.

2.2 Perceived Dissatisfaction

As a core concept in the PPM framework, perceived dissatisfaction is depicted as an individual’s subjectively negative attitude towards and adverse evaluation of overall prior experience associated with a certain service provider[18][19]. Videlicet, in terms of the comparison between expected and actual performance, when an individual’s perceptions of the performance of a chosen retailer meet his/her expectations, satisfaction occurs, otherwise, dissatisfaction is confirmed[20]. In accordance with existing literature, a higher level of dissatisfaction is negatively related to repurchase intentions, thus, yielding higher intentions to switch[5]. From a human migration perspective, an individual’s perception of dissatisfaction is viewed as a pivotal factor to capture his/her psychological states to switch from the current place to another location [6]. Furthermore, the previous research findings reveal that consumers are more likely to develop their loyalty towards a specific service provider when they maintain a high level of satisfaction with the certain retailer[18]. Conversely, when consumers are dissatisfied with the incumbent shopping channel, they will be more likely to switch to another shopping channel. Informed by the aforementioned statements, this study assumes the negative relationship between perceived satisfaction of the current service provider and switching intentions and proposes the following hypothesis:

H1b. Perceived dissatisfaction of online channel is positively associated with intentions to switch from the online to the offline channel.

3. Pull Factors

In the present study, the pull factors refer to positive characteristics of alternatives that draw individuals to a specific service provider[6]. Parallel to prior studies, we propose two pull factors attracting prospective individuals from online retailers to physical stores: alternative attractiveness and perceived ownership. In the PPM paradigm, alternative attractiveness is extensively used as a decisive factor at the destination that pulls individuals to this destination, which is congruent with the conceptualization in the channel switching literature[19]. Additionally, more recently, marketing scholars have paid substantial attention to the role of psychological ownership on both consumers’ repurchase intentions and their retention of a certain retailer[21][22], therefore, the construct of perceived ownership is incorporated into the current research stream.

3.1 Alternative Attractiveness

Attributing to the attractiveness of alternatives, consumers are prone to maintain a relationship with a specific service provider even if they are less dissatisfied with this service provider[23]. Alternative attractiveness, here, is conceptualized as the consumer’s estimate of viable satisfactory products or services are available in a certain shopping channel[19][23]. Consumers’ assessment of alternative attractiveness may be shaped by heterogeneous attributes, such as competencies, word of mouth, and reputation[24]. The positive association between alternative attractiveness of competing service providers and consumers’ intentions to switch has been confirmed in various academic settings. In light of reference [19], a lack of attractive alternative offerings of competing service providers may lead to an amplified retention of the incumbent service provider and a diminished intention to switch. Following the same line of reasoning, reference [8] suggests that perceiving few viable alternatives is effective in facilitating consumers to develop loyalty towards the current service provider, rather than motivating them to switch to other service providers. Moreover, the existing empirical evidence indicates that the more the viable alternatives that consumers perceive, the higher the likelihood of their switching to other service providers [5]. Based on these transcriptions, the following hypothesis is derived:

H2a: Alternative attractiveness of offline channel is positively associated with intentions to switch from the online to the offline channel.

3.2 Perceived Ownership

As a valuable construct explaining consumer decision-making behavior, perceived ownership, or psychological ownership, is conceptualized as a state in which individuals feel connected to a target and further perceive that this target is theirs[21][25]. An individual’s sense of possession towards a target (i.e., perceived ownership) is considered as a material or immaterial ownership, which is related yet distinct from legal ownership, attachment, and identification[22]. The psychological ownership theory denotes that individuals can develop a sense of ownership through any of three routes: taking control of a target (i.e., touching or using a product), investing themselves in a target (i.e., spending time or energy in a target), and establishing a intimate relationship with a target (i.e., being familiar with a target in various ways)[21][25]. Drawing upon previous studies, consumers can garner detailed impressions of a product when touching it in a physical store, which may further lead to a feeling of ownership[26]. Furthermore, a sense of ownership is conducive to promoting consumers’ intentions for and impulse buying of the product in a brick-and-mortar store; this, in turn, leads to a switch from the online retailer to the offline store[26]. Above all, this study ultimately chooses perceived ownership as a core pull factor, and hypothetically, perceived ownership has a positive effect on consumers’ switching intentions. To this end, the following hypothesis is put forward:

H2b: Perceived ownership of offline channel is positively associated with intentions to switch from the online to the offline channel.

4. Mooring Factors

In this research stream, the mooring factors refer to an individual’s cost-benefit analysis of his/her switching behaviors, according to the economic, psychological, and interpersonal contexts[8]. The mooring factors, here, are posited to moderate the relationships between push factors and switching intentions as well as between pull factors and switching intentions. Previous research on consumers’ channel switching indicated that the constructs of switching costs and variety seeking were constantly investigated in the literature [5][19]. More recently, more and more empirical evidence on the influence of subjective norms on consumer switching behavior arisen in both the information system and marketing sectors [6][27]. For this, the current study proposes three mooring factors: switching costs, variety seeking, and subjective norms.

4.1 Switching Costs

As a focal mooring factor in the PPM framework, switching costs have been extensively explored in switching behavior research, and are defined as the perceived sacrifices or penalties that are incurred in the process of changing service providers, including learning costs, evaluation costs, transaction costs, and so forth[9][19]. Broadly speaking, switching costs are conceptually classified into three dimensions: financial switching costs, procedural switching costs, and relational switching costs[28]. Financial switching costs refer to the perceived monetary costs when an individual switching from a channel to another [9]. Procedural switching costs are characterized as the perceived time and effort demanded for the consumer switching behavior [28]. Moreover, relational switching costs refer to an individual’s perceptions of psychological or emotional discomfort associated with changing service providers[19]. Considerable existing research shows that a consumer’s perceptions of switching costs are effective in encouraging the switching barriers[9], and developing his/her loyalty towards the incumbent service provider[5]; this, in turn, leads to the diminished switching tendencies [19]. Rather, the lower the switching costs that consumer perceive, the higher the likelihood of their engagement with another service provider [5][29]. Therefore, the following hypotheses are proposed on grounds of the above discussions:

H3a: Switching costs moderate the relationships between push factors and switching intentions.

H4a: Switching costs moderate the relationships between pull factors and switching intentions.

4.2 Variety Seeking

By reference to the consumer varied behavior literature, the term variety seeking is defined as an individual’s predisposition to pursue variation or diversity in terms of choosing goods, services, or providers of these[30]. The underlying mechanism of consumers’ variety seeking behaviors is rooted in their beliefs that varied choices may lead to higher utility of the end result[30]. Consistent with existing studies, variety seeking is conceptually dichotomized into two dimensions: direct variety seeking and derived variety seeking[31]. Direct variety seeking is usually motivated by a series of consumer’s intrinsic and spontaneous tendencies, such as the quest for social distinctiveness and the desire for novel things [31]. On the contrary, consumers’ derived variety seeking behaviors, in general, are promoted by their extrinsic and rational considerations which appear to be aimed at maximizing the consumption utility [30]. In addition, the extant research reveals that consumers’ intentions to switch to another service provider are positively associated with a high level of their disposition to seek variety in the choice of service providers [32]. As such, consumers with a low level of variety seeking are more likely to exhibit high levels of loyalty and retention, and exert a positive effect on marketing practitioners’ efforts for the service quality improvement[33]. As we elaborated before, the following hypotheses are established:

H3b: Variety seeking moderates the relationships between push factors and switching intentions.

H4b: Variety seeking moderates the relationships between pull factors and switching intentions.

4.3 Subjective Norms

From a interpersonal relationship perspective, subjective norms are conceptualized as the pressures from important referent others (e.g., family members or close friends) an individual perceives when deciding whether to engage in a particular behavior[34]. Based on the psychological research findings, an individual’s perceptions of subjective norms may be induced by two different routes: first, individuals are prone to view the opinions of unanimous majority as being more accurate; and second, individuals strive to be accepted by significant others through aligning their behaviors with the expectations of referent others[27]. Moreover, individuals’ perceptions of subjective norms have a significant effect on their decision-making behaviors, which is due to that individuals attempt to interact with important relatives through complying with these people’ behaviors[6]. In other words, a high level of perceived subjective norms is effective in encouraging individuals to follow the opinions of referent others, and even switch to another service provider[27]. By the same token, from the perspective of the attitude-behavior relationship, an individual’s tendency to conform to the opinions of important others may lessen the influence of attitudes on behaviors, and further draws themselves away from the incumbent service [35]. In sum, this study postulates that an individual’s perceptions of subjective norms will significantly motivate or impede his/her switching behaviors. Accordingly, the following hypotheses are advanced and the conceptual framework of this study is illustrated in [Figure 1]:

H3c: Subjective norms moderate the relationships between push factors and switching intentions.

H4c: Subjective norms moderate the relationships between pull factors and switching intentions.

Fig. 1. Conceptual Framework

Ⅲ. Empirical Validation

1. Data Collection and Samples

We conducted a web-based survey in October, 2021, which was created by Google Forms and was distributed on the platform of Amazon Mechanical Turk (Amazon MTurk). A total of 350 questionnaires were distributed and 345 responses were finally collected, of these, 13 incomplete responses were eliminated prior to the data analysis stage. Therefore, 332 significant responses are used as the final data sample of the current study, including 161 (48.5%) male respondents and 171 (51.5%) female respondents. In terms of the age of respondents, the majority of respondents are between 18 to 49 years old, which accounts for 294 (88.5%). Moreover, 301 (91.6%) respondents have achieved a bachelor’s degree, of these, 85 (25.6%) respondents have received a master’s degree. Regarding the monthly income, over 75.6% respondents show a monthly income level between 1, 000 to 5, 000 U.S. dollars. Finally, the results indicate that 212 (63.8%) respondents are from western cultures, and 120 (36.1%) respondents are from eastern cultures. [Table 1] describes more detailed demographic characteristics of respondents.

Table 1. Demographic Characteristics

Note: N = 332

2. Measures

The perceived risk scale is modified from reference[36], including “I am concerned that the product purchase from an online channel may not suit me”, and so forth. The 4-item scale of perceived dissatisfaction is adapted from reference[37], for instance, “I sometimes feel disappointed about my shopping experience using the online channel”, and so forth. The measurements of alternative attractive are modified from reference[5], for example, “I think the offline channel would be much fairer than the online channel is”, and so forth. The scale of perceived ownership is revised from reference[26], including “I sometimes feel like I own a product after trying this product in an offline store”, and so forth. The measure items of switching intentions are modified from reference[9], for instance, “I expect to switch from an online channel to offline channel to handle my future shopping needs”, and so forth. In addition, the measure items of switching costs are revised from reference[38], for example, “I may lose some benefits if I switch from an online channel to offline channel”, and so forth. The variety seeking scale is adapted from reference [5], including “I am willing to try out new and different service providers”, and so forth. The measurements of subjective norms are revised from reference[5], for instance, “Most people who are important to me would like the practice of searching information in an online channel but purchasing in an offline channel, ” and so forth. A summary of measurement items appears in [Table 2]. Finally, all scales used in this study employ a 7-point Likert scale ranging from “1=strongly disagree” to “7=strongly agree”.

Table 2. Measurement Items

3. Results

3.1 Reliability and Validity

To confirm the robustness of selected scales, a confirmatory factor analysis (CFA), a correlation analysis, and a reliability analysis were conducted respectively. First, as reported in [Table 3], the coefficients of Cronbach’s α are all greater than the recommended cut-off value of 0.70[39], which shows a satisfactory reliability. Second, as shown in [Table 4], the diagonal coefficients of the correlation matrix (i.e., square roots of AVE) all exceed the off-diagonal elements (i.e., correlation coefficients), exhibiting an acceptable discriminant validity. Third, the standardized factor loadings are all greater than the recommended threshold of 0.70[40], the coefficients of CR all exceed the recommended minimum value of 0.70[41], and the coefficients of AVE are all greater than the recommended threshold of 0.50[40]. The results discussed above show a favorable convergent validity, as described in [Table 3].

Table 3. Construct Reliability and Validity

Notes: SFL = Standardized Factor Loadings, CR = Composite Reliability, AVE = Average Variance Extracted, CA = Cronbach’s α

Table 4. Descriptive Statistics and Correlation Matrix

Notes: 1. Diagonal elements of correlation matrix are square roots of AVE. 2. **. Correlation is significant at the .01 level (2-tailed).

3.2 Main Effect Model Testing

To empirically validate the main effect model, the current study conducted a path analysis. With regard to the relationship between perceived risk of the online channel and switching intentions, the results manifest that there exists a significant association between these two constructs (β = .133, p < .01), hence, H₁a is supported. The results also indicate that individuals’ perceptions of perceived dissatisfaction of the online channel are positively related to their intentions to switch to the offline channel (β = .470, p < .001), H₁b is accepted accordingly. As such, regarding H₂a, the positive relationship between alternative attractiveness of the offline channel and switching intentions is confirmed (β = .233, p < .001), thus, H₂a is supported. In addition, the results show that switching intentions are positively influenced by perceived ownership of the offline channel (β = .294, p < .001), lending credence to H₂b. More detailed results appear in [Table 5]. Finally, the primary fit indices of the model are all in compliance with the recommended threshold (χ²/df = 2.880(3.00), GFI = .911(.90), CFI = .928(.90), RMSEA = .075(.08), SRMR =.047(.06)), indicating a good model fit [42][43].

3.3 Interaction Model Testing

The PROCESS for SPSS (Model 1) is utilized to estimate the moderation effects of mooring factors on the relationship between push factors and switching intentions, and between pull factors and switching intentions. In terms of the moderating role of switching costs, the results report that switching costs moderate the relationship between pull factors and switching intentions (b = .130, CI [.090, .162], p < .001), whereas the analogous effect on the relationship between push factors and switching intentions is insignificant (b = .014, CI [―.032, .060], p > .05), therefore, H₃a is rejected and H₄a is accepted. Moreover, the significant moderation effects of variety seeking on the relationship between push factors and switching intentions (b = .127, CI [.079, .174], p < .001), and between pull factors and switching intentions (b = .064, CI [.012, .117], p < .05) are confirmed, thereby, H₃b and H₄b are supported. Finally, the results ascertain the significant interactions between push factors and subjective norms (b = .054, CI [.002, .106], p < .05). However, the similar interactions between pull factors and subjective norms are not found (b = .008, CI [―.051, .067], p > .05). Accordingly, H₃c is accepted and H₄c is rejected. [Table 6] describes more detailed results.

Table 5. Main Effect Model Testing Results

Notes: * p < .05, **p < .01, ***p < .001

Table 6. Interaction Model Testing Results

Notes: * p < .05, **p < .01, ***p < .001

Ⅳ. Conclusion

1. Discussion

Parallel to the consumer channel switching behavior literature, the bulk of the extant research places greater emphasis on consumers’ switching behaviors from physical stores to online retailers. Diverging from this, the present study encourages greater focus on the presumable factors influencing an individual’s intention to switch from an online to an offline channel. The results, as expected, show that assumed push factors (i.e., perceived risk and dissatisfaction of the online channel) significantly force a consumer to leave from the online channel, and pull factors (i.e., alternative attractiveness and perceived ownership of the offline channel) positively attract consumers to leave for the offline stores. Additionally, regarding the moderation effects of mooring factors on a consumer’s channel switching decisions, all of the postulated moderating roles of mooring factors (i.e., switching costs, variety seeking, and subjective norms) are confirmed, except for the significant interactions between push factors and switching costs as well as between pull factors and subjective norms. A plausible explanation of the results is that perceived switching costs are generally considered as a proactive and effective factor that may motivate or inhibit an individual’s switching behavior[9][38], however, perceived subjective norms are viewed as a passive and involuntary factor influencing consumer switching behavior[5][6].

2. Theoretical and Managerial Implications

Several theoretical and managerial implications are presented as follows. First, the existing research on the consumer channel switching behavior is appended with new insights into the considerable factors influencing consumers’ intentions to switch from the online to the offline channel. That is, on trap basis of the PPM model, the current study identifies several factors that have an effect on the consumer switching behavior from the online retailer to the physical stores, which offers more comprehensions into the consumer multichannel shopping behavior literature. Second, this study complements the research stream on the bricks-and-mortar bandwagon (i.e., adding physical stores) by exploring why and when do consumers switch to the offline retailer. With regard to the business applications, first, this study empirically confirms the positive roles of both alternative attractiveness and psychological ownership in the process of drawing consumers to the offline channel. These findings suggest that top managers should recognize the importance of these factors and embrace these constructs into current retail space design and segmentation structures. Second, the results demonstrate the significant impact of both switching costs and variety seeking in triggering an individual’s switching behavior from the online to the offline channel. Hence, marketers should consider these two variables when establishing the marketing strategy.

3. Limitations and Future Research

Certain limitations of this study can pave the way for future research. First, the current study is limited to the comparatively single research design, which is detrimental to the external validity of our findings. Seen in this light, to strengthen the validation of the findings, future research is recommended to apply distinctive research designs, such as experimental research and multi-method studies. Second, this study investigates the factors influencing consumer switching behavior from the online to the offline retailer without considering some potential factors, such as the product category. In response, to obtain a more in-depth understanding of the consumer switching behavior from the online to the offline channel, future studies are suggested to consider the product category as a vital potential factor (e.g., hedonic and utilitarian products). Finally, the consumer varied behavior literature suggests that individuals attempt to obtain both experiential and instrumental benefits from the variety seeking behavior[44]. Nonetheless, the current study mainly directs focus to the aspect of instrumental benefits. Therefore, two different aspects of the consumer variety seeking behavior could additionally be considered in future research.