The Influence of Internet Celebrities’ Expertise and Attraction on Residents’ Intention to Purchase Household Energy-Saving Products in the Context of an Online Community
Abstract
:1. Introduction
2. Theoretical Background and Hypotheses
2.1. The Impact of an Internet Celebrities’ Expertise on the Audience’s Purchase Intention
2.2. The Relationship between Internet Celebrities ’Attraction and Fans’ Purchase Intention
2.3. The Mediating Role of Parasocial Relationships
2.4. The Moderating Effect of Green Self-Efficacy
3. Data Collection and Methodology
Data Collection and Sampling
4. Data Analysis and Result
4.1. Measurement Model Analysis
4.2. Structural Model Testing
Hypothesis Testing
- The effects of the internet celebrities‘ expertise, social attraction, and task attraction on fans’ intention to purchase products.
- 2.
- The mediating role of parasocial relationships.
- 3.
- The moderating role of green self-efficacy.
5. Conclusions, Implications, and Limitations
5.1. Conclusions
5.2. Theoretical Contributions
5.3. Practical Implications
5.4. Limitations
Author Contributions
Funding
Conflicts of Interest
References
- Appiah, M.O. Investigating the multivariate Granger causality between energy consumption, economic growth and CO2 emissions in Ghana. Energy Policy 2018, 112, 198–208. [Google Scholar] [CrossRef]
- Yue, T.; Long, R.; Chen, H.; Liu, J.; Liu, H.; Gu, Y. Energy-saving behavior of urban residents in China: A multi-agent simulation. J. Clean. Prod. 2020, 252, 119623. [Google Scholar] [CrossRef]
- McDougall, G.H.G. The green movement in Canada: Implications for marketing strategy. J. Int. Consum. Mark. 1993, 5, 69–87. [Google Scholar]
- Barr, S.; Gilg, A.W.; Ford, N. The household energy gap: Examining the divide between habitual- and purchase-related conservation behaviours. Energy Policy 2005, 33, 1425–1444. [Google Scholar] [CrossRef]
- Zhang, Y.; Xiao, C.; Zhou, G. Willingness to pay a price premium for energy-saving appliances: Role of perceived value and energy efficiency labeling. J. Clean. Prod. 2020, 242, 118555. [Google Scholar] [CrossRef]
- Iyengar, R.; Bulte, C.V.D.; Valente, T.W. Opinion Leadership and Social Contagionin New Product Diffusion. Mark. Sci. 2011, 30, 195–212. [Google Scholar] [CrossRef] [Green Version]
- Cho, Y.; Hwang, J.; Lee, D.; Techfore, J. Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach. Technol. Forecast. Soc. Change 2012, 79, 97–106. [Google Scholar] [CrossRef]
- Zhao, X.; Cheng, H.; Zhao, H.; Jiang, L.; Xue, B. Survey on the households’ energy-saving behaviors and influencing factors in the rural loess hilly region of China. J. Clean. Prod. 2019, 230, 547–556. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, X.; Guo, D. Policy implications of the purchasing intentions towards energy-efficient appliances among China’s urban residents: Do subsidies work? Energy Policy 2017, 102, 430–439. [Google Scholar] [CrossRef]
- Yue, T.; Long, R.; Chen, H. Factors influencing energy-saving behavior of urban households in Jiangsu Province. Energy Policy 2013, 62, 665–675. [Google Scholar] [CrossRef]
- Tan, C.-S.; Ooi, H.-Y.; Goh, Y.-N. A moral extension of the theory of planned behavior to predict consumers’ purchase intention for energy-efficient household appliances in Malaysia. Energy Policy 2017, 107, 459–471. [Google Scholar] [CrossRef]
- Chen, S.-C.; Hung, C.-W. Elucidating the factors influencing the acceptance of green products: An extension of theory of planned behavior. Technol. Forecast. Soc. Change 2016, 112, 155–163. [Google Scholar] [CrossRef]
- Shahangian, S.A.; Tabesh, M.; Yazdanpanah, M. How can socio-psychological factors be related to water-efficiency intention and behaviors among Iranian residential water consumers? J. Environ. Manag. 2021, 288, 112466. [Google Scholar] [CrossRef]
- Akhtar, R.; Sultana, S.; Masud, M.M.; Jafrin, N.; Al-Mamun, A. Consumers’ environmental ethics, willingness, and green consumerism between lower and higher income groups. Resour. Conserv. Recycl. 2021, 168, 105274. [Google Scholar] [CrossRef]
- Zhou, K.; Yang, S. Understanding household energy consumption behavior: The contribution of energy big data analytics. Renew. Sustain. Energy Rev. 2016, 56, 810–819. [Google Scholar] [CrossRef]
- Ding, Z.H.; Wang, G.Q.; Liu, Z.H.; Long, R.Y. Research on differences in the factors influencing the energy-saving behavior of urban and rural residents in China—A case study of Jiangsu Province. Energy Policy 2017, 100, 252–259. [Google Scholar] [CrossRef]
- Sun, Y.; Liu, N.; Zhao, M. Factors and mechanisms affecting green consumption in China: A multilevel analysis. J. Clean. Prod. 2019, 209, 481–493. [Google Scholar] [CrossRef]
- Wang, B.; Wang, X.; Guo, D.; Zhang, B.; Wang, Z. Analysis of factors influencing residents’ habitual energy-saving behaviour based on NAM and TPB models: Egoism or altruism? Energy Policy 2018, 116, 68–77. [Google Scholar] [CrossRef]
- Wang, S.; Lin, S.; Li, J. Exploring the effects of non-cognitive and emotional factors on household electricity saving behavior. Energy Policy 2018, 115, 171–180. [Google Scholar] [CrossRef]
- Gaspar, R.; Antunes, D. Energy efficiency and appliance purchases in Europe: Consumer profiles and choice determinants. Energy Policy 2011, 39, 7335–7346. [Google Scholar] [CrossRef]
- Mills, B.; Schleich, J. What’s driving energy efficient appliance label awareness and purchase propensity? Energy Policy 2010, 38, 814–825. [Google Scholar] [CrossRef] [Green Version]
- Fink, M.; Koller, M.; Gartner, J.; Floh, A.; Harms, R. Effective entrepreneurial marketing on Facebook—A longitudinal study. J. Bus. Res. 2020, 113, 149–157. [Google Scholar] [CrossRef]
- Whitehead, J.L., Jr. Factors of source credibility. Q. J. Speech 1968, 54, 59–63. [Google Scholar] [CrossRef]
- Kim, S.; Kandampully, J.; Bilgihan, A. The influence of eWOM communications: An application of online social network framework. Comput. Hum. Behav. 2018, 80, 243–254. [Google Scholar] [CrossRef]
- Ohanian, R. The impact of celebrity spokespersons’ perceived image on consumers’ intention to purchase. J. Advert. Res. 1991, 31, 46–54. [Google Scholar]
- Uribe, R.; Buzeta, C.; Velásquez, M. Sidedness, commercial intent and expertise in blog advertising. J. Bus. Res. 2016, 69, 4403–4410. [Google Scholar] [CrossRef]
- Daneshvary, R.; Schwer, R.K. The association endorsement and consumers’ intention to purchase. J. Consum. Mark. 2000, 17, 203–213. [Google Scholar] [CrossRef]
- Erdogan, B.Z. Celebrity endorsement: A literature review. J. Mark. Manag. 1999, 15, 291–314. [Google Scholar] [CrossRef]
- Ohanian, R. Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. J. Advert. 1990, 19, 39–52. [Google Scholar] [CrossRef]
- Campbell, D.E.; Wells, J.D.; Valacich, J.S. Breaking the Ice in B2C Relationships: Understanding Pre-adoption Ecommerce Attraction. Inf. Syst. Res. 2013, 24, 219–238. [Google Scholar] [CrossRef]
- Zheng, X.; Men, J.; Xiang, L.; Yang, F. Role of Technology Attraction and Para-social Interaction in Social Shopping Websites. Int. J. Inf. Manag. 2020, 51, 102043. [Google Scholar] [CrossRef]
- Bandura, A. Social Foundations of Thought and Action: A Social Cognitive Theory; Prentice Hall: Englewood Cliffs, NJ, USA, 1986. [Google Scholar]
- McCroskey, J.C.; Hamilton, P.R.; Weiner, A.N. The effect of interaction behavior on source credibility, homophily, and inter-personal attraction. Hum. Commun. Res. 1974, 1, 42–52. [Google Scholar] [CrossRef]
- Lee, J.E.; Watkins, B. YouTube vloggers’ influence on consumer luxury brand perceptions and intentions. J. Bus. Res. 2016, 69, 5753–5760. [Google Scholar] [CrossRef]
- Beege, M.; Schneider, S.; Nebel, S.; Rey, G.D. Look into my eyes! Exploring the effect of addressing in educational videos. Learn. Instr. 2017, 49, 113–120. [Google Scholar] [CrossRef]
- Borup, J.; West, R.E.; Thomas, R.; Graham, C.R. Examining the impact of video feedback on instructor social presence in blended courses. Int. Rev. Res. Open Distrib. Learn. 2014, 15, 232–256. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Liu, Q.; Chen, W.; Wang, Q.; Stein, D. Effects of instructor’s facial expressions on students’ learning with video lectures. Br. J. Educ. Technol. 2019, 50, 1381–1395. [Google Scholar] [CrossRef]
- Aw, E.C.X.; Chuah, S.H.W. “Stop the unattainable ideal for an ordinary me!” fostering para-social relationships with social media influencers: The role of self-discrepancy. J. Bus. Res. 2021, 132, 146–157. [Google Scholar] [CrossRef]
- Liu, M.T.; Liu, Y.; Zhang, L.L. Vlog and brand evaluations: The influence of parasocial interaction. Asia Pac. J. Mark. Logist. 2019, 31, 419–436. [Google Scholar] [CrossRef]
- Sakib, M.D.N.; Zolfagharian, M.; Yazdanparast, A. Does para-social interaction with weight loss vloggers affect compliance? The role of vlogger characteristics, consumer readiness, and health consciousness. J. Retail. Consum. Serv. 2020, 52, 101733. [Google Scholar] [CrossRef]
- Horai, J.; Naccari, N.; Fatoullah, E. The effects of expertise and physical attractiveness upon opinion agreement and liking. Sociometry 1974, 37, 601–606. [Google Scholar] [CrossRef] [Green Version]
- Bansal, H.S.; Voyer, P.A. Word-of-mouth processes within a services purchase decision context. J. Serv. Res. 2000, 3, 166–177. [Google Scholar] [CrossRef] [Green Version]
- Eyal, K.; Rubin, A.M. Viewer aggression and homophily, identification, and parasocial relationships with television characters. J. Broadcast. Electron. Media 2003, 47, 77–98. [Google Scholar] [CrossRef]
- Zhang, H.; Lu, Y.; Gupta, S.; Zhao, L. What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Inf. Manag. 2014, 51, 1017–1030. [Google Scholar] [CrossRef]
- Sharma, S.; Crossler, R.E. Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electron. Commer. Res. Appl. 2014, 13, 305–319. [Google Scholar] [CrossRef]
- Kang, J.Y.M.; Johnson, K.K.P. F-Commerce platform for apparel online social shopping: Testing a Mowen’s 3M model. Int. J. Inf. Manag. 2015, 35, 691–701. [Google Scholar] [CrossRef]
- Porter, C.E.; Donthu, N. Cultivating Trust and Harvesting Value in Virtual Communities. Manag. Sci. 2008, 54, 113–128. [Google Scholar] [CrossRef]
- Bandura, A. Self-Efficacy: The Exercise of Control; Freeman: New York, NY, USA, 1997. [Google Scholar]
- Meinhold, J.L.; Malkus, A.J. Adolescent environmental behaviors: Can knowledge, attitudes, and self-efficacy make a difference? Environ. Behav. 2005, 37, 511–532. [Google Scholar] [CrossRef]
- Huang, H.P. Media use, environmental beliefs, self-efficacy, and pro-environmental behavior. J. Bus. Res. 2016, 69, 2206–2212. [Google Scholar] [CrossRef]
- Shen, X.L.; Li, Y.J.; Sun, Y.; Chen, Z.; Wang, F. Understanding the role of technology attractiveness in promoting social commerce engagement: Moderating effect of personal interest. Inf. Manag. 2019, 56, 294–305. [Google Scholar] [CrossRef]
- Rubin, R.R.; McHugh, M.P. Development of para-social interaction relationships. J. Broadcast. Electron. Media 1987, 31, 279–292. [Google Scholar] [CrossRef]
- Chen, G.; Gully, S.M.; Eden, D. Validation of a new general self-efficacy scale. Organ. Res. Methods 2001, 4, 62–83. [Google Scholar] [CrossRef] [Green Version]
- Shen, D.; Cho, M.H.; Tsai, C.L.; Marra, R. Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet High. Educ. 2013, 19, 10–17. [Google Scholar] [CrossRef]
- Shelton, C.; Yang, J.; Liu, Q. Managing in an age of complexity: Quantum skills for the new millennium. Int. J. Hum. Resour. Dev. Manag. 2005, 5, 127–141. [Google Scholar] [CrossRef]
Variables | Item | Frequency | Frequency |
---|---|---|---|
Gender | Male | 142 | 42.6% |
Female | 192 | 57.4% | |
Age | Under 20 | 3 | 0.9% |
20–25 | 215 | 64.4% | |
26–30 | 27 | 8.1% | |
31–35 | 73 | 21.9% | |
36–40 | 13 | 3.9% | |
40 and above | 3 | 0.9% | |
Education level | High school and secondary | 6 | 1.8% |
specialized school | 10 | 3.0% | |
College degree | 153 | 45.8% | |
Bachelor and above | 165 | 48.2% | |
Monthly income | Less than 157dollars | 1 | 0.3% |
157–289 dollars | 90 | 26.9% | |
290–438 dollars | 52 | 15.6% | |
439–617 dollars | 25 | 7.5% | |
618–735 dollars | 21 | 6.3% | |
736–975 dollars | 23 | 6.9% | |
975 dollars above | 122 | 36.5% |
Index | χ2/df | GFI | AGFI | NFI | TLI | CFI | RMSEA |
---|---|---|---|---|---|---|---|
Standard | <3 | >0.9 | >0.9 | >0.9 | >0.9 | >0.9 | <0.05 |
Result | 2.295 | 0.948 | 0.917 | 0.958 | 0.979 | 0.985 | 0.048 |
Variables | Loading | Cronbach′s α | Composite Reliability | AVE |
---|---|---|---|---|
expertise | 0.777 | 0.883 | 0.8843 | 0.6569 |
0.793 | ||||
0.868 | ||||
0.801 | ||||
Social attraction | 0.886 | 0.901 | 0.9018 | 0.6969 |
0.828 | ||||
0.819 | ||||
0.804 | ||||
Task attraction | 0.780 | 0.897 | 0.8994 | 0.6911 |
0.839 | ||||
0.842 | ||||
0.862 | ||||
Parasocial interaction relationships | 0.791 | 0.844 | 0.8509 | 0.5893 |
0.786 | ||||
0.668 | ||||
0.817 | ||||
Green self-efficacy | 0.828 | 0.940 | 0.9408 | 0.6945 |
0.856 | ||||
0.835 | ||||
0.855 | ||||
0.860 | ||||
0.766 | ||||
0.830 | ||||
Product purchase intention | 0.810 | 0.851 | 0.8537 | 0.6605 |
0.808 | ||||
0.820 |
Construct | Expertise | Social Attraction | Task Attraction | Parasocial Relationships | Green Self-Efficacy | Purchase Intention |
---|---|---|---|---|---|---|
expertise | 0.810 | |||||
Social attraction | 0.344 ** | 0.835 | ||||
Task Attraction | 0.683 ** | 0.445 ** | 0.831 | |||
Parasocial relationships | 0.543 ** | 0.498 ** | 0.546 ** | 0.768 | ||
Green self-efficacy | 0.649 ** | 0.494 ** | 0.670 ** | 0.625 ** | 0.833 | |
Purchase intention | 0.518 ** | 0.504 ** | 0.549 ** | 0.690 ** | 0.650 ** | 0.813 |
Path | Path Coefficient | t-Value | Hypothesis | Results |
---|---|---|---|---|
EX → PIN | 0.7040 | 10.3129 *** | H1 | Supported |
SA → PIN | 0.3094 | 9.824 *** | H2 | Supported |
TA → PIN | 0.4744 | 11.4553 *** | H3 | Supported |
Mediation Paths | Indirect Effects | Lower Bound | Upper Bound | p-Value |
---|---|---|---|---|
EX-PSI-PIN | 0.2810 | 0.2050 | 0.3666 | 0.000 |
SA-PSI-PIN | 0.1901 | 0.1403 | 0.2440 | 0.0001 |
TA-PSI-PIN | 0.2665 | 0.1972 | 0.3450 | 0.000 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Luo, B.; Nie, M.; Ji, H. The Influence of Internet Celebrities’ Expertise and Attraction on Residents’ Intention to Purchase Household Energy-Saving Products in the Context of an Online Community. Energies 2023, 16, 3332. https://doi.org/10.3390/en16083332
Luo B, Nie M, Ji H. The Influence of Internet Celebrities’ Expertise and Attraction on Residents’ Intention to Purchase Household Energy-Saving Products in the Context of an Online Community. Energies. 2023; 16(8):3332. https://doi.org/10.3390/en16083332
Chicago/Turabian StyleLuo, Biao, Mengzhen Nie, and Hongmei Ji. 2023. "The Influence of Internet Celebrities’ Expertise and Attraction on Residents’ Intention to Purchase Household Energy-Saving Products in the Context of an Online Community" Energies 16, no. 8: 3332. https://doi.org/10.3390/en16083332
APA StyleLuo, B., Nie, M., & Ji, H. (2023). The Influence of Internet Celebrities’ Expertise and Attraction on Residents’ Intention to Purchase Household Energy-Saving Products in the Context of an Online Community. Energies, 16(8), 3332. https://doi.org/10.3390/en16083332