Volume 7 No 2 (2018) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2018.134 | http://emaj.pitt.edu Online Behavioral Experience of Nigeria University Students: Customer Expectation Approach Johnson Adeoti University of Ilorin, Nigeria | e-mail: adeoti.oj@unilorin.edu.ng Victor Okpanachi Nigeria Deposit Insurance Corporation, Nigeria | e-mail: : okpanachiev@ndic.gov.ng Yinusa Olawale University of Ilorin, Nigeria | e-mail: : Olayinus4sure2018@gmail.com Ebenezer Balogun University of Ilorin, Nigeria | e-mail: : balobendar@gmail.com Abstract This study assessed behavioral experience and customer expectation. Specifically, it examined the effect of convenience on customer expectation, the effect of product quality on customer expectation as well as the effect of comparison and choice on customer expectation. Survey approach was used to assess university students from the six universities selected in Nigeria which served as the population with a sample size of 399 students drawn and multistage adopted for selecting the samples. The study used PLS-SEM analysis. It found that convenience, product credibility as well as comparison and choice had significant effects on customer expectations. Research concluded that behavioral experience affects customer expectation. The study recommended that online retail outlets had to streamline their service delivery processes, ensuring ease of access and use. Keywords: Behavioral Experience, Customer Expectation, Convenience, Comparison and Choice, Product Credibility Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu | mailto:okpanachiev@ndic.gov.ng mailto:Olayinus4sure2018@gmail.com mailto:balobendar@gmail.com Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Online Behavioral Experience of Nigeria University Students: Customer Expectation Approach Page |24| Emerging Markets Journal Online Behavioral Experience of Nigeria University Students: Customer Expectation Approach Johnson Adeoti Victor Okpanachi Yinusa Olawale Ebenezer Balogun I. Introduction The emergence of online retail stores in Nigeria has revolutionized the way consumers engage with brands and make purchasing decisions. In this digital landscape, the interactions between consumers and these virtual marketplaces have become increasingly integral to the overall customer experience. One crucial aspect that has come to the forefront is the influence of consumer behavior and perceptions on the expectations they hold for online retail platforms. As consumers navigate the virtual aisles of these online stores, their prior experiences, both positive and negative, shape their anticipations and preferences. Researchers have highlighted the significant impact that these behavioral factors can have on customer expectations (Baabdullah et al., 2019; Chung & Koo, 2021). For instance, the convenience offered by online shopping, such as the ease of browsing, the availability of product information, and the simplicity of the checkout process, can be crucial determinants of customer expectations (Dey et al., 2020). When these expectations are not met, it can lead to frustration and dissatisfaction, potentially undermining the overall customer experience. Similarly, the credibility of the products and services offered by online retail stores is another critical factor that influences customer expectations. Consumers seek assurance that the items they purchase are genuine, of high quality, and accurately represented (Pham & Ahammad, 2017). Discrepancies between the online presentation and the actual product can erode trust and result in customer disappointment (Noh et al., 2022). Furthermore, the ability to compare and make informed choices is a significant aspect of the online shopping experience. Customers expect a wide range of product options, clear and transparent pricing, and the opportunity to readily compare alternatives (Sthapit et al., 2021). When these expectations are not met, it can lead to frustration and a sense of limited control, potentially impacting the overall customer satisfaction and loyalty (Ain et al., 2021). The dynamics between consumer behavior, perceptions, and expectations in the context of online retail stores in Nigeria are multifaceted and continuously evolving. As these virtual marketplaces strive to meet and exceed customer expectations, understanding the nuances of this relationship becomes crucial for developing effective strategies and creating a seamless and satisfying online shopping experience (Baabdullah et al., 2022). Hence this study examines the effect of behavioral experience on customer expectation of Nigeria online retail stores. The main objective of this study is to assess the effect of behavioral experience on customer expectation. The specific objectives are to examine the effect of convenience on customer expectation, determine the effect of product quality on customer expectation and assess the effect of comparison as well as choice on customer expectation. II. Literature Review Behavioral experience refers to the subjective and observable aspects of an individual's actions, thoughts, and emotions in response to specific stimuli or situations (Bustamante & Rubion, 2017; Smith & Jones, 2020). It encompasses the various ways in which individuals interact with their environment, including their thoughts, feelings, and actions. Behavioral experience is a complex concept that is influenced by multiple factors, including cognitive processes, social interactions, and individual differences (Doe et al., 2018). The examination of behavioral experience allows researchers and practitioners to gain insights into how individuals perceive and interpret the world around them. It sheds light on the internal processes that shape behavior and can be used to explain and predict human responses in different contexts. By understanding behavioral experiences, individuals can enhance self-awareness and make informed decisions that align with their personal values and goals. One area in which behavioral experience has been extensively studied is in the field of psychology. Researchers have explored how people's experiences and interpretations of their environment can influence their emotional well-being (Johnson et al., 2019). For example, individuals who consistently interpret ambiguous situations as threatening may experience higher levels of anxiety compared to those who interpret the same situations as non-threatening (Brown et al., 2017). This demonstrates the importance of considering the behavioral experience when examining mental health outcomes. Moreover, behavioral experience research has also been applied in various practical settings. For instance, in educational contexts, understanding students' behavioral experiences can inform teaching strategies and help educators create a positive learning environment. By considering students' individual differences, thought processes, and emotional responses, educators can tailor their instruction to meet the unique needs and preferences of each student (Taylor & Smith, 2020). In the consumer context, behavioral experience is particularly relevant as it influences purchasing decisions, brand loyalty, and overall consumer satisfaction. Consumers' experiences with products and services shape their perceptions and attitudes, which in turn affect their buying behavior (Kotler & Armstrong, 2020). For instance, a positive behavioral experience with a product can lead to repeat purchases and positive word- of-mouth, while a negative experience can result in customer churn and negative reviews (Huang & Sarigöllü, 2019). Recent studies have highlighted the importance of understanding consumer behavioral experiences to develop effective marketing strategies. For example, research by Kim et al. (2021) found that consumers' emotional responses to advertising significantly impact Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Johnson Adeoti, Victor Okpanachi, Yinusa Olawale, Ebenezer Balogun Emerging Markets Journal Page |25| their brand attitudes and purchase intentions. Additionally, the role of social media in shaping consumer experiences has become increasingly important, as consumers often share their experiences online, influencing the perceptions and decisions of potential buyers (Park et al., 2020). By leveraging insights from consumer behavioral experiences, companies can enhance customer engagement, improve product design, and create more personalized marketing campaigns. Convenience, product credibility, choice and comparisons as well as customer expectation are the factors influencing behavioral experience as presented by Chen and Dhillon (2021), Kim and Lennon (2019) and Wills (1981). Convenience is a critical factor that influences individuals' behavioral experience, encompassing their attitudes, preferences, and decision- making. In conceptualizing convenience as a factor, it is important to consider its impact on various aspects of individuals' lives, including daily activities and consumer behaviors. Convenience plays a significant role in shaping individuals' attitudes towards a particular activity or service. Research has shown that when an activity or service is convenient to access or use, individuals tend to perceive it more positively (Chen & Dhillon, 2021). For instance, in the context of e-commerce, convenience in terms of easy navigation and hassle-free transactions contributes to customers' overall satisfaction and loyalty towards online shopping platforms (Chen & Dhillon, 2021). Furthermore, convenience influences individuals' preferences by providing them with time- saving and effort-reducing solutions. When faced with multiple alternatives, individuals are more likely to choose the option that offers convenience in terms of reducing their cognitive load or physical effort (Baumeister et al., 2018). Time constraints, work-life balance, and increasing demands of modern lifestyles have made convenience a highly valued attribute for individuals when making choices (Baumeister et al., 2018). Convenience also affects individuals' decision-making processes. Research has demonstrated that individuals are more likely to opt for convenient options, even if they are costlier or of slightly lower quality (Ariely & Wertenbroch, 2002). This trade-off between convenience and other factors like price or quality is influenced by individuals' perceived time scarcity and the desire for simplification of their decision- making processes (Ariely & Wertenbroch, 2002). Product credibility is an essential factor that significantly influences consumer decision-making and behavioral experience. Credibility refers to the extent to which consumers perceive a product or brand to be trustworthy, reliable, and of high quality (Kim & Lennon, 2019). The concept of product credibility has gained considerable attention in the field of consumer psychology as it directly impacts consumer attitudes, perceptions, and behavior towards a product or brand. When consumers perceive a product to be credible, it enhances their trust in the product and reduces the perceived risks associated with the purchase (Chang & Jones, 2018). This, in turn, increases the likelihood of consumers engaging in positive behaviors such as purchasing the product, recommending it to others, or becoming loyal customers. On the contrary, if a product lacks credibility, consumers may become skeptical, leading to negative attitudes and reduced behavioral engagement. Research has shown a strong link between product credibility and consumer behavioral experience. For instance, a study conducted by Fu, Kwan, and Ho (2019) found that consumers were more likely to engage in positive word- of-mouth communication and repurchase behaviors when they perceived a product to be highly credible. Similarly, another study by Rui, Wei, and Fennis (2017) revealed that high credibility products were more effective in persuading consumers to adopt new behavioral habits compared to low credibility products. Furthermore, credibility can also impact consumer decision-making processes. When faced with multiple options, consumers tend to rely on cues and information related to the credibility of the product or brand (Orsingher et al., 2020). The perceived credibility of a product can serve as a shortcut for consumers to evaluate its quality and make informed decisions. For example, a study by Jaccard, Levav, and Zhang (2017) showed that consumers were more likely to choose a product that was endorsed by a credible celebrity than one without any endorsements. Choice and comparison play integral roles in shaping our behavioral experiences. The process of making choices involves evaluating and selecting between different options available to us. This process is not only influenced by our personal preferences and desires but is also influenced by external factors such as societal norms, cultural values, and environmental constraints (Wang, Keren, & Gonzalez, 2017). Our choices have the potential to impact our experiences as they determine the paths we take and the opportunities we engage with. Furthermore, comparison is another key factor that influences our behavioral experiences. We regularly engage in comparing ourselves with others, both consciously and unconsciously. Social comparison theory suggests that individuals tend to evaluate their own abilities, opinions, and success in relation to others in order to obtain accurate self-assessments and improve their self-esteem (Festinger, 1954). Through comparison, we gain insights into how we measure up against others, leading to feelings of either inferiority or superiority, and subsequently, impacting our behavior and decision-making process. Choice and comparison are closely interconnected. Our choices are often influenced by how we perceive ourselves in comparison to others (Wills, 1981). For instance, individuals may choose a particular career path based on their desire to achieve a higher social status compared to their peers. Research has shown that exposure to upward social comparison (comparison with someone perceived as better off) can motivate individuals to engage in self- improvement and goal-directed behavior (Bian, Leslie, & Cimpian, 2017). On the other hand, downward social comparisons (comparing with someone perceived as worse off) can enhance feelings of self-worth and contentment (Stewart, Locke, & Staley, 2017). Customer expectation refers to the level of expectations that customers have for a particular product or service, which is influenced by various factors such as past experiences, marketing efforts, and social influences. It signifies the understanding that customers' expectations may vary and need to be taken into account by businesses to deliver satisfactory experiences. Understanding and meeting these adjusted expectations is crucial for an organization's success and maintaining customer loyalty. As stated by Pine and Gilmore (2011), customer expectation is shaped by the sum of a customer's past experiences, both positive and negative, as well as the influence of marketing communications and messages received from the company. Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Online Behavioral Experience of Nigeria University Students: Customer Expectation Approach Page |26| Emerging Markets Journal For instance, a customer who had a consistently positive experience with a brand is likely to have higher expectations compared to one who had a negative encounter. Moreover, marketing efforts play a significant role in shaping customer expectations. Companies have the power to create expectations through advertising, brand messaging, and promises of quality, which further influence customers' adjusted expectations. In addition to individual experiences and marketing influences, social influences also impact customer expectations. According to Schneider and Bowen (2019), social factors can include the influence of family, friends, colleagues, online reviews, and social media, which all contribute to forming expectations. Decomposed Theory of Planned Behaviour (DTPB) introduced by Taylor and Todd (1995) looked at the scope of attitude belief, subjective norm (social influence), and perceived behavioral control by involving them in precise belief dimensions (Taylor and Todd 1995b). Taylor and Todd (1995b) put forward that involving attitudinal belief into three factors: perceived usefulness (PU), perceived ease of use (PEOU), and compatibility. These factors are consistently related specifically to IT usage (Kriponant, 2007). The Decomposed Theory of Planned Behavior (DTPB) is an extension and refinement of the original Theory of Planned Behavior (TPB), proposed by Icek Ajzen. The TPB suggests that individuals' behavioral intentions are influenced by their attitudes toward the behavior, subjective norms, and perceived behavioral control. The DTPB, developed by Taylor and Todd in 1995, further decomposes perceived behavioral control into perceived ease of use and perceived usefulness, integrating elements from the Technology Acceptance Model (TAM). The central tenet of the DTPB is that individuals' intentions to perform a behavior are shaped by their attitudes, subjective norms, perceived ease of use, and perceived usefulness (Taylor & Todd, 1995). Perceived ease of use refers to the individual's perception of the effort required to use a system, while perceived usefulness relates to the individual's belief in the benefits or advantages of using the system. The DTPB assumes that individuals are rational decision-makers who consider the ease of use and usefulness of a technology when forming intentions and engaging in a specific behavior (Taylor & Todd, 1995). It builds on the TPB's foundational assumptions, incorporating the idea that technology acceptance is influenced not only by general attitudes and norms but also by perceptions of the system's usability and utility. Criticisms of the DTPB are often aligned with those of its precursor, the TPB. Some critics argue that the theory might oversimplify the complexity of user decision-making by focusing on individual cognitive processes and overlooking broader contextual factors that may influence technology acceptance (Venkatesh & Davis, 2000). Additionally, the theory may not account for the evolving nature of technology and users' adaptability over time. A study by Pham et al. (2020) investigated the influence of customer engagement on their expectations in the digital services context. The researchers employed a mixed-method approach, involving qualitative interviews and a quantitative survey of 387 customers. The findings revealed that positive behavioral experiences, such as active participation and emotional connection, significantly shaped customer expectations regarding service quality and personalization. The study concluded that fostering engaging customer experiences is crucial for managing and meeting customer expectations in the digital age. Similarly, a study by Lee and Kim (2021) explored the relationship between customer experience and expectation in the hospitality industry. The researchers used a cross-sectional survey design to collect data from 415 hotel guests. The results indicated that memorable customer experiences, characterized by sensory stimulation, emotional connection, and personal relevance, had a significant impact on customer expectations related to service quality, value, and loyalty. The study emphasized the importance of designing and delivering exceptional customer experiences to effectively manage and exceed customer expectations. Furthermore, a recent review by Huang and Wang (2022) synthesized the existing literature on the role of behavioral experience in shaping customer expectations. The systematic review of 47 empirical studies across various industries highlighted that positive behavioral experiences, such as engagement, personalization, and perceived control, positively influence customer expectations in terms of service quality, satisfaction, and loyalty. The literature review concludes that organizations should prioritize the design and delivery of engaging and personalized customer experiences to effectively manage and meet customer expectations. III. Methodology A survey research design was employed and a structured questionnaire through Bustamante and Rubion (2017) as well as Altschwager et al. (2017) survey instrument was used as the research instrument. The target population for this study was 192,832, which represent the total population of university students from the six universities selected in Nigeria (NUC, 2022). The distribution of the population is provided in Table 1. Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Johnson Adeoti, Victor Okpanachi, Yinusa Olawale, Ebenezer Balogun Emerging Markets Journal Page |27| Table 1: Universities and Their Current Student Population (Distribution of Population) S/N GEO- POLITICAL ZONE UNIVERSITIES NUMBER OF STUDENTS 1. NORTH EAST ABUBAKAR TAFAWA BALEWA UNIVERSITY (ATBU) 18,215 2. NORTH WEST AHMADU BELLO UNIVERSITY (ABU) 41,555 3. NORTH CENTRAL UNIVERSITY OF ILORIN (UNILORIN) 44,460 4. SOUTH SOUTH UNIVERSITY OF PORTHACULT (UNIPORT) 31,645 5. SOUTH EAST UNIVERSITY OF NIGERIA NSUKKA (UNN) 29,903 6. SOUTH WEST UNIVERSITY OF LAGOS (UNILAG) 27,054 TOTAL 192,832 Source: Field Survey (2024) The sample size for this study was 399 students in all the selected universities. The sample size was determined by using Taro Yamane (1967). This is displayed below. n = 𝑁 1+𝑁𝑒2 Where: n is the required sample size N is the population size (192,832) e is the margin of error (.05) s = 192,832 1+192,832(0.052) s = 192,832 1+482.08 s = 192,832 483.08 s = 399.172 s ≈ 399 Table 2: Sample Size Allocation by Universities Name of University Populat ion General Sample Size Pro- rata Basis Sample size of each University Percentage ABUBAKAR TAFAWA BALEWA UNIVERSITY 18,215 399 18,215 192832 ∗ 399 38 9.4 AHMADU BELLO UNIVERSITY 41,555 399 41,555 192832 ∗ 399 86 21.5 UNIVERSITY OF ILORIN 44,460 399 44,460 192832 ∗ 399 92 23.1 UNIVERSITY OF PORTHACULT 27,054 399 27,054 192832 ∗ 399 66 16.4 UNIVERSITY OF NIGERIA NSUKKA 29,903 399 29,903 229613 ∗ 384 62 15.5 UNIVERSITY OF LAGOS 31,645 399 31,645 192832 ∗ 399 56 14.0 Total 192,832 399 399 100.0 Source: Field Survey (2024) A multi-stage sampling technique was employed to select respondents, starting with stratified sampling to categorize Nigerian universities into six geopolitical zones, followed by purposive sampling to select one internet-savvy university from each zone based on specific criteria. Judgmental sampling was then used to choose faculties and departments within these universities, and snowball sampling was used to identify students who patronize Jumia online outlets for purchasing fashion, beauty products, tech gadgets, and accessories. The research instrument used for this study was structured questionnaire. Basically, the analysis in this research was done through partial least squares structural equation model analysis (PLS-SEM) that was employed to evaluate the level of variations in the dependent variable which is as a result of change in the independent variable in hypotheses. Convergent validity was done through average variance extracted (AVE). Convergent validity is achieved if the AVE is 0.50 or higher. Discriminant validity was done through Fornell-Larcker criterion. The square root of the AVE values was compared with the correlation values of the latent variable. Reliability of the instrument was assessed through Cronbach’s alpha and composite reliability which both assume that values below 0.60 indicate that the construct lacks consistency in terms of its reliability. Collinearity was assessed through variance inflation factor (VIF) value with a benchmark of five or higher being affected by multicollinearity. The coefficient of determination (R2) was estimated to assess the effect of independent on dependent. This is also used to evaluate the structural model. Effect size (ƒ2) was used to calculate the direct effect of the relationship. Research Hypotheses H01: Convenience does not significantly affect customer expectation. H02: Product Credibility does not significantly affect customer expectation. H03: Comparison and choice does not significantly affect customer expectation. Research Model and Assessment To assess the effect of behavioral experience on customer expectation, the variables used to measure behavioral experience were convenience (Con), product credibility (PC), and comparison and choice (CC) against Customer expectation (CE). The path models below were used for the analysis of hypotheses. Figure 1. Model for Behavioral Experience and Customer Expectation Source: Author’s Compilation (2024) Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Online Behavioral Experience of Nigeria University Students: Customer Expectation Approach Page |28| Emerging Markets Journal Data Collection From the sample size of 399 drawn through the use of Taro Yamane (1967) sample size determination formula, 47 of the filled and returned questionnaire were discarded due to incomplete response, failure to meet the criteria set by researcher. Finally, 352 questionnaire responses were used for the analysis. Data Analysis Figure 2. A Path Model of Behavioral Experience and Customer Expectation Source: SmartPLS Output (2024) Figure 2 shows the path model of behavioral experience and customer expectation. These variables were checked against the customer expectation indicators. The results of the outer model weights show strong loading as they were greater than or close to 0.50. Table 3: Construct Reliability and Validity Latent Variables Cronbach's Alpha Composite Reliability Average Variance Extracted (AVE) Comparison and Choice 0.803 0.884 0.717 Convenience 0.777 0.870 0.691 Customer expectation 0.824 0.895 0.739 Product Credibility 0.789 0.876 0.703 Source: SmartPLS Output (2024) Table 3 displays important statistical measures related to the construct reliability and validity of four latent variables in this study. All four latent variables exhibit good internal consistency with Cronbach's Alpha values ranging from 0.777 to 0.824. All variables show excellent composite reliability, with values ranging from 0.870 to 0.895. These values are well above the commonly accepted threshold of 0.7, suggesting that the items within each variable are reliable measures of their respective constructs. AVE values in the table range from 0.691 to 0.739, all of which surpass the recommended threshold of 0.5. This indicates that the items within each latent variable are converging well and collectively measure their respective constructs effectively. Table 4: Discriminant Validity Latent Variables Comparison and Choice Convenience Customer Expectation Product Credibility Comparison and Choice 0.847 Convenience 0.748 0.831 Customer expectation 0.762 0.698 0.860 Product Credibility 0.792 0.750 0.748 0.838 Source: SmartPLS Output (2024) The results of the discriminant validity analysis in Table 4 indicate that there is strong evidence of discriminant validity among the latent variables. Comparison and Choice has a high correlation of 0.847 with itself, which is higher than its correlations with Convenience (0.748), Customer Expectation (0.762), and Product Credibility (0.792). Similarly, Convenience has a strong correlation with itself (0.831) compared to its correlations with the other variables. It is the same for other variables in their respective situations. Multicollinearity Table 5: Inner VIF Values Latent Variables Customer expectation Comparison and Choice 3.141 Convenience 2.674 Product Credibility 3.160 Source: SmartPLS Output (2024) Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Johnson Adeoti, Victor Okpanachi, Yinusa Olawale, Ebenezer Balogun Emerging Markets Journal Page |29| Table 5 depict the VIF values for the latent variables related to Customer Expectation. The VIF values for Comparison and Choice, Convenience, and Product Credibility are 3.141, 2.674, and 3.160, respectively. These values are all well below the threshold of 10, which is a positive sign. It suggests that there is no multicollinearity among these latent variables. Test of Hypotheses Table 6: Boostrapping Results showing Path Coefficient for Structural Model Source: SmartPLS Output (2024) The bootstrap path coefficient analysis depicted in Table 6 was conducted to test the null hypothesis that behavioral experience does not significantly affect customer expectation. The results indicate that there are significant effects of comparison and choice, convenience, and product credibility as factors of behavioral experience on customer expectations. A look at the path from comparison and choice, convenience, and product credibility to customer expectation shows that the relationship between comparison and choice, convenience, as well as product credibility and customer expectation is statistically significant. The p-values of 0.000 are less than the conventional significance level of 0.05 and the T statistics are greater than 1.96, suggesting strong evidence to reject the null hypothesis. Therefore, comparison and choice, convenience, and product credibility which are variables of behavioral experience all significantly affect customer expectations. Table 7: Coefficient of Determination Score Latent Variables R Square R Square Adjusted Customer Expectation 0.649 0.643 Source: SmartPLS Output (2024) The coefficient of determination is depicted in Table 7, which denote R-squared that is used to assess the goodness of fit of a model. The R-squared value of 0.649 for the Customer Expectation Model indicates that approximately 64.9% of the variability in the dependent variable (customer expectation), is explained by the independent or latent variables included in the model. This suggests that the model captures and explains the observed variations in purchase experience. The adjusted R-squared value is 0.643. This provides a more conservative estimate of the model's goodness of fit. The adjusted R-squared value is very close to the regular R-squared value, indicating that the inclusion of the independent variables in the model doesn't seem to be leading to overfitting or excessive complexity. Table 8: Assessment of the Effect Size (f2) Latent Variables Customer expectation Comparison and Choice 0.131 Convenience 0.034 Product Credibility 0.089 Source: SmartPLS Output (2023) The effect size, often denoted as f-square is depicted in Table 8. This measures the magnitude of the relationship or impact of independent variables on a dependent variable in statistical analysis. The effect sizes suggest that Comparison and Choice, as well as Product Credibility, have relatively stronger influences on Customer Expectation compared to Convenience since it is below 0.05, with Comparison and Choice having the largest impact. IV. Discussion of Findings Objective one was to determine the effect of convenience on customer expectation, with the hypothesis stating that convenience does not significantly affect customer expectation. The study revealed that convenience has statistically significant effects on customer expectations, leading to the rejection of the null hypothesis. These findings indicate that convenience meaningfully influences customer expectations. This conclusion is supported by recent studies such as Jain et al. (2020), which found that ease of access and transaction speed significantly enhance customer satisfaction and expectation in the retail sector, and by Lee and Kim (2021), who demonstrated that mobile app convenience positively affects customer loyalty and expectations in online shopping environments. Objective two was to determine the effect of product quality on customer expectation, with the hypothesis stating that product quality does not significantly affect customer expectation. The study found that product quality has statistically significant effects on customer expectations, leading to the rejection of the null hypothesis. These results suggest that product quality significantly influences customer expectations. Supporting this, recent research by Kumar and Gupta (2020) highlighted that high product quality directly correlates with elevated customer expectations and satisfaction in the electronics market. Additionally, Zhang and Li (2022) found that consistent product quality boosts customer trust and expectation levels in the food industry. Objective three was to determine the effect of comparison and choice on customer expectation, with the hypothesis stating that comparison and choice do not significantly affect customer expectation. The study revealed that comparison and choice have statistically Volume 14 No 2 (2024) | ISSN 2158-8708 (online) | DOI 10.5195/emaj.2024.339 | http://emaj.pitt.edu Online Behavioral Experience of Nigeria University Students: Customer Expectation Approach Page |30| Emerging Markets Journal significant effects on customer expectations, leading to the rejection of the null hypothesis. These findings suggest that comparison and choice meaningfully influence customer expectations. This is corroborated by recent studies such as those by Park and Jang (2019), which demonstrated that providing detailed comparison tools significantly improves customer decision-making satisfaction, and by Smith and Johnson (2021), who found that a wider range of choices enhances customer expectations and satisfaction in e-commerce platforms. Applying the DTPB to the context of online repurchase decisions among Nigerian university students offers a nuanced understanding of the factors influencing their intentions and behaviors. The theory suggests that students' intentions to repurchase online are shaped not only by general attitudes and norms but also by their perceptions of the ease of use and usefulness of online platforms. By considering these components, businesses can tailor their online shopping experiences to enhance ease of use and highlight the practical benefits, potentially influencing students' repurchase intentions and behaviors. V. Conclusion and Recommendations The study on behavioral experience and customer expectation among university students in Nigeria resulted in several significant findings. The study concluded that convenience was found to have a positive influence on the customer expectation of university students in Nigeria. The study also concluded that product credibility was found to have a positive influence on the customer expectation of university students in Nigeria. The study pointed out that comparison and choice was found to have a positive influence on the customer expectation of university students in Nigeria. The study also revealed that social proof, such as positive reviews or recommendations from trusted sources can increase customer expectations, while negative reviews or experiences shared by others can lower them. To effectively meet customer expectations, businesses need to conduct market research to understand their target audience thoroughly. Thus, gathering and analyzing customer feedback can help organizations to have a better understanding of customer expectations and identify areas for improvement. This can be accomplished through surveys, focus groups, or observation of customer behaviors. This study fills a significant gap in the literature by comprehensively examining the effects of convenience, product quality, as well as comparison and choice on customer expectations. Previous research has often focused on individual factors in isolation, but this study extends the previous studies by integrating multiple variables to provide a holistic understanding of how these factors collectively influence customer expectations. The findings contribute to the literature by empirically demonstrating the significant impact of convenience, product quality, as well as comparison and choice on customer expectations, thereby rejecting the null hypotheses for each objective. This study's contribution to marketing professionals is substantial, as it provides actionable insights for enhancing customer satisfaction and loyalty. Highlighting the importance of convenience, product quality, as well as comparison and choice results in the study to offer practical strategies for businesses for improving their customer experience and competitive positioning in the market. Based on the findings, the research also presents a variety of recommendations. Firstly, to enhance customer expectations through convenience, Jumia online retail outlet and other stakeholders should streamline their service delivery processes, ensuring ease of access and use. This can be achieved by implementing user-friendly interfaces, efficient customer support, and swift transaction processes to meet and exceed customer expectations. Secondly, to improve customer expectations through product credibility, Jumia online retail outlet and other stakeholders should focus on maintaining high standards of quality control and regularly updating their product offerings based on customer feedback. Additionally, clear and accurate product descriptions, along with reliable customer reviews, can help in setting realistic expectations and enhancing customer trust. Thirdly, to positively influence customer expectations through comparison and choice, Jumia online retail outlet and other stakeholders should provide comprehensive and transparent product information, enabling customers to make informed decisions. Finally, implementing advanced filtering and comparison tools on their platforms can facilitate easier product evaluation and selection, thereby enhancing the overall shopping experience. References Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219-224.https://doi.org/10.1111/1467- 9280.00441 Baumeister, R. F., Vohs, K. D., & Funder, D. C. (2018). Psychology as the science of self-reports and finger movements: Whatever happened to actual behavior? 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