Abstract
Purpose
The purpose of this study is to examine the variables affecting college students’ consumption behaviour in the context of the internet celebrity economy and the We Media environment.
Design/methodology/approach
In this study, five independent variables − perceived ease of use, perceived usefulness, attitude, We Media environment and internet celebrity marketing, as well as one mediating variable, consumption intention, are used to analyse college students’ consumption behaviour.
Findings
This study concluded that all five independent variables have positive relationships with the consumption intention and that the consumption intention also has a positive relationship with the consumption behaviour.
Originality/value
This study expanded the technology acceptance model and theory of planned behaviour model, which could provide insights for future research on consumption intention and behaviour. In addition, this study gives guidance for businesses considering to join this new industry in the internet celebrity economy.
Keywords
Citation
Moorthy, K., Runxuan, L., Chun T'ing, L. and Jing Yii, K. (2024), "College students’ consumption behaviour in internet celebrity economy under We Media environment: a study among Malaysian and Chinese students", Journal of Responsible Production and Consumption, Vol. 1 No. 1, pp. 62-80. https://doi.org/10.1108/JRPC-09-2023-0002
Publisher
:Emerald Publishing Limited
Copyright © 2024, Krishna Moorthy, Lin Runxuan, Loh Chun T'ing and Kwang Jing Yii.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
The recent development of mobile internet and mobile intelligent terminals which are evolving quickly and gaining popularity has resulted in the increase of mobile commerce activities and the rise of new media forms. The emergence of “We Media” allows each netizen to actively share information in this context, which is a shift from just passively consuming information. Shane Berman and Chris Willis of the American Journalism Institute initially coined the phrase “We Media” in a 2003 study paper (Yang and Zhang, 2021). A more precise definition was then put out by the American technology journalist Dan Jimmer: personal media with the trend of blogging is also known as “We Media”. In other words, “We Media” refers to a method through which individuals or groups of individuals, use technology tools and communication platforms of new media, to create and disseminate content to target audiences or a broader audience of prospective consumers. From the perspective of consumers, social media marketing can quickly draw attention to a particular brand, increase trust and loyalty and help consumers make better decisions. As a result, in the age of social media, consumer behaviour has also been substantially affected. Starting with the characteristics of consumer behaviour in the We Media environment (Zhang, 2018), the value production and realisation in each step of the consuming decision-making process are thoroughly investigated, to provide references for market growth in the We Media era.
College students raised in the new millennium are very much internet dependent. The internet has had a significant impact on their way of thinking, work habits and value development. Consequently, they have different manner of thinking with original viewpoint, and their behaviours are also more autonomous. Due to good living environment coupled with high quality and level of education received, college students can access new information easily and master cutting-edge technologies quickly. Therefore, the emergence of the internet celebrity economy swiftly draws in a sizable proportion of college students. With the general rise of education levels, college students will eventually replace high school students as the majority of customers in the future.
Problem statement
With the progress of society and the development of science and technology, consumption concepts and habits have changed significantly. Thus, it is necessary to continuously innovate and improve the study on influencing factors of consumption behaviour. In recent years, the rapid rise of internet celebrity economy not only changes business models tremendously, but also provides a new marketing model. The emergence of this new marketing model will inevitably affect people’s consumption behaviour and habits (Hua et al., 2022). However, after a detailed review of prior literature, the researcher found that there is scarce research on the influencing factors of college students’ consumption behaviour in the internet celebrity economy under the environment of We Media. The consumption habits of college students, who are constantly at the forefront of fashion and technology, are becoming more and more representative (Li, 2022). Thus, it is necessary to research on college students’ consumption behaviour in the internet celebrity economy under the We Media environment.
Research objectives
The general objective of this study is to examine the factors influencing college students’ consumption behaviour in internet celebrity economy under We Media environment. The specific objectives of carrying out this research are:
To find out the relationship between perceived ease of use (PEOU) and consumption intention.
To investigate the relationship between perceived usefulness (PU) and consumption intention.
To study the relationship between attitude (ATT) and consumption intention.
To consider the relationship between We Media environment (WME) and consumption intention.
To investigate the relationship between internet celebrity marketing (ICM) and consumption intention.
To examine the relationship between consumption intention (CI) and behaviour.
It is expected that this research will give a good model fit for the research framework.
Constructs used in this study and their importance
In the literature, research on consumption behaviour in We Media environment is relatively rare. In this study, five independent variables − PEOU, PU, ATT, WME and ICM, as well as the mediating variable, CI, are used to analyse college students’ consumption behaviour. Perceived ease of use and perceived usefulness were adopted from technology acceptance model (TAM); attitude, consumption intention and consumption behaviour were adopted from the theory of planned behaviour (TPB); and We Media environment and internet celebrity marketing variables were added in the research model to study their relationship with the consumption intention. No other study has combined these two theories with two additional variables to study the college student’s intention and behaviour under We Media environment. It is necessary to do a research concerning the college students’ consumption behaviour in the internet celebrity economy under the We Media environment. Without the study, there would be a knowledge vacuum about the elements that influence consumption behaviour in the internet celebrity economy.
Scope of the study
The scope of this study is about college students in Malaysia and China for the period of 2022. The reason for researcher to choose college students as the target respondents is firstly, with the continuous improvement of human culture level, college students are the foundation of future social development, and a universality of conclusions can be drawn. Secondly, college students have a stronger ability to accept and explore, and emerging industries and business models have a significant impact on them. Since the rise of internet celebrity economy is still short, college students are selected as the research target to study the influencing factors of consumption behaviour in internet celebrity economy and the conclusions drawn from the study will be more scientific. Malaysia and China are chosen because college students from these two countries are more available for the researchers, and thus, questionnaires are easier to be collected. As for the period, choosing the latest year is considered to be more scientific and convincing for the study of current consumption behaviour.
The rest of the paper is organised into literature review of the theories, variables and hypotheses, research methodology, data analysis, findings, discussion on findings, theoretical and managerial contribution, limitations and recommendations.
Literature review
Keywords definition
College students: College students are a group of people who have not graduated from basic higher education and professional higher education or who have graduated from higher education and entered the society.
Consumption behaviour: Consumption behaviour derived from consumer behaviour, a study of persons, groups or organisations and all behaviour related to the purchase, use and disposal of goods and services. It is the study of how a person’s emotions, attitudes and preferences affect their choice to make a purchase. The study of consumer behaviour originally emerged in the 1940s and 1950s as a distinct marketing subdiscipline, but it has since become an interdisciplinary area that integrates elements of psychology, sociology, social anthropology, anthropology, ethnography, marketing and economics (especially behavioural economics).
We Media: “We Media” refers to the new media that disseminates standard and non-standard information to a not-specified majority or specific person through contemporary and electronic methods and does so with privatisation, plebification and autonomisation. Shayne Bowman and Chris Willis co-authored a study paper on “We Media” in July 2003 that was released by the Media Center of the American Press Institute. It provided a fairly precise description of We Media as follows: We Media is a way for the general public to begin understanding how, after being digitally improved and linked to the global body of information, the general public can create and share their own facts, their own news.
Internet celebrity economy: The business network of internet celebrities is referred to as the “internet celebrity economy” and it consists of platforms, agencies, support groups, online marketing tactics and incubators. Through targeted marketing that uses stylish internet celebrities as its image spokesmen and their aesthetic inclinations as guiding elements, the internet celebrity economy turns admirers into buyers.
Theoretical foundation
The TAM and TPB models have been extensively used in research on the elements that influence consumer behaviour. Hence, this study also proposes to use these theories (Yulistia, 2017; Ardiyanto and Kusumadewi, 2019; Hansen et al., 2004).
Technology acceptance model
TAM has been effectively used as a theoretical framework to anticipate online shoppers’ intentions and behaviour within this established range (Gefen et al., 2003a; Gefen et al., 2003b). Davis (1985) first proposed TAM as an adaptation of the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975). The TRA, which is a modification of TAM, proposes two ideas as predictors of attitudes towards behavioural intentions and usage of information technology: perceived utility and perceived ease of use. According to the TAM hypothesis, information technology utilisation results from behavioural intentions (Davis et al., 1989). This study used the basic concept of TAM, namely, perceived ease of use, perceived usefulness and external variables, to explain the factors that influence college students’ consumption behaviour in internet celebrity economy towards consumption intention. The original TAM Model is represented in Figure 1.
Theory of planned behaviour
Similar to TAM, Ajzen (1991) added a new element called “perceived behavioural control” to the TRA to create TPB. According to the availability of resources and opportunities, perceived behavioural control reflects how easy or difficult it is to carry out a behaviour (Ajzen, 1991). According to TPB, “attitude”, “subjective norms” and “perceived behavioural control” all have an impact on a consumer’s “behavioural intention”. Empirical research has demonstrated the suitability of this model for analysing customer behaviour in the context of online shopping (George, 2004; Hansen et al., 2004). TRA and TPB models were put to the test by Hansen et al. (2004), and the findings indicated that TPB could explain consumer behaviour more effectively than TRA. This study used the basic concept of attitude from TPB to explain the factors that influence college students’ consumption behaviour in internet celebrity economy towards consumption intention. Attitude being the strongest predictor in TPB, is only adopted as an independent variable. The original TPB model is shown in Figure 2.
The current developments in We Media environment
At the beginning of 2018, the Huffington Post announced the closure of its blog operation, a media product based on its online platform. Many people are wondering why the company announced the closure of its blog platform at this time. Jerome, a new media observer, has the following explanation: In the American society, the threshold of the content-oriented media platform is very low, and the content verification standard is very low when the media release information. Such a loose environment has created a large number of media platform derivatives. From the perspective of the media market, We Media have a large number, disorderly management and great content homogeneity. The current media market presents an oversupply. Digital media seems to be improving technologically, but the overall trend is declining. Almost all network companies have developed their own media platforms by means of technology, and the content and advertisements are monopolised by the powerful. The minority We Media market is walking on thin ice, and it is extremely difficult to survive (Li, Zou and Yang, 2019).
Internet celebrity economy in the environment of “We Media”
The growth of network technology is inextricably linked to the marketing strategy of internet superstars. Based on how it has changed over time, the internet celebrity economy can be split into three eras: the age of text, the age of visuals and the age of internet celebrities. In the early days of the internet, there were early internet celebrities. Before 2014, internet use was less widespread, and content communication was mostly verbal. Some original authors earned a lot of admirers by using cutting-edge language, and they made money by selling books online. The business model of internet superstars has changed dramatically under the new media development paradigm.
Review of previous studies and hypotheses development
Zhang et al. (2021) conducted a study about consumers’ purchase intentions under e-commerce live mode. Through analysis, there is a positive relationship between perceived ease of use and behavioural intention. In addition, according to the research of Wang (2018), in the context of mobile online shopping, online shopping intention is affected by perceived ease of use and usefulness, and there is a positive correlation between intention and the two perceptions. A similar viewpoint is concluded by Ferdianto (2022) and Keni (2020), who mentioned that the easier it is for consumers to operate the application in shopping, the higher the tendency for consumers to repurchase through the same application. Ventre and Kolbe (2020) admitted the existence of a positive and direct effect of perceived usefulness on online purchase intention. Hence, the following hypotheses are suggested:
There is a positive relationship between perceived ease of use and consumption intention of college students in the internet celebrity economy.
There is a positive relationship between perceived usefulness and consumption intention of college students in the internet celebrity economy.
Positivity will increase the wish to engage in the desired behaviour (Kinally and Bolduc, 2020). According to the findings of Rahmiati and Yuannita (2019), positivity has a considerable and favourable effect on attitude and purchase intention. In a similar vein, Kinally and Bolduc (2020) and Sanne and Wiese (2018) both found that attitude is often used to forecast a person’s behavioural intentions and serves as a critical mediator to support the favourable association between other online behavioural characteristics and purchase intentions. The study by Alit and Mazouzi (2023) also confirmed partial mediating roles of attitudes of consumers between (shopping convenience, perceived privacy, website design) and online purchase intention. Therefore, the following hypothesis is suggested.
There is a positive relationship between attitude and consumption intention of college students in the internet celebrity economy.
According to Wang (2018), We Media is a wide term that embodies the media individuation and popularisation growth trend. We Media is a brand-new medium born of modern network and electronic technology. It highlights the spirits of the information sharing and could span the space and time limit, creating a free social divide for more participants, where information is expressed. Its operability, interactivity and communicability are also very strong. Zhang et al. (2021) indicated that live platform has the characteristics of visibility, interactivity, authenticity and entertainment. These characteristics drive more intention to consume. It is found that social media has a stimulus role for green consumption among the younger generation to devise their subjective norms and perceptions (Xie and Madni, 2023). So, the following hypothesis is proposed.
There is a positive relationship between We Media environment and consumption intention of college students in the internet celebrity economy.
Internet celebrity marketing is now the most advantageous implementation strategy. At the moment, the primary channels for realising the internet celebrity economy are advertising, live streaming platform incentives, etc. Through individualised language or content, internet celebrity economy draws a sizable fan base and establishes a fan economy. The influencer factor has also become an important factor affecting consumer behaviour. Through a survey of online celebrities’ fans in the live streaming community, Meng et al. (2020) found that the credibility and professional appeal of web celebrities have an impact on consumers’ purchase intention. Relevant studies show that the live streaming characteristics of web celebrities have a certain impact on improving consumers’ purchase intention. Preliminary empirical review revealed that in the dynamic era of social media, it is clear that celebrity endorsements continue to play a pivotal role in influencing consumer behaviour (Norah, 2024). Hence, the following hypothesis is derived.
There is a positive relationship between internet celebrity marketing and consumption behavioural intention of college students in the internet celebrity economy.
Based on the TAM, Zheng (2014) proposed the influencing factors of college students’ digital reading in his research and concluded that college students’ willingness to adopt digital reading mode has a positive effect on actual reading behaviour. The study by Chen et al. (2024) concluded that attitude, perceived behavioural control, health consciousness and past eating behaviour positively and significantly influenced consumers’ intention to adopt sustainable healthy dietary patterns. Hence, the following hypothesis.
There is a positive relationship between consumption intention and consumption behaviour of college students in the internet celebrity economy.
Proposed research framework
Figure 3 shows the research framework of this study. PU, PEOU, ATT, WME and ICM are the independent variables of this study. Students’ consumption intention is a mediating variable, means that it is both a dependent variable and an independent variable, while consumption behaviour is the dependent variable of consumption intention.
Research methodology
Research method
A quantitative approach is used in the study to evaluate the hypothesis and ultimately draw a generalisation. This method examines patterns in human behaviour by decomposing social behaviour into variables that can be numerically translated as frequencies or rates and whose interrelationships may be investigated using statistical tests (Payne and Payne, 2004). In this research, data has been collected on college students’ consumption behaviour in internet celebrity economy under We Media environment. Such acts do not normally occur in a quantitative manner, but the researcher created a questionnaire that asked participants to score various claims. As a result, the researcher can use tools to obtain data, such as surveys, to quantify a wide range of occurrences. The advantages of quantitative approaches include their lower implementation costs, standardisation for easy comparisons and ability to routinely determine the impact magnitude (Kabir, 2016).
Data collection
For investigation purpose, the primary data, which is first-hand data is used. Primary data is actually more reliable, believable and objective than secondary data. Since primary data was not altered by people, its validity exceeds that of secondary data (Kabir, 2016). As the data are self-collected, the investigator has no reason to doubt their validity or quality (Bryman, 2016). A sort of observational study approach called a cross-sectional study design is used in many fields of social science and education. The cross-sectional research is used as it has the advantages of being comparatively less expensive and having the ability to swiftly gather a large amount of data (Hemed and Tanzania, 2015).
Target population
The population is the set of individuals from which the researcher intends to generalise and choose a sample. The number of college students in China and Malaysia is 10.787 million and the researcher intends to investigate the factors influencing college students’ consumption behaviour in internet celebrity economy under We Media environment in China and Malaysia. Since the age of the college students varies, most range from 18 to 25 (Platto et al., 2022), researcher investigate the age group of respondents by four categories: 18 and below, 19–21, 22–24 and 25 and above.
Sampling frame
A list of components that represent the target population makes up the sampling frame (Hemed and Tanzania, 2015). Due to the size of the target population and the difficulty in gathering all the information from it, the sample frame is not accessible in this study. Studying the viability reveals that it is impossible to have a sample that truly reflects the features of the entire community since it is a subset of that group (Young, 2020). The primary target respondents are college students. Since the research is conducted in Malaysia and the researchers are from Malaysia and China, the sampling locations are mostly Malaysia and China.
Sampling technique
Purposive sampling, also known as judgemental sampling, is a type of non-probability sampling that was used in this study. The major goal of the chosen sample is to concentrate on a particular group’s characteristics that can provide the best answers to the study questions (Etikan et al., 2016). This sampling technique necessitates that the researcher has a thorough understanding of the goal of the study to be able to locate and contact appropriate participants. In this study, the researcher chooses respondents based on their attitude towards We Media and who used We Media in the past.
Sample size
The sample size is an important consideration in research that seeks to draw conclusions about the population from the sample. The sample size is determined to make sure the sample is sufficient to accurately predict the outcomes for the total population (Taherdoost, 2016). The sample size calculation must be done carefully; else, the sample inference drawn from it could not be valid and could result in some errors (Faber and Fonseca, 2014). According to Hinkin (1995), an item-to-response ratio should range between 1:4 and 1:10. As there are 29 items in the questionnaire, a sample size between 116 and 290 would be considered sufficient and useful for the purpose of data analysis. Based on this, 300 usable questionnaires were used for data analysis.
Research instrument
The structured questionnaires were used to collect primary data and were distributed through the Internet. Responses collected through Google Form were stored in the online database once the respondents completed the form. The questionnaire consists of three sections. Demographic data of the target respondents are collected in Section A, Section B consists of items for five independent variables, while items for the mediating variable and dependent variable are included in Section C.
The questionnaire for the variables was developed adapting the research studies of the following researchers:
Pilot test
The objective of the pilot test is to evaluate the suitability of the research tools (Van Teijlingen and Hundley, 2002). According to In (2017), a pilot test should normally be conducted with a sample size of 10% to 20%. Hence, the researcher takes 50 participants for testing in the pilot study.
Reliability test
Cronbach’s alpha.
Cronbach’s alpha is normally used to test the reliability. Cronbach’s alpha is used as the standard procedure to evaluate the correlation values between the questionnaire’s items. Alpha was developed by Lee Cronbach in his research Cronbach (1951), to assess the internal consistency of a test or scale that ranged from 0 to 1.
Normality test.
As the data is the underlying presupposition in parametric testing, determining the normality of the data is a prerequisite for many analytical research (Mishra et al., 2019). One of the techniques used in this study to check for normality is the skewness and Kurtosis test. If the data distribution is symmetrical, it will appear as a bell-shaped curve with equal size to the left and right of the centre point (Mishra et al., 2019). Skewness refers to the symmetry measurement or more particularly the absence of symmetry (Cain et al., 2017). The high-value Kurtosis of data set tends to show a heavy tail and vice versa. The results of the skewness and Kurtosis tests are acceptable when the values fall within ±3 and ±10, respectively (Ryu, 2011).
Results
Demographic profile of respondents
Total 456 responses were received through questionnaires distributed, but only 300 responses were usable. The respondents of the research consisted of 78 males (26%), and 222 females (74%). Age group of 19–21 is the largest group of the respondents, constituting 43.67% of the respondents; followed by age between 22 and 24 (26.33%), and 25 and above (18.33%). Age 18 and below is the least, with a proportion of 11.67%.
In terms of country, most respondents come from China (86.33%), followed by Malaysia (11.67%) and others (2%). The majority of the respondents, 115, are in year 4 (38.34%), followed by 70 in year 3 (23.33%), 63 in year 1 (21%) and 52 in year 2 (17.33%). On a weekly basis, 141 respondents spend 2 to 5 h on We Media (47%), 60 spend 6 to 9 h (20%), 54 spend less than 2 h (18%), while 45 spend more than 9 h (15%).
Among the respondents, 254 had followed some internet celebrity when using We Media (84.67%), while 46 said they never followed any internet celebrity when using We Media (15.33%). In addition, 215 had bought some products recommended by internet celebrity when using We Media (71.67%), while 85 said they never followed any internet celebrity when using We Media (28.33%). Little Red Book is referred by 53% of the respondents on online consumption, followed by TikTok (49.33%); Bilibili (21.33%); Sina Weibo (19%); Kuaishou (14.33%); Instagram (11%); and Youtube (7.33%); while 3.33% of the respondents used other platforms.
Pilot study result
Normality − skewness and Kurtosis
Pearson’s correlation coefficient analysis
According to Table 2, the Cronbach’s alpha values of the variables are all above 0.6, which means that all the variables have the acceptable and good reliability and fit with the research topic.
Table 3 shows that all variables are normally distributed because the value of Skewness and Kurtosis fall between ±3 and ±10.
According to the results in Table 4, there is no multicollinearity problem because all the correlation values are less than 0.9 (Mohammadi, 2022). In addition, all of the indicators demonstrated significant relationships since their p-values are lower than 0.05.
Multiple linear regression
According to Table 5, the R2 is 68.6%, indicating that 68.6% of the variation in the consumption intention was explained by the variation in the independent variables (PEOU, PU, ATT, WME, ICM). The remaining 31.4% is explained by other variables that are not tested in this study.
Based on Table 6, the F-value is 128.350 with a small p-value that is less than 0.05, indicating that the model is significant. It shows that one or more independent variables (PEOU, PU, ATT, WME, ICM) have the relationship between the mediating variable (CI). Hence, it shows that the model is appropriate for this study.
Table 7 shows the p-value of the variables. As all independent variables have p-values that are less than 0.05, it can be concluded that all the independent variables, namely, PEOU, PU, ATT, WME and ICM have significant positive relationships with the mediating variable CI. In conclusion, all the hypotheses, H1 to H6, are accepted.
The multiple linear regression (MLR) is established as below:
Simple linear regression
According to Table 8, R2 is 65.2%, suggesting that 65.2% of the variation in the behaviour was explained by the variation in the mediating variable CI. The remaining 34.8% is explained by other variable not being tested in this study.
Table 9 shows a vast F-value of 558.414 with a small p-value of less than 0.05. This indicates that the model is significant and the mediating variable CI has a significant relationship with consumption behaviour. Hence, it shows that the model is appropriate for this study.
According to Table 10, the p-value of the mediating variable CI is less than 0.05, indicating that the mediating variable CI has a significant positive correlation to consumption behaviour. Thus, H6 is accepted.
The formula is established by the simple linear regression (SLR) is shown below:
Table 11 shows the summary of the hypothesis testing.
Discussion
The outcome shows that the perceived ease of use has a significant positive relationship with consumption intention (p < 0.05). Rahmiati and Yuannita (2019) noted that the factor perceived ease of use has a high level of significance in increasing purchase intention. Thus, consumption intention will be improved in the internet celebrity economy. A similar stand points were concluded by Ferdianto (2022); and Keni (2020), who mentioned that the easier it is for consumers to operate the application in shopping, the higher the tendency for consumers to repurchase through the same application. At the same time, the finding in the study of Kanchanatanee et al. (2014) justified that perceived ease of use has positive indirect effect on intention to use e-marketing. Accordingly, it can be inferred that perceived ease of use has a positive effect on consumption intention in the internet celebrity economy.
The outcome reveals that perceived usefulness has a significant positive relationship with consumption intention (p < 0.05). The study by Rahmiati and Yuannita (2019) found that perceived usefulness has a high level of significance in increasing purchase intention. Ventre and Kolbe (2020) confirmed the existence of a positive and direct effect of perceived usefulness on online purchase intention. Apart from this, the finding in study of Wang (2018) found that perceived usefulness has a positive influence on the consumption intention of college students in the internet celebrity economy, which is consistent with the outcome of this research.
It can be seen that attitude has a significant positive relationship with consumption intention (p < 0.05). Previous research has found a positive relationship between attitude and behavioural intention. A positive outlook increases the likelihood of engaging in the behaviour (Kinally and Bolduc, 2020). The findings of Rahmiati and Yuannita (2019) showed that it has a substantial and favourable impact on attitudes and purchasing intentions. Similar findings were made by Kinally and Bolduc (2020); and Sanne and Wiese (2018), both noted that attitude is frequently used to forecast a person’s behavioural intentions.
The outcome reveals that We Media environment has a significant positive effect on consumption intention (p < 0.05). Lu et al. (2020) also has a similar result that the content presentation of We Media positively affects purchase intention. The abundant contents of We Media will appeal customers and provoke their intention to purchase. In addition, Wang (2018) confirmed that We Media environment has a positive impact on the consumption intention of college students in the internet celebrity economy. We Media environment has a high interactivity as audiences can comment and give thumbs up to express their support to whom they like. Yu et al. (2017) discovered that when consumers are attracted by the things in front of them, it will have a significant impact on consumers’ consumption intentions as they will ignore other information.
The results show that internet celebrity marketing has a significant positive link with consumption intention to use the system (p < 0.05). Chen et al. (2021) stated that followers’ attachment and trust to a YouTube internet celebrity positively influences their impulse buying behaviour. Furthermore, Meng et al. (2020) found that the credibility and professional appeal of the internet celebrities have an impact on consumers’ purchase intention. A similar finding by Wang (2018) revealed that the main way of internet celebrity marketing is to record personal videos, so as to influence the purchase of fans through video spread. Thus, Wang (2018) concluded that internet celebrity marketing has a positive impact on consumption intention of college students in the internet celebrity economy.
The consumption intention has a positive relationship with the consumption behaviour of college students in the internet celebrity economy (p < 0.05). A study conducted by Lu et al. (2020) found that purchase intention is the prerequisite for consumers to implement purchase behaviour. Identically, Kim and Jan (2021) also reported that consumption intention will positively predict use behaviour. Pütter (2017) discovered that customer buying intention has a positive relationship with buying behaviour.
Conclusion
Theoretical implication
This research has academic contribution. It studied the factors influencing college students’ consumption behaviour in internet celebrity economy under We Media environment. The original variables in TAM (perceived ease of use and perceived usefulness) and TPB (attitude) are used to analyse consumption intention and consumption behaviour. In addition, this study adds two variables, We Media environment and internet celebrity marketing because We Media environment is considered a factor to improve college students’ consumption intention and internet celebrity marketing is known to be an intrinsic motivation to consume under We Media environment. This study expands the TAM and TPB models to facilitate future research on consumption intention and behaviour. It also serves as a reference to researchers who wish to do further research on internet celebrity economy. As all the hypotheses are supported by the research results, it can be construed that this research model has reinforced both the TAM model and TPB theory with two additional variables.
Managerial implication
This research may be beneficial to enterprises which want to explore new markets in the internet celebrity economy. Enterprises can adapt their marketing strategies according to the independent variables mentioned in this research. This research contributes to providing information of internet celebrity economy and We Media environment, and assists enterprises to have a better understanding. The information of this research also offer insights to enterprises to explore new markets in the internet celebrity economy. Furthermore, as the subjects of this research are college students, the research can provide some suggestions on online consumption to college students and supply recommendations of online consumption management to universities.
Limitations
There have been various restrictions throughout this research that should be noted for any future research projects. Firstly, the one-sidedness of the research influencing elements is one of its drawbacks. College students’ purchasing behaviour in the internet celebrity economy are frequently influenced by a variety of factors. As a result, the research may not be able to identify all variables that affect college students’ purchasing decisions in the internet celebrity economy. Secondly, due to time constrain, the research was only able to gather data during a short period of time. The last limitation is the gender and country imbalance of the respondents. The proportion of the female respondents is 74% but the male respondents is only 26%. Similarly, the majority of the respondents come from China (86.33%), while only 11.67% of the respondents come from Malaysia. As a consequence, the study on how respondents who possess a given trait see themselves may be influenced by the limited involvement of the particular criterion.
Recommendations
Firstly, future studies should combine internal and external factors influencing consumption behaviour in the internet celebrity economy while referring to a large number of mature markets domestically and abroad. The future studies should contain more variables to perfect the system. Secondly, longitudinal approach is recommended. Lastly, future researchers are advised to collect a balanced number of responses for both gender group and countries.
In conclusion, the objective of this research is to investigate the factors influencing college students’ consumption behaviour in internet celebrity economy under We Media environment. This research made contributions to future researchers who wish to look into the variables that influence college students’ consumption behaviour. Furthermore, this research is beneficial to give suggestions to enterprises which want to explore new markets in the internet celebrity economy and college students who want to consume in internet celebrity economy.
Figures
Reliability analysis for pilot study
| Variables | No. of Items | Cronbach’s alpha |
|---|---|---|
| PEOU | 4 | 0.795 |
| PU | 4 | 0.785 |
| ATT | 4 | 0.719 |
| WME | 4 | 0.717 |
| ICM | 4 | 0.709 |
| CI | 5 | 0.853 |
| BE | 4 | 0.633 |
The reliability of the pilot study is tested using the Cronbach’s alpha. According to Table 1, the Cronbach’s alphas for all the variables are above 0.6, thus are acceptable (Sharma, 2016)
Source: Authors’ own creation
Reliability analysis for full data
| Variables | No. of Items | Cronbach’s alpha |
|---|---|---|
| PEOU | 4 | 0.795 |
| PU | 4 | 0.814 |
| ATT | 4 | 0.796 |
| WME | 4 | 0.799 |
| ICM | 4 | 0.806 |
| CI | 5 | 0.862 |
| BE | 4 | 0.768 |
Source: Authors’ own creation
Skewness and Kurtosis
| Variable | Skewness | Kurtosis |
|---|---|---|
| PEOU | −0.588 | 0.313 |
| PU | −0.294 | 0.007 |
| ATT | −0.175 | 0.046 |
| WME | −0.445 | 0.644 |
| ICM | −0.854 | 1.197 |
| CI | −0.549 | 0.222 |
| BE | −0.440 | 0.337 |
Source: Authors’ own creation
Pearson’s correlation coefficient analysis
| PEOU | PU | ATT | WME | ICM | CI | BE | |
|---|---|---|---|---|---|---|---|
| PEOU | 1 | ||||||
| PU | 0.651** | 1 | |||||
| <0.001 | |||||||
| ATT | 0.579** | 0.742** | 1 | ||||
| <0.001 | <0.001 | ||||||
| WME | 0.475** | 0.410** | 0.381** | 1 | |||
| <0.001 | <0.001 | <0.001 | |||||
| ICM | 0.607** | 0.728** | 0.645** | 0.490** | 1 | ||
| <0.001 | <0.001 | <0.001 | <0.001 | ||||
| CI | 0.627** | 0.705** | 0.670** | 0.461** | 0.786** | 1 | |
| <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |||
| BE | 0.590** | 0.664** | 0.666** | 0.430** | 0.798** | 0.807** | 1 |
| <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Source: Authors’ own creation
Model summaryb (MLR)
| R | R-square | Adjusted R square | Standard error of the estimate |
|---|---|---|---|
| 0.828a | 0.686 | 0.680 | 0.47350 |
aPredictors = (Constant), PEOU, PU, ATT, WME, ICM;
bDependent variable = CI
Source: Authors’ own creation
Analysis of variance (ANOVAa − MLR)
| Sum of square | df | Mean square | F | p-value | |
|---|---|---|---|---|---|
| Regression | 143.881 | 5 | 28.776 | 128.350 | <0.001b |
| Residual | 65.915 | 294 | 0.224 | ||
| Total | 209.797 | 299 |
aDependent variable= CI;
bPredictors= (Constant), PEOU, PU, ATT, WME, ICM
Source: Authors’ own creation
Multiple linear regression analysisa
| Parameter estimate | Standardised estimate | t | p-value | Tolerance | VIF | |
|---|---|---|---|---|---|---|
| (Constant) | −0.332 | −1.921 | 0.056 | |||
| PEOU | 0.141 | 0.133 | 2.866 | 0.004 | 0.498 | 2.007 |
| PU | 0.119 | 0.116 | 1.995 | 0.047 | 0.317 | 3.159 |
| ATT | 0.196 | 0.178 | 3.514 | <0.001 | 0.417 | 2.397 |
| WME | 0.058 | 0.046 | 1.176 | <0.001 | 0.709 | 1.410 |
| ICM | 0.533 | 0.484 | 9.360 | <0.001 | 0.399 | 2.503 |
aDependent variable = CI
Source: Authors’ own creation
Model summaryb (SLR)
| R | R square | Adjusted R square | Standard error of the estimate |
|---|---|---|---|
| 0.807a | 0.652 | 0.651 | 0.45741 |
aPredictors= (Constant), CI;
bDependent variable= BE
Source: Authors’ own creation
Analysis of variance (ANOVAa − SLR)
| Sum of square | df | Mean square | F | p-value | |
|---|---|---|---|---|---|
| Regression | 116.835 | 1 | 115.835 | 558.414 | <0.001b |
| Residual | 62.349 | 298 | 0.209 | ||
| Total | 179.184 | 299 |
aDependent variable= BE;
bPredictors= (Constant), CI
Source: Authors’ own creation
Simple linear regression analysisa
| Parameter estimate | Standardised estimate | t | p-value | Tolerance | VIF | |
|---|---|---|---|---|---|---|
| (Constant) | 0.943 | 8.865 | <0.001 | |||
| CI | 0.746 | 0.807 | 23.631 | <0.001 | 1.000 | 1.000 |
Note: aDependent variable = BE
Source: Authors’ own creation
Summary of hypothesis testing result
| Hypothesis | MLR | ||
|---|---|---|---|
| p-value | Beta | Results | |
| H1 | 0.004 | 0.141 | Supported |
| H2 | 0.047 | 0.119 | Supported |
| H3 | <0.001 | 0.196 | Supported |
| H4 | <0.001 | 0.058 | Supported |
| H5 | <0.001 | 0.533 | Supported |
| H6 | <0.001 | 0.746 | Supported |
Source: Authors’ own creation
| Variables | Researcher/s |
|---|---|
| Perceived ease of use | Wang, 2018 |
| Perceived usefulness | Wang, 2018 and Zhang et al., 2021 |
| Attitude | Gao et al., 2022 and Zhang et al., 2021 |
| We Media environment | Wang, 2018 |
| Internet celebrity marketing | Wang, 2018 and Luo and Ma, 2021 |
| Consumption intention | Wang, 2018 and Zhang et al., 2021 |
| Consumption behaviour | Wang, 2018 and Gao et al., 2022 |
References
Ajzen, I. (1991), “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.
Alit, N. and Mazouzi, D. (2023), “Factors influencing consumers' attitudes and intentions towards online Shopping - a survey of a sample of consumers in Algeria”, Malaysian Journal of Consumer and Family Economics, Vol. 31 No. 1, pp. 788-814.
Ardiyanto, F. and Kusumadewi, H. (2019), “Pengintegrasian technology acceptance model (TAM) dan Kepercayaan Konsumen pada marketplace online Indonesia”, Jurnal Inspirasi Bisnis Dan Manajemen, Vol. 3 No. 2, pp. 177-192.
Bryman, A. (2016), Social Research Methods, Oxford University Press, Oxford.
Cain, M.K., Zhang, Z. and Yuan, K.H. (2017), “Univariate and multivariate skewness and kurtosis for measuring nonnormality: prevalence, influence and estimation”, Behavior Research Methods, Vol. 49 No. 5, pp. 1716-1735.
Chen, X., Jiang, X. and Wu, L. (2024), “Drivers of consumers’ intention to adopt sustainable healthy dietary patterns: evidence from China”, Frontiers in Sustainable. Food Systems, Vol. 7, p. 1269242, doi: 10.3389/fsufs.2023.126924.
Chen, T.Y., Yeh, T.L. and Lee, F.Y. (2021), “The impact of internet celebrity characteristics on followers' impulse purchase behavior: the mediation of attachment and parasocial interaction”, Journal of Research in Interactive Marketing, Vol. 15 No. 3, pp. 483-501, doi: 10.1108/JRIM-09-2020-0183.
Cronbach, L.J. (1951), “Coefficient alpha and the internal structure of tests”, Psychometrika, Vol. 16 No. 3, pp. 297-334.
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003, doi: 10.1287/mnsc.35.8.982.
Davis, F.D. (1985), “A technology acceptance model for empirically testing new end-user information systems: theory and results”, Doctoral dissertation, Massachusetts Institute of Technology.
Etikan, I., Musa, S.A. and Alkassim, R.S. (2016), “Comparison of convenience sampling and purposive sampling”, American Journal of Theoretical and Applied Statistics, Vol. 5 No. 1, pp. 1-4.
Faber, J. and Fonseca, L.M. (2014), “How sample size influences research outcomes”, Dental Press Journal of Orthodontics, Vol. 19 No. 4, pp. 27-29.
Ferdianto, R. (2022), “The role of perceived usefulness and perceived ease of use in increasing repurchase intention in the era of the covid-19 pandemic”, Research Horizon, Vol. 2 No. 2, pp. 313-329.
Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, London.
Gao, F., Cui, C. and Wu, Z. (2022), “Analysis on the influence factors of We-Media on college students' consumption decision under the internet celebrity economy: a case study of Xiaohongshu”, Journal of Modern Marketing, Vol. 3, pp. 58-61, doi: 10.19932/j.carol carroll nki. 22-1256/F 2022.03.058.
Gefen, D., Karahanna, E. and Straub, D.W. (2003a), “Trust and TAM in online shopping: an integrated mode”, MIS Quarterly, Vol. 27 No. 1, pp. 51-90.
Gefen, D., Karahanna, E. and Straub, D.W. (2003b), “Inexperience and experience with online stores: the importance of TAM and trust”, Engineering Management, IEEE Transactions, Vol. 50 No. 3, pp. 307-321.
George, J.F. (2004), “The theory of planned behavior and internet purchasing”, Internet Research, Vol. 14 No. 3, pp. 198-212.
Hansen, T., Møller Jensen, J. and Stubbe Solgaard, H. (2004), “Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior”, International Journal of Information Management, Vol. 24 No. 6, pp. 539-550.
Hemed, M. and Tanzania, G.F.M.E.R. (2015), “Cross-sectional studies”, Training Course in Sexual and Reproductive Health Research Geneva, Vol. 12.
Hinkin, T.R. (1995), “A review of scale development practices in the study of organizations”, Journal of Management, Vol. 21 No. 5, pp. 967-988.
Hua, T., Ma, Q. and Zeng, S. (2022), “What are factors affecting consumer behavior”, International Conference on Science Education and Art Appreciation (SEAA 2022), Atlantis Press, pp. 935-947.
In, J. (2017), “Introduction of a pilot study”, Korean Journal of Anesthesiology, Vol. 70 No. 6, pp. 601-605.
Kabir, S.M.S. (2016), Basic Guidelines for Research. An Introductory Approach for All Disciplines, Book Zone Publication, Chittagong.
Kanchanatanee, K., Suwanno, N. and Jarernvongrayab, A. (2014), “Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use e-marketing”, Journal of Management Research, Vol. 6 No. 3, pp. 1-13.
Keni, K. (2020), “How perceived usefulness and perceived ease of use affecting intent to repurchase?”, Jurnal Manajemen, Vol. 24 No. 3, pp. 481-496.
Kim, H. and Jan, I.U. (2021), “Consumption value factors as determinants of use intention and behavior of car-sharing services in the Republic of Korea”, Innovation: The European Journal of Social Science Research, pp. 1-19, doi: 10.1080/13511610.2021.1890551.
Kinally, W. and Bolduc, H. (2020), “Integrating the theory of planned behavior and uses and gratifications to understand music streaming intentions and behavior”, Atlantic Journal of Communication, Vol. 28 No. 3, pp. 1-15, doi: 10.1080/15456870.2020.1718676.
Li, Z. (2022), “Research on the promotion of college students' employment by We Media based on big data”, Academic Journal of Science and Technology, Vol. 3 No. 2, pp. 112-115.
Li, J., Zou, S. and Yang, H. (2019), “How does ‘storytelling’ influence consumer trust in We Media advertorials? An investigation in China”, Journal of Global Marketing, Vol. 32 No. 5, pp. 319-334.
Lu, H., Chen, Z., Liu, M. and Huang, Y. (2020), “The influence of Webcast on consumers’ purchase decision under the internet celebrity economy”, International Conference on Frontier Computing, Springer, Singapore, pp. 1219-1226.
Luo, H. and Ma, X. (2021), “A study on the influence of “grass-growing economy” on college students' consumption behavior in We-Media Era-A case study of XiaoHongshu”, China Collective Economy, Vol. 25, pp. 165-166.
Meng, L., Liu, F. and Chen, S. (2020), “Research on the influence mechanism of information source characteristics of different types of live webstars on consumers' purchase intention”, Nankai Management Review, Vol. 1, pp. 131-143.
Mishra, P., Pandey, C.M., Singh, U., Gupta, A., Sahu, C. and Keshri, A. (2019), “Descriptive statistics and normality tests for statistical data”, Annals of Cardiac Anaesthesia, Vol. 22 No. 1, p. 67.
Mohammadi, S. (2022), “A test of harmful multicollinearity: a generalized ridge regression approach”, Communications in Statistics-Theory and Methods, Vol. 51 No. 3, pp. 724-743.
Norah, B. (2024), “The influence of celebrity endorsements on consumer behavior in the era of social media”, Journal of Communication, Vol. 5 No. 1, pp. 1-13.
Payne, G. and Payne, J. (2004), Key Concepts in Social Research, Sage, London.
Platto, S., Serres, A. and Jingyi, A. (2022), “Chinese college students’ attitudes towards animal welfare”, Animals, Vol. 12 No. 2, p. 156.
Pütter, M. (2017), “The impact of social media on consumer buying intention”, Marketing, Vol. 3 No. 1, pp. 7-13.
Rahmiati, R. and Yuannita, I.I. (2019), “The influence of trust, perceived usefulness, perceived ease of use, and attitude on purchase intention”, Jurnal Kajian Manajemen Bisnis, Vol. 8 No. 1, pp. 27-34.
Ryu, E. (2011), “Effects of skewness and Kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling”, Behavior Research Methods, Vol. 43 No. 4, pp. 1066-1074.
Sanne, P.N.C. and Wiese, M. (2018), “The theory of planned behaviour and user engagement applied to Facebook advertising”, SA Journal of Information Management, Vol. 20 No. 1, pp. 1-10, doi: 10.4102/sajim.v20i1.915.
Sharma, B. (2016), “A focus on reliability in developmental research through Cronbach’s alpha among medical, dental and paramedical professionals”, Asian Pacific Journal of Health Sciences, Vol. 3 No. 4, pp. 271-278.
Taherdoost, H. (2016), “Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research”, How to test the validation of a questionnaire/survey in a research (August 10, 2016).
Van Teijlingen, E. and Hundley, V. (2002), “The importance of pilot studies”, Nursing Standard, Vol. 16 No. 40, p. 33.
Ventre, I. and Kolbe, D. (2020), “The impact of perceived usefulness of online reviews, trust and perceived risk on online purchase intention in emerging markets: a Mexican perspective”, Journal of International Consumer Marketing, Vol. 32 No. 4, pp. 287-299.
Wang, J. (2018), “Research on the business prospect of internet celebrity economy based on We-Media environment”, Knowledge Economy, Vol. 19, pp. 73-74.
Xie, S. and Madni, G.R. (2023), “Impact of social media on young generation’s green consumption behavior through subjective norms and perceived green value”, Sustainability, Vol. 15 No. 4, p. 3739, doi: 10.3390/su1504373.
Yang, P. and Zhang, W. (2021), “The current and future situations of We media and Chinese museum development”, Luoyang Archaeological, Vol. 2, pp. 87-91.
Young, H.P. (2020), “Individual strategy and social structure”, Individual Strategy and Social Structure, Princeton University Press, Princeton.
Yu, X., Xu, Z. and Guo, W. (2017), “Research on the influence of online merchants' product information presentation on consumers' behavioural intention: a model construction based on social presence theory”, Information Theory and Practice, Vol. 10, pp. 80-84.
Yulistia, Y. (2017), “Analisis pengaruh efektivitas dan manfaat E-Commerce terhadap sikap dan perilaku pengguna dengan menggunakan metode TAM (Studi Kasus: UKM Kota Palembang)”, JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), Vol. 4 No. 1, pp. 93-100.
Zhang, B., Zhang, Q. and Zhao, C. (2021), “The influence of network broadcast characteristics on consumers' purchase intention under e-commerce live broadcast mode: the mediating role of consumer perception”, China's Circulation Economy, Vol. 35, pp. 52-61, doi: 10.14089/j.carol carroll nki cn11-3664/f 2021.06.005.
Zhang, M. (2018), “Research on consumer behavior and marketing strategy of mobile internet advertising audience”, Capital University of Economics and Business.
Zheng, Z. (2014), “An empirical study of college students' digital reading behavior pattern based on TAM”, Fudan University.