Channel Switching Behavior Research Articles

Psychographic segmentation of multichannel customers: investigating the influence of individual differences on channel choice and switching behavior

This study investigates the role of individual differences in channel choice and switching behavior in a multichannel environment using latent class analysis on data from 1512 customers. Psychographic variables from five domains (risk attitudes, cognitive ability, motivation, personality, and decision-making style) serve as covariates for multichannel customer behavior. We identify six segments that differ significantly on six psychographic variables (readiness to take risks, need for cognition, autotelic and instrumental need for touch, and rational and intuitive decision-making styles). The results advance the theory-building of multichannel customer behavior and present insights for proactively managing customer journeys of distinct segments.

Online product pricing considering product quality and return policy

With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a “showroom” effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers’ optimal pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management.

Explaining consumers’ channel-switching behavior in the post-COVID-19 pandemic era

Significant changes have been brought about in consumer behaviour as a result of the COVID-19 pandemic. Digital consumption has attracted a large number of new consumers during the pandemic. However, there are few academic studies on the determinants of these crucial changes in consumer behaviour. Addressing this gap, this study investigates consumers’ channel-switching behaviour during the COVID-19 pandemic. Using a sample of about 2,640 respondents collected after the outbreak, this study aims to define the key drivers of the changes in consumers’ shopping channel decisions. The study results show that several factors significantly affected consumers’ decisions to change their shopping habits after the pandemic broke out, including marital status, price, quality, convenience, and overall satisfaction with current and new shopping channels. More importantly, this study is one of the few to investigate the differences in determining factors regarding consumers’ choices of online and traditional channels in the post—COVID-19 pandemic era. The level of convenience, the time spent making purchases, technology competency, the abundance of product information, the ability to check product quality, and income significantly impact purchasing channel decisions between online and traditional channels.

Optimal two-period pricing strategies in a dual-channel supply chain considering market change

This paper considers a dual-channel supply chain consisting of one manufacturer with an online direct channel and one offline retailer who distributes the manufacturer’s products, both of whom may adopt a Different or Same price strategy over two periods (Strategy D or S). Strategy D(S) means that the manufacturer and retailer set different(same) retail prices for online and offline channels in the two sales periods. The main purposes of this research are to identify the manufacturer’s and the retailer’s optimal two-period pricing strategy and explore how consumers’ channel preference, price competition, and market change affect the strategy equilibrium. We derive and compare the equilibrium outcomes by establishing four game models: (1) Model SS, both the manufacturer and retailer adopt Strategy S; (2) Model SD, the manufacturer chooses Strategy S while the retailer selects Strategy D; (3) Model DS, the manufacturer chooses Strategy D while the retailer selects Strategy S; (4) Model DD, both choose Strategy D. The research findings demonstrate that most prices increase as the market change rate raises, except for the first-period prices under Model DD, which remain unchanged. Under the different prices settings, a shrinking market would impel supply chain members to utilize a skimming pricing strategy, while a growing market drives them to select a penetration pricing strategy. Different from traditional wisdom, Strategy D would always benefit the manufacturer, while it may hurt the retailer and the whole supply chain under certain conditions. Accordingly, either Strategy DS or DD may be the Nash equilibrium. After considering the consumers’ channel switching behavior, the retailer has more incentive to adopt Strategy S in a shrinking market.

Omnichannel Fashion Retail in Indonesia: How it affects Marketing Performance?

Objectives: Mobile commerce is an appropriate omnichannel practice adoption for fashion retail. Omnichannel in the retail industry is currently becoming an innovation strategy to increase consumer interaction by integrating various sales channel services. Consumers' channel-switching behavior remains as one of the challenges in the retail industry. To deal with these challenges, providing multiple points of contact to interact with consumers that gain competitive advantage and improve marketing performance is required.Methodology: This study is a causal research with a 95% confidence interval in collecting and constructing the data structure to evaluate the cause-and-effect relationship of the variables. The primary data was obtained through a survey of 91 retail fashion consumers in Indonesia. Subsequently, the data was analyzed using structural equation modeling.Finding: Compared to the previous studies, the results of this study indicated that novelty, hedonic, and utilitarian motivations influence brand love through omnichannel customer experience. To improve brand love, companies need to improve customer experience.Conclusion: Based on the findings, the researchers felt one of the biggest flaws in the study was only collecting data on consumers of international clothing products whose physical stores have only been operating in Indonesia

Collective user switching behavior reveals the influence of TV channels and their hidden community structure

Television is the primary medium through which most families access entertainment and information in their daily lives. Thus, understanding users’ TV viewing behavior is meaningful for several practical issues, such as evaluating the influence of TV channels and providing personalized TV recommendations. However, most existing works regarding TV viewing data are limited to basic statistics (e.g., TV ratings). In this paper, we analyze a large-scale TV viewing dataset for a city in China via a complex network approach. We construct a directed network that characterizes the collective channel-switching behavior of viewers. By using the PageRank method, we reveal the influential TV channels that are more in line with people’s expectations than their rankings based on simple TV ratings. We further construct a network in which channels are linked according to their similarity in users’ switching preferences. This network exhibits a clear community structure, which can help TV stations understand which channels are in the bottleneck and which channels have potential. Overall, our work provides a system perspective to evaluate TV channels and their relationships.

판매원이 경험한 고객 쇼루밍 행동이 판매원 창의성을 매개로 직무성과에 미치는 효과: 서비스 스크립트의 조절적 역할을 중심으로*

A recent interest in multi-channel retailing is customer showrooming behavior which has appeared as one of the important issues in the dynamic retailing environments. Customer showrooming behavior refers to shopping behavior by consumers who intentionally benefit from the explanations and services of on retailers in on channel before actually purchasing from a different retailers in another channel. It was reported that in all 68% of US Internet users had decided to buy online channel decided to first check products at traditional offline store. This statistics imply the seriousness of the customer showrooming behavior and various researchers have expressed their concern over rising showrooming phenomenon. Although research studies in the past have deal with behaviors related consumer showrooming like cross-channel free riding, channel switching behavior, but individual studies on showrooming from a employee, namely, salesperson perspective have not been conducted yet. With this focus, we concentrate on offline retailing salesperson, surprisingly largely ignored stakeholders in multi-channel studies. This study aims to examine the mediating effect of salesperson creativity on the relationship between experienced customer showrooming behavior and job performance using department store salesperson samples. Second, we investigate the moderating effect of service scripts that weaken the effects of experienced customer showrooming behavior and salesperson creativity. Also, this study seek to examine the moderating effects of service script on the mediating effect of salesperson creativity. Noting the theoretical and empirical supports provided by past studies, we formulated the following hypotheses. <Figure 1> presents the research model. Figure 1 Research model and empirical analysis result χ2(215)=626.15, p<.05, RMSEA=.07, TLI=.91, SRMR=.05, CFI=.92 Hypothesis 1: Salesperson creativity will mediate the positive relationship between customer showrooming behavior and job performance. Hypothesis 2: Service scripts will moderate the positive relationship between customer showrooming behavior and salesperson creativity. Specially, the positive relationship between customer showrooming behavior and salesperson creativity will be weaker with high levels of service script Hypothesis 3: Service Scripts will moderate the mediating effect of salesperson creativity on the relationship between customer showrooming behavior and job performance such that the indirect effect of customer showrooming behavior on job performance is weaker when service scripts is high. We collected data from salespersons who were working in a department store. We contacted the human resources department of the store and obtained permission to survey their staff. Specifically, they facilitated the distribution of the survey instruments to a total 600 full-time salespersons who were working in various sales units in the department store. The process yielded a total of 397 questionnaire responses, representing a response rate 69.1%. We conducted confirmatory factor analysis to examine the convergent and discriminant validity of the measurement items using M-plus. The measurement model fit the data well(fit indices: χ2(215)=626.15, p<.05, RMSEA=.07, TLI=.91, SRMR=.05, CFI=.92). All the measure demonstrated strong composite reliabilities(CR) ranged from .80 to 93(see Table 1). Also, we confirmed the condition for discriminant validity among the constructs. All the average variance extracted(AVE) values were greater than the squared correlations between a construct and any other variable(see Table 1). Table 1 The results of Confirmatory factor analysis construct item Standardized loading t Cronbach

Fashion Consumers’ Channel Switching Behavior During the COVID-19: Protection Motivation Theory in the Extended Planned Behavior Framework

The purpose of this study is to understand fashion consumers’ channel switching to online stores during the COVID-19. We proposed an extended theory of planned behavior by incorporating protection motivation theory. The results showed that consumer assessments of perceived severity and altruistic fear of COVID-19 and response efficacy and self-efficacy of channel switching increased their beliefs (i.e., attitude, perceived behavior control, subjective norm) and intentions to switch shopping channels to online. We also found that the age (young vs. old) moderated the effects of response efficacy and self-efficacy on perceived behavior control, perceived severity on subjective norm, perceived behavior control on channel switching intentions, and channel switching intention on actual switching behavior. The findings provide fashion retailers and the society with a better understanding about fashion consumers’ shopping channel switching under the pandemic.

Performance Analysis of Adaptive Multicast Streaming Services in Wireless Cellular Networks

To improve bandwidth efficiency, multicast is used for mobile IPTV services. However, conventional multicast employs the most robust (lowest rate) modulation and coding scheme (MCS) for all multicasting channels to ensure that all users in a cell can receive videos with acceptable quality, leading to the worst spectral efficiency. To improve spectral efficiency, the adaptive MCS multicasting (aMCSm) scheme, which dynamically adjusts the MCS of each multicasting channel according to channel users’ received signal-to-noise ratios (SNRs), was proposed. In this paper, the blocking rate, dropping rate, and bandwidth usage of mobile IPTV services under the aMCSm and the proposed admission control mechanism were analyzed based on a Markovian queueing model. The proposed mathematical model considers adaptive modulation and coding (AMC), time-varying link characteristics, channel popularity, and channel switching behavior. Numerical results demonstrate that our mathematical analyses are accurate and the aMCSm achieves better blocking rate and bandwidth usage performance, compared with the conventional multicast and multi-partition multicast schemes. The impact of channel switching or MCS variation on the performance of mobile IPTV services was investigated as well.

Pinball attacks against Dynamic Channel assignment in wireless networks

In this paper, we expose a class of stealthy attacks, which we coin pinball attacks, that exploit the adaptive nature of Dynamic Channel Assignment algorithms in wireless networks. In a pinball attack, the attacker strategically induces interference in the neighborhood of certain nodes to cause a cascading channel switching behavior, thereby exploiting the topology of the network to his own advantage. The attacker’s problem is formulated as a Markov Decision Process in which the attacker seeks to maximize his long-term net rewards. To overcome the exponential size of the state space, we derive suboptimal attack policies through approximation methods. We expose the pinball attacks on two general setups that arise practically in wireless communication systems. In the first setup, each wireless node is an access point aiming to select a channel that has minimum interference with its neighbors. In the second setup, we consider wireless nodes with multiple antennas whereby every node should communicate with each of its neighbors on a separate channel. We formally establish the equivalence of both setups by showing that the identified pinball attack policies on one can be directly mapped to attack policies on the other. We present numerical results to assess the impact of the obtained pinball attack policies under various topologies, and show that they outperform other known attack policies over all topologies considered. We also propose a novel approach to scale up the attack to larger size networks with special structures.

Multi-period price optimization problem for omnichannel retailers accounting for customer heterogeneity

In recent times many brick and mortar retailers have considered adding online channels to compete with e-tailers. These retailers have further enabled omnichannel features into their businesses considering customer's channel switching behavior. This article presents a price optimization problem for such a retailer who already had many offline stores but then added an online channel to improve its digital presence. The retailer has a distribution center that replenishes these stores periodically. Furthermore, the retailer uses either the distribution center or any of the offline stores to fulfill online orders. The paper takes an integrated approach to price optimization, inventory control, and e-fulfillment problem and developed a decision support model for such a retailer having multiple objectives of profit maximization and lost sales minimization. The lost sales minimization is achieved either by decreasing the probability of no-purchase option (by reducing the posted price) or by fulfilling online demands at relatively higher costs (it is not optimal to meet all the demand). The paper tests the efficacy of conventional Non-dominated sorting genetic algorithm-II (NSGA-II) and recently proposed direct Zigzag search in order to solve the resulting NP-hard discrete multi-objective optimization problem. We offer a new algorithm that exploits the neighborhood search feature of the Zigzag method to extend the NSGA-II front further. Furthermore, using realistic and real-world inspired data, we generated some useful insights for omnichannel retailers.

Predictors of Commercial Zapping During Live Prime-Time Television

<h3>ABSTRACT</h3> Television advertising-avoidance research delivers a broad range of estimates for the extent of channel switching during advertising breaks (<i>i.e.</i>, commercial zapping). The diverse methods likely contribute to the disparity in commercial-zapping estimates. This study implements a combined hidden-observation/survey approach and tests potential predictors of commercial zapping. The use of remote controls emerged as a significant driver of observed commercial zapping. Perceived clutter and advertising triggers emerged as significant predictors of reported commercial zapping. This study empowers media planners to determine prime-time advertising-audience erosion more accurately. Additionally, with a greater degree of confidence, advertisers can address factors that drive channel-switching behavior and dismiss factors that do not.

Channel Switching Behavior From Traditional Grocery Stores to Branded Grocery Chains in Karachi

The advent of branded grocery chains has compelled customers to switch from traditional grocery stores to branded grocery chains or monthly grocery purchase. This phenomenon has been recently observed in Pakistan. The aim of this study is to explore the customers' level of switching over from traditional grocery stores to branded grocery chains and also to find out the determinants of switching over from traditional grocery stores to branded grocery chains in Karachi. This study is quantitative in nature and encompasses the Positivist paradigm. The deductive approach is employed and survey method is opted for in this research. As per the requirement of the theme, closed-ended questionnaire is used as research instrument and SPSS statistical software is employed for data analysis. As sampling strategy, convenience sampling technique is opted. Total of 105 valid questionnaires were received; 35 from each store i.e., Naheed Super Market, Imtiaz Super Store and Chaseup Departmental Store. To ascertain level of switching over it can be seen from the results that 90% of the respondents have been buying since four months and above from branded grocery chains. The research study reveals that the three independent variables (Proximity, Environment and Engagement) are correlated with channel switching with proximity having a low correlation.

Explaining consumers’ channel-switching behavior using the theory of planned behavior

This exploratory study examined channel-switching behavior using the theory of planned behavior in three retail channels: bricks-and-mortar stores, catalogs, and the Internet. This theory assumes that individual attitudes and beliefs, along with subjective norms and control factors, will lead to an intention to perform a certain behavior, whether to switch channels or not. An online survey was administered at four different research sites and resulted in 547 usable surveys. Factor analysis and regression were used for data analysis. Attitude towards channel-switching was significantly influenced by hedonic and utilitarian beliefs in stores and catalogs. However, in the case of the Internet channel, attitude towards channel-switching was only influenced by utilitarian beliefs. Normative beliefs negatively influenced subjective norms in all the channels. Self-efficacy, information, and product type were important factors that impacted perceived behavioral control (PBC) in all channels. Time influenced PBC only in catalogs, and money did not influence PBC in any of the channels. Attitude and subjective norms influenced channel-switching intentions for three channels, whereas PBC was a significant predictor for channel-switching intention for only catalogs and the Internet.

A consumer model for channel switching behavior

With the phenomenal growth of direct order marketing with the Internet and catalogs as alternative channels, customers increasingly face more choices of where to purchase goods and services. This paper develops a formal consumer model to explain channel switching behavior. Becker’s theory of time allocation is expanded to consumer decision making between distribution channels. The final model suggests that consumers face a tradeoff when deciding where to buy goods and services. From this tradeoff an indifference curve is developed where the consumer chooses between alternative distribution channels on the basis of the relative opportunity costs of time, costs of goods, pleasure derived from shopping, perceived value of goods, and relative risk of each channel. Strategies for direct and multi‐channel marketers are developed using this model.