Introduction
Traditionally, consumers bought goods and services using the brick-and-mortar model where people make physical visits to a store and complete a purchase. However, technological advancements in the business world have changed this format of commerce by making it possible for consumers to purchase goods online. This development has eliminated the need for physical contact between buyers and sellers because both parties can interact virtually and exchange money for goods or services that are commonly delivered to their homes (Xiaoping and Tao, 2021). These market changes have heralded a new age of commerce that pivots on digital engagements between businesses and customers.
The e-commerce industry has grown against the backdrop of improved technological use in business. Several trends are responsible for promoting these changes, including the entry of a younger demographic market in the global e-commerce space, increased use of technology in marketing promotions, enhanced rates of internet penetration among communities and a growing middle-class population (Xiao, 2020). The emergence of competition among different multinational companies in expanding their global market outreach has forced players to use web analytical tools to differentiate themselves from the competition (Xiaoping and Tao, 2021). This trend has created a favorable working environment for business and customers to engage with one another virtually. Associated changes are geared towards changing consumer behavior especially in redefining the time and resources they spend towards a specific product or service (Xiao, 2020). Therefore, the process of optimizing customer experiences needs to be designed with the recognition that the e-commerce environment needs to be improved for better user experiences.
Live stream shopping stems from the larger body of online marketing activities that consumers participate in. This type of shopping often involves an influencer promoting a product using social media platforms, such as Facebook, YouTube, or Instagram to a global audience by demonstrating its use in real-time (Influencer Marketing Hub, 2021). Therefore, live streams works in the same way as traditional home shopping television shows do where a person of influence draws the audiences’ attention to a product or service to generate sales (Influencer Marketing Hub, 2021). Based on this account, the difference between traditional television-based promotion campaigns and live stream shopping is the medium of communication.
The latter primarily relies on social media platforms to engage audiences in real time, while television promotion programs could be pre-recorded (Pai, 2021). An additional difference between live stream shopping and traditional shopping relates to the nature of interaction between buyers and sellers. Unlike traditional formats of product promotion, interactions between buyers and sellers in live stream shopping are unedited (Verma and Kumar, 2021). Stated differently, both parties interact in real time and feedback can be obtained by asking questions or commenting on the subject of the promotion campaign at the same time that engagements are ongoing.
Some of the earliest accounts involving the use of live streaming in e-commerce can be traced to the mid-1990s (Influencer Marketing Hub, 2021). However, it is only until the early 2000s that this technique was widely accepted in business (Verma and Kumar, 2021). The recent COVID-19 pandemic has increased the demand for live streaming with more than 98% growth in demand reported in the last two years (Verma and Kumar, 2021). This figure indicates that the live streaming business is worth more than $125 billion (Verma and Kumar, 2021). This platform is expected to grow in dominance as the number of social media users increase around the world.
Cognizant of the new interest on live stream promotion, many e-commerce platforms are integrating this promotion technique in their marketing strategies or core operations. Most of these companies acknowledge the existence of a high potential for exploiting live stream shopping to increase sales and generate interest in new product development (Syed et al., 2018). In addition, they understand that this type of promotion is potentially lucrative because it appeals to a younger generation. Particularly, Generation Z and Millennials have been reported to form the bulk of live stream audiences (Influencer Marketing Hub, 2021). Thus, there is promise that a new generation of customers is likely to embrace this type of marketing promotion strategy. However, it is unclear how its use could alter their behaviors.
Research Problem
Based on the differences between traditional promotion techniques and live stream shopping, attention has been drawn to the varying impacts that both platforms have on markets or audiences. For example, live stream shopping has a higher entertainment value compared to traditional shopping methods (Syed et al., 2018). In turn, its prominence generates interest about a product or service, thereby creating the opportunity for monetization. Based on this background, social media promotion is evolving as new applications are developed and segments of users emerge (Verma and Kumar, 2021). These changes mean the impact that marketing promotions could vary with new developments in social media marketing. Live stream shopping is one of the latest products representing these social media changes. The present study seeks to understand its impact on consumer behavior.
Research Aim and Objectives
The purpose of this study is to understand differences in consumer behavior among live stream shoppers. The investigation is inspired by the lack of adequate research on the impact that live stream shopping has on consumer behavior. The three-stage model of service consumption is the conceptual framework for the study. It explores the influence of social media marketing on consumer behavior based on three stages of purchase experiences – pre-purchase, service encounter, and post-encounter stages. The research questions are based on this framework of engagement and appear as follows:
- In what ways does live stream shopping affect customers’ decisions to buy a product or service?
- What impact does live stream shopping have on customer experiences when making online purchases?
- How does live stream shopping affect services offered to customers after completing a purchase?
Importance of Study
Different companies have adopted live stream shopping in their marketing strategies with varying outcomes. This method of shopping varies with changes in demographic characteristics across different market profiles. For example, live streaming is more commonly used in China compared to the US (Influencer Marketing Hub, 2021). In the populous nation, experts estimate that up to 30% of the country’s population participates in live streaming as active participants or observers (Arcadier, 2021). In Singapore and Malaysia, live stream promotions have increased by more than 200% due to COVID-19 market-related changes (Influencer Marketing Hub, 2021). Sales numbers from the marketing promotion platform have equally increased by 100% due to the pandemic and it is estimated that the figures are likely to increase further with the entrenchment of technology in marketing promotions (Arcadier, 2021). These statistics indicate that live stream shopping has the potential to become the mainstream method of buying goods and services. Thus, it is expected that the findings of this study will be useful in facilitating this transition by identifying core areas of focus.
Literature Review
As highlighted in the first chapter of this dissertation, the aim of this study is to understand differences in consumer shopping behaviors using social media. This chapter contains a review of what other scholars have said or written about the research topic. Its key sections highlight the gap in the literature that justifies the current investigation and key concepts that are linked to the research topic.
Accelerated Digitization in Business
Digitization refers to the use of automated techniques to carry out organizational functions. This concept is included in this review because live stream promotions are products of the digitization trend that stems from e-commerce adoption (Xiaoping and Tao, 2021). The growth of this trend has occurred against the backdrop of a growing realization that brick-and-mortar sales are often time-consuming and more expensive than online shopping methods (Xiaoping and Tao, 2021). Despite the benefits of shopping online, some researchers argue that certain businesses have remained successful by introducing new value prepositions (Verma and Kumar, 2021). For example, an independent study conducted in Stockholm among small and medium enterprises revealed that cultivating in-store consumer experiences, product curation, and integration of local systems with international ones provided the main platform for sustaining brick-and-mortar business in the contemporary business world (Hracs & Jansson, 2020). Broadly, these sentiments point to the growing influence of digitization in democratizing production processes and creating new communication channels for business growth.
The influence of digitization in business operations has increased in the last two decades. The growth and dominance of tech-based companies, such as Amazon and Netflix, highlight the power of digitization in the present business environment. Focusing on Amazon alone, the effects that its online retail strategy has had on the industry is unmatched (Hracs & Jansson, 2020). Netflix has also had a similar impact in the entertainment business and film streaming services. Indeed, it has contributed to the death of traditional entertainment sectors, such as the movie theatre industry (Hracs & Jansson, 2020). The music industry has equally been affected by the same trend because piracy and duplication of music files have transformed how people consume content.
Based on the success of companies that have adopted digitization in their mainstream production processes, several industries have started to generate data relating to the impact that this process has had on their operations (Roy and Srivastava, 2017). Notably, the integration of digitization techniques in corpofrat6e management has been adopted in several industries, including energy, banking, insurance, and telecommunication sectors (Arcadier, 2021). Players in some industries have reported a higher level of digital integration than others have. For example, the tourism and hospitality sector has recorded the highest impact of digital adoption among mainstream global economic business segments (Rui et al., 2021). The success of digital integration has been demonstrated by the increased reliance on automated systems to provide customer services (Roy and Srivastava, 2017). These changes have been associated with improved innovativeness and productivity in the workplace.
Some aspects of food production in the beverage and food industries have been affected by digitization in the same manner as described above. For example, some production processes have been commercialized using robotics (Arcadier, 2021). Additionally, meat-processing industries use automated systems to prepare meat by cutting portions of the product into equal sections. Coffee-making processes have also improved their functionality by using robotics to make products and not people (Arcadier, 2021). The result has been the development of a consistent taste in food – a fete, which has improved service quality. Some of the leading coffee houses, which have adopted this automation technique, include Crown Coffee in Singapore (Roy and Srivastava, 2017). Overall, technology use can help to improve customer satisfaction and operational costs.
Broadly, the above examples demonstrate the power of digitization in the contemporary business world. It is expected to further increase in influence as more businesses and industries adopt technology at various levels of their marketing operations (Rowley and Oh, 2019). The marketing practices of various companies have undergone a period of significant change because of digitization. Particularly, the advent of service 4.0 concept, which promoted higher levels of innovation and productivity have accelerated this trend.
Content Marketing
Studies that have investigated the impact of technology on consumer behavior have highlighted the importance of reviewing content marketing as a new area of digital promotions. This strategy is premised on the idea that successful marketing campaigns are based on the development of relevant content (Al-Kwifi, Farha and Zaraket, 2020). The growth of social media has significantly affected how brands communicate to their customers (Zhang, Jinpeng and Khan, 2020). Relative to this development, content marketing is a relatively new area of academic interest because it is linked with studies, which have investigated the role of social media in influencing consumer behavior (Al-Kwifi, Farha and Zaraket, 2020). Given that social media gives companies the freedom to develop innovative marketing campaigns, content marketing has become an integral part of marketing communications.
The earliest works of literature that have focused on content marketing tried to highlight its unique characteristics and situate it within the contemporary literature of marketing communications. Recently, the discussion has shifted towards portraying content marketing as a digital communications strategy (Meng, 2021). Particularly, most scholars have tried to present content engagement procedures as activities aimed at maximizing consumer engagement and trust (Wang et al., 2020). In this framework of engagement, firms often optimize engagement and trust by developing sustainable marketing campaigns with long-term benefits to a business and its clients.
Marketers have pursued different forms of content marketing strategies to suit various marketing needs and trends. Commonly, most of them follow these marketing strategies to develop content for their websites and blog posts (Al-Kwifi, Farha and Zaraket, 2020). Alternatively, content marketing can be user-generated where comments and feedback form the basis for making marketing policies and campaigns (Sparre, 2020). Alternatively, content marketing campaigns can be implemented as paid media where promotion programs are developed in collaboration with companies or brands to foster engagement (Al-Kwifi, Farha and Zaraket, 2020). Broadly, the link between content marketing and consumer behavior is found in the purpose of the marketing strategy, which is to retain customers and foster brand engagement.
Social media platforms have developed a “creator” economy where marketing campaigns are designed to foster the highest levels of engagement between businesses and their customers. In this regard, given that companies spend many resources to develop content for their marketing campaigns, they need to be wary of the impact that such companies would have on their organizational goals (Mattison and Brouthers, 2021). The failure to do so means that they may lose focus of achieving the overall goals of the firm and instead give attention to the process of maximizing engagements, which is distracting.
Despite the growth in content marketing literature within the past decade, the effects of this type of marketing on consumer behavior have been hampered by scattered results across different segments of the marketing literature. Furthermore, confusion about terminology placement has impeded the ability of researchers to explain the relationship between content development and consumer behavior. For example, the concepts of content and social media marketing have been used interchangeably in marketing literature with mixed results (Bu et al., 2021). In this review, content and social media marketing follow different paths of development, which affect consumer behavior in similar fashion. For example, content marketing is more focused on consumer behavior as opposed to brand engagement, while social media marketing is primarily engaged with consumer engagement first before all other concerns (Bu et al., 2021). Therefore, interchanging both terms is likely to have in impact on consumer behavior. Overall, it is difficult to understand the full scope of the effects that content marketing has on consumer behavior if these aspects of the analysis are ignored or under explored.
Factors Affecting Consumer Behavior
Social media have introduced new features in virtual communications with the potential to alter consumer behavior. The ability to provide real-time communications and ease of conveying anonymous feedback are some key features of social media communication, which have affected how people interact with one another (Patey, 2021). Furthermore, the boundless nature of social media communication means that companies are interacting with a global audience, as opposed to a local one. Broadly, these features may alter the limits of consumer behavior if compared to the foundations of traditional communication systems between companies and their customers.
As highlighted in this study, companies often attempt to influence consumer behavior to draw attention and resources from the market. Firms use different techniques to achieve this goal. The academic interest in this area of study has investigated consumer behaviors from the perspective of online consumer intentions (Beeler et al., 2017). Related themes that have been discussed include understanding the emotional impact of online shopping on consumer behavior and assessing the impact that these changes have on sales (Marsden and Henig, 2019). Studies reveal that consumer behavior is affected by complex goals and objectives (MacKay, Chia and Nair, 2021). This line of research has equally shown that consumer behavior is driven by internal shopping values held by a customer and the conditions for completing online transactions.
The trust between consumers and companies is a common theme of discussion among scholars who have discussed the impact of marketing communication systems on consumer behavior. Trust is an important feature for moderating consumer purchase intentions because of the myriad of challenges that consumers encounter when purchasing goods online. Thus, scholars consider trust an important predictor of corporate success (Wang et al., 2022). Based on this relationship, scholars have focused on understanding the link between consumer trust and purchase intentions (Wang et al., 2022). These purchase intentions are considered the building blocks to understanding consumer behavior.
Hypotheses Development
Three hypotheses will be tested in the present investigation. They stem from the research questions outlined in this study and are guided by the three-stage model of service consumption. The model focuses on a three levels of consumer purchasing experiences – pre-purchase, service encounter, and post-purchase stages. The hypotheses underpinning this study are as follows:
Pre-purchase Experiences: Customers often go through a range of emotions throughout their purchasing process. Pre-purchase customer experiences refer to the set of emotions they experience before making the decision to buy a product (Latif et al., 2019). These feelings are developed when customers conduct prior market research or when they are seeking recommendations from their friends about the best products to buy (Beeler et al., 2017). Stemming from this background, current research studies indicate that social media marketing enhances consumer pre-purchase experiences (Kornberger and Mantere, 2020). Moderating consumer-purchasing experiences using algorithms and machine learning techniques helps to temperate such outcomes (Jiang and Kim, 2020). In this analysis, attention is drawn to the fact that live stream shopping involves an influencer who then attracts an audience big enough to have a sales impact. Stemming from the positive influence that social media marketing methods have had on consumer engagement, it is hypothesized that live stream shopping will enhance consumer pre-purchase experiences.
(Hypothesis 1 – Live stream shopping enriches consumer pre-purchase experiences)
Service-Encounter Experiences: Service encounter experiences refer to feelings that customers experience while in the process of making a purchase. Scholars who have explored the range of emotions that customers experience in this stage of the buying process suggest that successful influencers can create positive feelings among their audiences, including evoking passion, fantasy and pleasure (Xu et al., 2021). Similarly, successful influencers may be persuasive and encourage their followers to absorb a wealth of product information to facilitate a sale. Based on these findings, we hypothesize that live stream shopping enhances service encounter experiences.
(Hypothesis 2 – Live stream shopping enhances consumer service-encounter experiences)
Post-purchase Customer Experiences: As its name suggests, post-purchase customer experiences refer to the range of emotions that customers encounter after making a purchase. Current literature suggests that in a networked environment, customers are likely to stimulate cognitive and affective feelings (Xu et al., 2021). Particularly, scholars note that environments where companies have a high social presence are likely to increase perceived usefulness, thereby evoking positive consumer attitudes (Beeler et al., 2017). Based on this assessment, as demonstrated below, it is hypothesized that live stream shopping improves post-purchase customer experiences.
(Hypothesis 3 – Live stream shopping improves post-purchase customer experiences)
Conceptual Framework
The three-stage model of service consumption is employed as the conceptual framework for the current investigation. It suggests that consumers go through three types of experiences when making a purchase – pre-service, service encounter, and post-service (Latif et al., 2019). The pre-service stage is characterized by a heightened level of need awareness, information search, evaluation of alternatives, and considerations involved with making decisions about product purchases.
Comparatively, the service encounter stage differs from the pre-service phase because it involves requests of service provision from known suppliers and delivery personnel (Beeler et al., 2017). Furthermore, at the service encounter stage of interaction, several delivery interactions happen (Latif et al., 2019). They may occur on two levels because there is a low frequency stage where low-contact services are harbored and a high frequency wave length where integrative models of services are accommodated (Latif et al., 2019). The level of interaction between buyers and sellers influences the type of frequency to be adopted.
The post-encounter service stage is the last of the three stage model. It involves an evaluation of service performance and future intentions. Key concepts that are reviewed at this stage of analysis include customer satisfaction and an integrated model of service consumption (Beeler et al., 2017). Overall, the three-stage model of service consumption outlines a series of stages where the researcher will explore consumer feelings as they are moderated by live stream shopping influences.
Summary
This literature review shows that live stream shopping is the most interactive and new way of shopping. However, there is little academic focus on this area of study because the present literature has generalized its impact with social media promotion campaigns. However, as demonstrated in this study, the impact of live streaming on consumer behavior will be examined in the present investigation. The current study seeks to fill this research gap by focusing on live streaming as an integral part of social media marketing campaigns.
Methodology
As highlighted in the first chapter of this dissertation, the aim of this study is to understand differences in consumer shopping behaviors based on the influences of live stream shopping. To recap, the current investigation seeks to answer three fundamental questions relating to the use of live stream shopping in marketing communications. The first question seeks to find out ways that this type of promotion affects customers’ decisions to buy a product or service. The second line of probe focuses on understanding the impact of live stream shopping on customer experiences when making online purchases. Alternatively, the third level of questions seeks to find out how live stream shopping affects the quality of services offered to customers after completing a purchase. This chapter highlights strategies that the researcher followed in answering these research questions.
Research Philosophy
Research investigations are often developed with a specific world-view to contextualize findings. Relative to this assertion, Stokes (2017) says four types of philosophies underpin research investigations – positivism, interpretivism, realism, and pragmatism. Positivism is associated with the reliance on facts, as opposed to subjective variables, to make conclusions about a research topic. Comparatively, as its name suggests, the interpretivism research approach relies on subjective elements of a research to make conclusions about a specific topic. Stated differently, this research philosophy integrates human interest in the interpretation of research findings (Saunders, Lewis and Thornhill, 2019). Alternatively, the realism research philosophy seeks to disassociate reality from human reasoning. The goal is to increase the objectivity of analysis and present findings that are independent of human limitations in reasoning. Comparatively, the pragmatism research approach differs from the above-mentioned philosophies because it is guided by the quest to find the most practical way of meeting the objectives of a study (Patten and Newhart, 2017). Therefore, above all concerns, the pragmatism research philosophy is focused on finding the best strategies to use in completing the objectives of a study.
Based on the unique characteristics of each of the research philosophies mentioned above, the interpretivist research philosophy was employed in the present study. The justification for its use is enshrined in the nature of the research topic, which focuses on understanding consumer behavior. As alluded in this paper, behavior is a subjective concept and its association with live stream shopping is conceptualized within this subjective framework. Relative to this assertion, this research approach is selected for use in the current investigation because it allows researchers to observe behaviors and make inferences about them based on their respective study contexts (Raju and Prabhu, 2019). This research philosophy is adopted in the current investigation because behavior is a complex concept that demands an interpretive understanding of its impact on consumers.
Research Method
The content of research studies often vary with the kind of materials available. In academic studies, two major research methods are applicable – qualitative and quantitative. Qualitative research involves the use of data collection instruments to measure variables that are assessed subjectively (Small and Mardis, 2018). Comparatively, quantitative research involves research investigations that have variables, which can be measured numerically. The current investigation employed both research methods to create a broader mixed methods framework of investigation. In this structure, both qualitative and quantitative aspects of the review were assessed. The mixed methods framework was included in the present investigation because it contains both qualitative and quantitative parts.
The qualitative nature of the research investigation was represented by the quest to measure consumer experiences. Given that people could develop different accounts of experiences when subjected to the same stimuli, it was difficult to employ one standard measure to assess this variable. Consequently, it was important to categorize such information as qualitative data and treat it as such (Prasad, 2017). The quantitative aspect of the current investigation was employed because of the need to understand the informants’ responses. Graphs, tables, and figures were deployed to meet this goal and the findings were presented using the same metrics of assessment. Therefore, it was important to use the mixed methods framework to carry out investigations in the study.
Research Design
The researcher employed the case study research design in the current study to investigate consumer behavior in online shopping. The case study technique was adopted because it provided a sample set of views that the researcher could use to develop a broader set of findings (Melnikovas, 2018). In the current investigation, the case study for the present investigation was domiciled in the analysis of consumer behavior using two social media lives streaming platforms – Instagram and Tik Tok. The case study approach uses these two social media channels to investigate consumer behavior. Therefore, the technique was employed to provide a basis for making inferences about consumer behaviors.
Sample Population
The researcher sampled the views of 178 participants who were students enrolled in several educational programs within a learning institution. As a criterion for inclusion, the selected participants were Instagram or Tik Tok users. Those who do not have an online purchasing experience on these platforms were excluded from the investigation. The participants wre recruited using the purposeful sampling method, which enables researchers to use their discretion to identify informers with unique sets of knowledge about a specific area of research (Nosiri, 2019). Thus, this technique was instrumental in helping the researcher to find informants who have bought goods or services using live stream shopping (Kim et al., 2018). The purposeful sampling method was useful in identifying participants with these unique characteristics (Zapata-Barrero and Yalaz, 2018). Therefore, the justification for using it was embedded in the search for informants who have used live stream to purchase goods online.
Research Instruments
Primary data was obtained from the respondents using structured questionnaires. These instruments of data collection were categorized into two sections. The first one sampled the demographic characteristics of the respondents, including their age, gender, highest education qualifications, and years spent shopping using live stream. The second part of the survey sampled the views of the respondents to understand their shopping experiences. The researcher measured their sentiments using the five-point Likert scale.
This tool capturers the intensity of people’s feelings using five levels of assessment, which include “strongly agree,” “agree,” “neither agree nor disagree,” “disagree,” and “strongly disagree” (Thompson et al., 2021). These five levels of sentiments expressed by the likert scale provided grounds for sampling different levels of intensity or feelings that the respondents had towards the questions posed. The researcher was interested in identifying and evaluating the respondents’ sentiments based on three levels of service experience – before, during, and after purchases. These levels of analysis are related with the three-stage service model of review highlighted in this document. Each level of service experience had four questions attached to them – each seeking to investigate the views of respondents on one area of application. Overall, the questionnaire had 12 sentences in the second part, meaning that each of the three sections had four statements.
Data Analysis
The researcher analyzed primary data collected from the respondents using the Statistical Package for the Social Sciences (SPSS) software – version 25. This statistical analysis tool was employed in the study because it is a comprehensive instrument for analyzing large volumes of data. Scholars have used it to scrutinize data in several social studies with impressive results (Hela, 2021). The descriptive and inferential data analysis tools from the SPSS software were used to analyze the findings. The five levels of responses highlighted above were later coded with the numbers, 1,2, 3, 4, and 5 to represent feelings of “strongly agree,” “agree,” “neither agree nor disagree,” “disagree,” and “strongly disagree” respectively in SPSS. This data was useful in coding the findings for further assessment using the Statistical packages for the Social Sciences (SPSS) software in the above-mentioned descriptive and inferential data analysis sections.
The descriptive section of the analysis provided information relating to the demographic characteristics of the respondents, using means, frequencies, and standards of deviation. The relationships among these variables were later assessed using the inferential data analysis technique. Particularly, the one-way ANOVA method was employed to evaluate the connection between different sets of variables (Allibang, 2020). Broadly, the descriptive and inferential data analysis techniques provided the framework for analyzing the primary data,
Ethical Implications of Study
It is important to understand the ethical impact of a research study if human subjects are involved as participants. This recognition of importance stems from the fiduciary duty of researchers to “do no harm” to their informants (Temple, 2019). Therefore, this duty is ingrained in the quest for researchers to protect their informants from threats that could jeopardize their safety or the integrity of the study. Based on this background, three ethical concerns were addressed in the study.
Confidentiality and Anonymity of Respondents: Respondents who take part in studies often run the risk of having their personal information disclosed to the public without their consent (Rael, 2017). The researcher mitigated this risk by reporting the findings anonymously. Therefore, no personal identifying information, such as names, courses studied, or year of study were included in the investigation. The goal of doing so was to maintain objectivity in the investigation.
Withdrawal from Study: All participants who took part in this investigation did so without being paid or coerced. Stated differently, their involvement in the research was because of their free will. Therefore, those who wished to exit the study had the freedom to do so at any point in the investigation and without any consequence, as recommended by Osafo, Paros and Yawson (2021). The goal of creating an environment of free consent is to obtain authentic data that would present an accurate picture of shoppers’ experiences using live stream shopping.
Treatment of Data: Procedures followed in the treatment of data are consistent with the goal of maintaining the confidentiality of findings by protecting the informants’ safety. To meet this goal, the researcher stored all the data collected in the investigation in a computer and secured it using a password. After completion of the study, this information will be destroyed to prevent future unauthorized access.
Reliability and Validity of Findings
The reliability of a study refers to the ability of a researcher to arrive at the same findings when using the same research methods at a different point in time. As highlighted above, the researcher collected data using the questionnaire survey method. To make sure the findings published met this threshold of proof, the researcher ensured that the information provided in the study was consistent with the views of the informants. This goal was achieved by employing the member-check technique, which involves sharing the findings of a study with users to make sure there is consistency of thought between their views and those expressed in the study (Lise and Jones, 2019). Additionally, the researcher made follow-ups using phone interviews and email communication. Therefore, the member-check technique provided the framework of safeguarding the reliability of the study.
Findings and Discussion
This chapter summarizes the findings obtained from implementing the strategies highlighted in chapter 3 above. To recap, the researcher collected primary data using surveys and sampled the views of 178 respondents who gave their views regarding their shopping experiences using live streams. The findings are classified into two categories – the first one contains descriptive information about the study’s variables and the second one holds inferential data relating to consumer behavior.
Descriptive Analysis
The descriptive analysis section appeared in the first part of the survey questionnaire. It sampled the researchers’ unique characteristics, including their gender, age, history of social media use, preferred social media platform, and education qualifications. The findings relating to each of these demographic variables are highlighted below.
Gender
According to Table 4.1 below, most of the respondents who took part in the study were female. They accounted for 63.5% of the total sample, while men made up 36.5% of the same populace. This finding is consistent with studies, which suggest that a higher proportion of Instagram and TikTok users are female (Heindl, 2021).
Table 4.1: Distribution of respondents according to gender
Age
Age was the second demographic variable sampled in the study. According to Table 4.2 below, 73.6% of the participants were aged between 18 and 30 years. This finding represents the average age of college students in campus. Therefore, it was consistent with the demographic profile of students in a learning institution. Alternatively, respondents, who were aged between 50 and 60 years old formed the smallest group of informants because they only accounted for 1.1% of the total population.
Table 4.2: Distribution of respondents according to age
Education Qualifications
The education qualifications of the informants were another demographic variable sampled in the investigation. Table 4.3 below shows that most of the informers listed high school as their highest education qualification. Comparatively, the lowest number of respondents who took part in the investigation were those who had a “PhD or higher” degree.
Table 4.3: Distribution of respondents according to education qualifications
The above-mentioned findings mean that most of the respondents who took part in the investigation had a low education qualification.
History of Live Stream Use
It was important to capture the respondent’s history of live stream use to determine whether it affected the quality of their responses, or not. In this study, this history of live stream use will be used to predict future behaviors. According to Table 4.4 below, most of the participants claimed to have used live stream in the last 2-5 months.
Table 4.4: Distribution of respondents according to history of social media use
Those who had used the platform for less than 2 months formed the smallest group of respondents. Alternatively, those who had used live streaming for between 5 and 10 months formed the highest number of participants. These statistics means that most of the respondents had significant experience with live-stream shopping.
Income
The respondents’ income was the last demographic variable sampled in the study. According to Table 4.5 below, most of the informants (66.2%) who participated in the study had an income of less than $10,000 per year. They represented 63.3% of the total sample. Comparatively, those who earned more than $40,000 annually formed the smallest sample of respondents at 1.9%.
Table 4.5: Distribution of respondents according to income
(Please state your income)
Broadly, the high concentration of participants in the “less than $10,000” income group is not surprising because most of the informants were college students. Consequently, it could be assumed that they have not started their careers yet and thus earn limited money. The effects of their financial power on their shopping behaviors will be explored in subsequent sections of this analysis.
Consumer Experiences
The second part of the questionnaire sampled the respondents’ views regarding different live stream shopping platforms. To this end, the researcher asked the respondents to state their preferred social media platform and the findings appear below.
Preferred Social Media Platform
Table 4.6 below demonstrates that most of the informants preferred to use Instagram as opposed to Tik Tok. The percentage of respondents who selected Instagram as their main social media marketing took was 73.6% of the total sample. Comparatively, those who preferred to use Tik Tok formed 36.4% of the total sample.
Table 4.6: Distribution of respondents according to preferred social media platform
The views of the respondents highlighted above were further examined based on the experiences garnered on the preferred social media platform. This analysis was undertaken by examining their pre-purchase, service-encounter, and post-purchase experiences.
Pre-Purchase Experience: Table 4.7 below presents the results of the combined experiences of both Tik Tok and Instagram users. The first two columns (pre-purchase experiences 1 and 2) related to the views of Instagram users, while the last two columns were those of Tik Tok users.
Table 4.7: Effects of live stream shopping on consumer pre-purchase experiences
The findings highlighted above demonstrate that the views of both sets of respondents were similar because they had common scores on the same statements. This conclusion was reached after reviewing the average mean for the four items listed above. None of the two sets of respondents posted a mean that was above 1.8 points. This finding means that the respondents shared similar pre-purchase experiences.
Service Encounter Experiences: The service encounter stage of the purchasing process involved an examination of the experiences of Instagram and Tik Tok users when buying products using live stream.
Table 4.8 below captures the views of the respondents. Again, the first two columns (service encounter experiences 1 and 2) represented the findings of Instagram users and the last two reflected those of Tik Tok users. Based on the cumulative findings depicted below, it can be assumed that both sets of consumers shared similar service encounter experiences. This statement is supported by the average mean of their findings, which was less than 3 for both sets of users. This finding means that there was no significant variation in the service experiences.
Table 4.8: Effects of live stream shopping on consumer service encounter experiences
Post-Encounter Experience: The last area of assessment relates to the post-encounter experiences of the respondents when they used Instagram and Tik Tok for live stream shopping. The findings were consistent with those of the pre-encounter and service encounter stages because both sets of consumers shared similar post-encounter experiences. According to table 4.9 below, this statement is derived from the average mean of the respondents, which was 2.3. The tendency for both Tik Tok and Instagram users to share a convergent outcome means that there was an insignificant variance in experience for both sets of consumers.
Table 4.9: Effects of live stream shopping on consumer post-encounter experiences
Overall, the above-mentioned findings indicate that live stream shopping had an impact on consumer behavior and purchasing choices. In the inferential analysis section below, these findings are assessed based on whether the demographic characteristics of the participants affected their views on the research topic, or not.
Inferential Analysis: Relationship between Gender and Consumer Behavior
The first part of the inferential analysis sought to examine the relationship between gender and consumer behavior. According to Table 4.10 below, this variable did not have an impact on the findings because the significance value for each of the variables failed to meet the p>0.05 significance threshold.
Table 4.10: Relationship between Gender and Consumer Behavior
Thus, the above-mentioned findings suggest that despite the high presence of female respondents in the study, gender did not affect their perceptions of live stream shopping and consumer behaviors.
Relationship Between Age and Consumer Behavior
The relationship between age and consumer behavior was examined in the current investigation using the same technique as those implemented above. Indeed, Table 4.11 below suggests that this value had an insignificant impact on consumer behavior because none of the statements answered met the significance value of p>0.05.
Table 4.11: Relationship between age and Consumer Behavior
The above-mentioned findings mean that despite the population of young respondents being significantly higher than those of elderly informants are, age did not affect the findings of the study. In other words, the views of both the young and elderly were consistent with the findings.
Relationship Between Education Qualification and Consumer Behavior
The relationship between education qualifications and consumer behavior formed part of the current probe because the researcher intended to find out if the educational differences of the respondents prompted them to make purchasing decisions. Table 4.12 below represents the findings and they indicate that this variable did not have a significant impact on the findings because their significance levels did not meet the p<0.05 threshold.
Table 4.12: Relationship between education qualifications and consumer behavior
Relationship Between Preferred Social Media Platform and Consumer Behavior
As highlighted in this document, the respondents were Instagram or Tik Tok users. The findings generated from their participation showed that more Instagram than Tik Tok users used live stream shopping. Table 4.13 below suggests that the impact of social media preference for either of the two platforms on consumer behavior was low. Similar to the findings generated from the analysis of the above-mentioned findings, those relating to the respondents’ social media platform differences equally had an insignificant cost on consumer behavior. This finding was achieved after analyzing the significance value of the statements given by the respondents, which did not meet the p>0.05 threshold.
Table 4.13: Relationship between preferred social media platform and consumer behavior
Relationship Between History of Social media Use and Consumer Behavior
The history of social media use and consumer behavior was equally investigated in the current investigation. According to the findings highlighted in Table 4.14 below, this demographic variable did not have an impact on the overall findings of the study. This statement means that the respondents’ history of social media use did not affect their purchasing behavior during online streaming shows.
Table 4.14: Relationship between history of social media use and consumer behavior
Impact of Income on Findings
As highlighted in this chapter, most of the respondents who took part in this investigation had an income of less than $10,000 per year. Table 4.15 below states the findings obtained after assessing the impact of this variable on the respondents’ views.
Table 4.15: Impact of income on findings
The findings mentioned above reveal that income affected the consumer behaviors of the respondents. This finding was derived after reviewing the significance values of the three variables in the study. Two out of the three variables met the significance threshold of p<0.05. Therefore, it could be assumed that income potential affects consumer behavior.
Analysis
Overall, live stream shopping emerged as a unique social media platform for promoting products because of its interactive nature. It provides new ways of engaging with customers, thereby strengthening the quality of business-customer relationships. The desire to use its interactive features is based on its multifaceted and engaging nature. Indeed, studies indicate that live stream interactions are engaging and interactive (Hartley, Montgomery and Siling Li, 2017). This attribute makes it possible to create awareness regarding the latest brands in the market and an opportunity to interact with them before purchase.
The three-stage model framework suggests that the physical stimuli consumers are exposed to before buying a product or service are likely to influence their purchasing decisions. Therefore, the physical characteristics of products that are presented to customers, such as their color, aroma and illumination are likely to have a significant impact on pre-purchasing decisions. These findings are consistent with those of García-Canal et al. (2018), and Ganamotse et al. (2017). This statement indicates that an individual’s quality of virtual environment affects their behavioral responses.
Particularly, the research evidence developed in this study indicates that the emotional states consumers develop in their initial contact with a company and is likely to affect their subsequent purchasing behaviors. Feelings of pleasure and arousal may mediate these feelings (Ganamotse et al., 2017). The virtual purchasing environment encountered by consumers equally mediates these outcomes. Overall, the nature of the relationship between live stream use and consumer behavior can be depicted as follows:
Alternatively, the effects of live streaming on consumer behavior can be explained using the social presence theory. It suggests that consumer behavior is altered by the proximity that people have towards action (Zhang, Wang and Zhang, 2021). When analyzed within the context of this study, it is established that live streaming is impactful to consumers because it makes them feel as though they are “already there” with the seller. This action is made possible because live stream supports the instant transmission of images and sounds to audiences, thereby promoting its acceptance and use. Research studies suggest that the influence of a streaming platform on consumers emanates from the fact that it affects how people understand content (Zeithaml et al., 2020). The social presence theory argues that the degree of a person’s involvement in marketing promotions, irrespective of the degree of association is likely to affect their behavior (Zhang, Wang and Zhang, 2021). Thus, it could be argued that live streaming has a significant impact on consumer behavior.
Summary
Three hypotheses were tested in this study. The first one claimed that live stream shopping enriches consumer pre-purchase experiences, while the second one proposed that live stream shopping enhances consumer service-encounter experiences. The third premise was that live streams improve post-purchase consumer experiences. These three hypotheses were confirmed in the current investigation. This finding is consistent with the existing body of literature, which has affirmed the positive impact of social media on business-customer relationships.
Conclusion and Recommendations
As highlighted in the first chapter of this dissertation, the aim of this study was to understand the impact that live stream shopping on consumer behavior using Tik Tok and Instagram as case studies. The investigation sought to answer three fundamental questions relating to the impact that live stream shopping has on consumer behavior. The first question focused on understanding the impact of live stream shopping on customers’ decisions to buy a product or service. This area of the probe represented the first layer of analysis according to the three-stage service model, which was the pre-experience stage of online shopping. The other two stages are service encounter and post-experience shopping. The second line of probe focused on understanding the impact of live stream shopping on customer experiences when making online purchases. This area of the analysis was associated with the service encounter process of online shopping. Alternatively, the third research sought to find out how live stream shopping affects services offered to customers after completing a purchase.
The pre-experience stage of shopping was affected by live stream shopping behaviors because it enhanced consumer interest in products and services. The service encounter stage similarly registered a positive impact of consumer behavior on live stream shopping and the results were replicated in the post-encounter stage. Broadly, these findings affirm the three hypotheses underpinning this study and indicate that live stream shopping is a powerful tool for influencing consumer behavior. This finding id affirmed because elements of consumer trust and interest are heightened when this technique is used in marketing promotion. Therefore, it forms the basis for supporting its use in the corporate sector.
Recommendations
The findings of this study indicate that live stream shopping has an impact on consumer behavior. Notably, this study has used the three-stage service experience model to come up with the findings where consumer behavior has been assessed based on its effects on consumer pre-purchase, service encounter, and post-encounter stages. These findings were developed by sampling the views of college students about their online shopping experiences. Future research studies may focus on a different sample population to investigate whether the same findings will be replicated, or not. Particularly, these studies should be contextualized in a non-institutionalized setting to investigate whether the same conclusions will emerge.
Based on the increasing prominence of live streaming as a mainstream form of promotion, it is important for companies to integrate this digital communication tool in their marketing plans. Tik Tok and Instagram, which are highlighted in this study, have shown the potential in keeping consumers engaged. The findings of this study equally affirm that consumer behavior is likely to be affected by the same marketing communication platform. Given the growing rate of internet penetration in various parts of the world, it will become increasingly imperative that consumers adopt live stream shopping techniques in their marketing campaigns. The interactive nature of the engagement platform is likely to generate the earliest interest in this area of research. Therefore, corporate firms, especially those of an international stature should consider using this promotion strategy in their business strategies.
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