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How consumers’ adopting intentions towards eco-friendly smart home services are shaped? An extended technology acceptance model

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Abstract

Eco-friendly smart home services (ESHS) play a significant role in environmental protection. The study aims to investigate consumers’ intention to adopt ESHS and employs the theory of technology acceptance model as the theoretical research framework. The model was further extended by incorporating the constructs of knowledge, perceived risk, and environmental consciousness. Data were collected from 643 respondents through a self-administered questionnaire survey and analyzed by structural equation modeling. Results confirmed that perceived ease of use, perceived usefulness, knowledge, and environmental consciousness significantly and positively influence consumers’ intention to adopt ESHS. Consumers’ perceived risk negatively influences perceived usefulness, and consumers’ perceived risk also reduces their intention to adopt ESHS. Moreover, consumers’ knowledge has a positive effect on perceived ease of use and perceived usefulness but has a negative effect on perceived risk. Based on these results, implications from the perspectives of policy makers, ESHS companies, marketing professionals, and practitioners are provided for motivating other consumers to adopt such eco-friendly services.

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References

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211

    Article  Google Scholar 

  • Ajzen I, Cote NG (2008) Attitudes and the prediction of behavior. In: CranoWD, Prislin P, editors. Attitudes and attitude change. New York: Psychology Press

  • Aldossari MQ, Sidorova A (2020) Consumer acceptance of Internet of Things (Iot): smart home context. J Comput Inform Syst 60(6):507–511

    Google Scholar 

  • Al-Emran M, Mezhuyev V, Kamaludin A (2021) Is M-learning acceptance influenced by knowledge acquisition and knowledge sharing in developing countries? Educ Inf Technol 26:2585–2606

    Article  Google Scholar 

  • Anderson JC, Gerbing DW (1988) “Structural equation modeling in practice: a review of the two-step approach.” Psychol Bull 103(3):411–423

    Article  Google Scholar 

  • Balta-Ozkan N, Amerighi O, Boteler B (2014) A comparison of consumer perceptions towards smart homes in the UK, Germany and Italy: reflections for policy and future research. Technol Anal Strat Manag 26(10):1176–1195

    Article  Google Scholar 

  • Bandura A (1977) Social Learning Theory. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Bansal G (2011) E-book usage: role of environmental consciousness, personality and past usage. J Comput Inform Syst 52(2):93–104

    Google Scholar 

  • Charlie W, Tom H, Richard H-B (2015) Smart homes and their users a systematic analysis and key challenges. Pers Ubiquit Comput 19:463–476

    Article  Google Scholar 

  • Chen R, He F (2003) Examination of brand knowledge, perceived risk and consumers’ intention to adopt an online retailer. Total Qual Manag Bus Excell 14(6):677–693

    Article  Google Scholar 

  • Chen S-C, Hung C-W (2016) Elucidating the factors influencing the acceptance of green products: an extension of theory of planned behavior. Technol Forecast Soc Chang 112:155–163

    Article  Google Scholar 

  • Cheng Y-H, Huang T-Y (2013) High speed rail passengers’ mobile ticketing adoption. Transp Res Part c Emerg Technol 30:143–160

    Article  Google Scholar 

  • Cheung R, Vogel D (2013) Predicting user acceptance of collaborative technologies: an extension of the technology acceptance model for e-learning. Comput Educ 63:160–175

    Article  Google Scholar 

  • Chiu C-M, Wang ETG (2008) Understanding Web-based learning continuance intention: the role of subjective task value. Informa Manag 45(3):194–201

    Article  Google Scholar 

  • Chong AY-L, Ooi K-B, Lin B, Bao H (2012) An empirical analysis of the determinants of 3G adoption in China. Comput Hum Behav 28(2):360–369

    Article  Google Scholar 

  • Chong AYL, Darmawan N, Ooi KB, Lin BS (2010) Adoption of 3G services among Malaysian consumers: an empirical analysis. Int J Mobile Commun 8(2):129–149

    Article  Google Scholar 

  • Chong G, Ling Z, Yuan Y (2011) The research and implement of smart home system based on Internet of Things. In: Proceeding of 2011 international conference on electronics, communications and control (ICECC), IEEE

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Article  Google Scholar 

  • Demiris G, Rantz MJ, Aud MA, Marek KD, Tyrer HW, Skubic M, Hussam AA (2004) Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: a pilot study. Med Inform Internet Med 29:87–94

    Article  Google Scholar 

  • Duric I, Barac D, Bogdanovic Z, Labus A, Radenkovic B (2021) Model of an intelligent smart home system based on ambient intelligence and user profiling. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03081-4

    Article  Google Scholar 

  • Featherman MS, Pavlou PA (2003) Predicting e-services adoption: a perceived risk facets perspective. Int J Hum Comput Stud 59(4):451–474

    Article  Google Scholar 

  • Feng S, Setoodeh P, Haykin S (2017) Smart home: cognitive interactive people-centric internet of things. IEEE Commun Mag 55(2):34–39

    Article  Google Scholar 

  • Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error: algebra and statistics. J Mark Res 18:382–388

    Article  Google Scholar 

  • Gao L, Wang S, Li J, Li H (2017) Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resour Conserv Recycl 127:107–113

    Article  Google Scholar 

  • Han H, Kim Y (2010) An investigation of green hotel customers’ decision formation: developing an extended model of the theory of planned behavior. Int J Hosp Manag 29(4):659–668

    Article  Google Scholar 

  • Harman HH (1976) Modern Factor Analysis. University of Chicago Press, Chicago, IL

    Google Scholar 

  • Heirsh S (2012) A review of the literature of perceived risk and identifying its various facets in e- commerce by customers: focusing on developing countries. Afr J Bus Manage 6(8):2888–2896

    Google Scholar 

  • Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43(1):115–135

    Article  Google Scholar 

  • Hong W, Thong JYL, Wong W-M, Tam K-Y (2015) Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. J Manag Inf Syst 18(3):97–124

    Article  Google Scholar 

  • Hubert M, Blut M, Brock C, Backhaus C, Eberhardt T (2017) Acceptance of smartphone-based mobile shopping: mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychol Mark 34(2):175–194

    Article  Google Scholar 

  • Hubert M, Blut M, Brock C, Zhang RW, Koch V, Riedl R (2019) The influence of acceptance and adoption drivers on smart home usage. Eur J Mark 53(6):1073–1098

    Article  Google Scholar 

  • Khedekar DC, Truco AC, Oteyza DA, Huertas GF (2017) Home automation-a fast-expanding market. Thunderbird Int Bus Rev 59(1):1–13

    Article  Google Scholar 

  • Kleijnen M, de Ruyter K, Wetzels M (2007) An assessment of value creation in mobile service delivery and the moderating role of time consciousness. J Retail 83(1):33–46

    Article  Google Scholar 

  • Lee DY, Lehto MR (2013) User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Comput Educ 61(1):193–208

    Article  Google Scholar 

  • Lee J-H, Song C-H (2013) Effects of trust and perceived risk on user acceptance of a new technology service. Soc Behav Personal Int J 41(4):587–597

    Article  Google Scholar 

  • Lee M-C (2009) Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electron Commer Res Appl 8(3):130–141

    Article  Google Scholar 

  • Liao SH, Hu DC, Chung YC, Huang AP (2021) Risk and opportunity for online purchase intention-a moderated mediation model investigation. Telemat Inform 62:101621

    Article  Google Scholar 

  • Li B, Yu J (2011) Research and application on the smart home based on component technologies and internet of things. Procedia Eng 15:2087–2092

    Article  Google Scholar 

  • Lippert SK, Forman H (2005) Utilization of information technology: examining cognitive and experiential factors of post-adoption behavior. IEEE Trans Eng Manage 52(3):363–381

    Article  Google Scholar 

  • Liu Y, Hong Z, Zhu J, Yan J, Qi J, Liu P (2018) Promoting green residential buildings: residents’ environmental attitude, subjective knowledge, and social trust matter. Energy Policy 112:152–161

    Article  Google Scholar 

  • Loureiro ML, McCluskey JJ, Mittelhammer RC (2001) Assessing consumer preferences for organic, eco-labeled, and regular apples. J Agric Resour Econ 26(2):404–416

    Google Scholar 

  • Mao X, Li K, Zhang Z, Jing L (2017) Design and implementation of a new smart home control system based on internet of things. In: Proceeding of 2017 international smart cities conference. IEEE

  • Marikyan D, Papagiannidis S, Alamanos E (2019) A systematic review of the smart home literature: a user perspective. Technol Forecast Soc Chang 138:139–154

    Article  Google Scholar 

  • Martins C, Oliveira T, Popovič A (2014) Understanding the Internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. Int J Inf Manage 34(1):1–13

    Article  Google Scholar 

  • Mishal A, Dubey R, Gupta OK, Luo Z (2017) Dynamics of environmental consciousness and green purchase behaviour: an empirical study. Int J Clim Change Strat Manag 9(5):682–706

    Article  Google Scholar 

  • Natarajan T, Balasubramanian SA, Kasilingam DL (2017) Understanding the intention to use mobile shopping applications and its influence on price sensitivity. J Retail Consum Serv 37:8–22

    Article  Google Scholar 

  • Nistor N, Heymann JO (2010) Reconsidering the role of attitude in the TAM: an answer to Teo (2009a). Br J Edu Technol 41(6):142–145

    Article  Google Scholar 

  • Paetz AG, Duetschke E, Fichtner W (2012) Smart Homes as a Means to sustainable energy consumption: a study of consumer perceptions. J Consum Policy 35(1):23–41

    Article  Google Scholar 

  • Pagiaslis A, Krontalis AK (2014) Green consumption behavior antecedents: environmental concern, knowledge, and beliefs. Psychol Mark 31(5):335–348

    Article  Google Scholar 

  • Pańkowska M, Pyszny K, Strzelecki A (2020) 'Users’ adoption of sustainable cloud computing solutions. Sustainability 12(23):1–21

    Article  Google Scholar 

  • Park C-K, Kim H-J, Kim Y-S (2014) A study of factors enhancing smart grid consumer engagement. Energy Policy 72:211–218

    Article  Google Scholar 

  • Park E, Kim S, Kim Y, Kwon SJ (2017) Smart home services as the next mainstream of the ICT industry: determinants of the adoption of smart home services. Univ Access Inf Soc 17(1):175–190

    Article  Google Scholar 

  • Park E, Ohm JY (2014) Factors influencing the public intention to use renewable energy technologies in South Korea: effects of the Fukushima nuclear accident. Energy Policy 65:198–211

    Article  Google Scholar 

  • Park HJ, Lee HS (2014) Product smartness and use-diffusion of smart products: the mediating roles of consumption values. Asian Soc Sci 10(3):54–61

    Article  Google Scholar 

  • Qian L, Yin J (2017) Linking Chinese cultural values and the adoption of electric vehicles: the mediating role of ethical evaluation. Transp Res Part d Transp Environ 56:175–188

    Article  Google Scholar 

  • Rannikko, & P. (1996) Local environmental conflicts and the change in environmental consciousness. Acta Sociol 39(1):57–72

    Article  Google Scholar 

  • Reinisch C, Kofler MJ, Iglesias F, Kastner W (2011) ThinkHome energy efficiency in future smart homes. EURASIP J Embed Syst 2011:1–18

    Article  Google Scholar 

  • Rizun M, Strzelecki A (2020) Students’ acceptance of the COVID-19 Impact on shifting higher education to distance learning in Poland. Int J Environ Res Public Health 17(18):6468

    Article  Google Scholar 

  • Rogers EV (1995) Diffusion of innovations, 4th edn. The Free Press, New York

    Google Scholar 

  • Schierz PG, Schilke O, Wirtz BW (2010) Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron Commer Res Appl 9(3):209–216

    Article  Google Scholar 

  • Schill M, Godefroit-Winkel D, Diallo MF, Barbarossa C (2019) Consumers’ intentions to purchase smart home objects: Do environmental issues matter? Ecol Econ 161:176–185

    Article  Google Scholar 

  • Sheng X, Zolfagharian M (2014) Consumer participation in online product recommendation services: augmenting the technology acceptance model. J Serv Mark 28(6):460–470

    Article  Google Scholar 

  • Shin J, Park Y, Lee D (2018) Who will be smart home users? An analysis of adoption and diffusion of smart homes. Technol Forecast Soc Chang 134:246–253

    Article  Google Scholar 

  • Shuhaiber A, Mashal I (2019) Understanding users’ acceptance of smart homes. Technol Soc 58:101110

    Article  Google Scholar 

  • Siyal M, Siyal S, Wu J, Pal D, Memon MM (2021) Consumer perceptions of factors affecting Online shopping behavior: an empirical evidence from foreign students in China. J Electron Commer Organ 19(2):1–16

    Article  Google Scholar 

  • Smale R, Spaargaren G, van Vliet B (2019) Householders co-managing energy systems: Space for collaboration? Build Res Inform 47(5):585–597

    Article  Google Scholar 

  • Soliman M, Abiodun T, Hamouda T, Zhou J, Lung CH (2013) Smart Home: integrating internet of things with web services and cloud computing. In: Proceeding IEEE international conference on cloud computing technology & science-volume IEEE

  • Teo T (2009) Is there an attitude problem? Reconsidering the role of attitude in the TAM. Br J Edu Technol 40(6):1139–1141

    Article  Google Scholar 

  • Teo T, Zhou M (2014) Explaining the intention to use technology among university students: a structural equation modeling approach. J Comput High Educ 26(2):124–142

    Article  Google Scholar 

  • Tsu Wei T, Marthandan G, Yee-Loong Chong A, Ooi KB, Arumugam S (2009) What drives Malaysian m-commerce adoption? An empirical analysis. Ind Manag Data Syst 109(3):370–388

    Article  Google Scholar 

  • Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365

    Article  Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Article  Google Scholar 

  • Wang J, Pham TL, Dang VT (2020a) Environmental consciousness and organic food purchase intention: a moderated mediation model of perceived food quality and price sensitivity. Int J Environ Res Public Health 17(3):850

    Article  Google Scholar 

  • Wang S, Wang J, Li J, Wang J, Liang L (2018a) Policy implications for promoting the adoption of electric vehicles: Do consumer’s knowledge, perceived risk and financial incentive policy matter? Transportation Research Part a: Policypractice 117:58–69

    Article  Google Scholar 

  • Wang S, Wang J, Yang F, Li J, Song J (2020b) Determinants of consumers’ remanufactured products purchase intentions: evidence from China. Int J Prod Res 58(8):2368–2383

    Article  Google Scholar 

  • Wang Y, Hazen BT (2016) Consumer product knowledge and intention to purchase remanufactured products. Int J Prod Econ 181:460–469

    Article  Google Scholar 

  • Wang Y, Wang S, Wang J, Wei J, Wang C (2018b) An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model. Transportation 47:397–415

    Article  Google Scholar 

  • Wei J, Zhao M, Wang F, Cheng P, Zhao D (2016) An empirical study of the volkswagen crisis in China: customers’ information processing and behavioral intentions. Risk Anal 36(1):114–129

    Article  Google Scholar 

  • Wei J, Zhu W, Marinova D, Wang F (2017) Household adoption of smog protective behavior: a comparison between two chinese cities. J Risk Res 20(7):846–867

    Article  Google Scholar 

  • Wu C-S, Cheng F-F, Yen DC, Huang Y-W (2011) User acceptance of wireless technology in organizations: a comparison of alternative models. Comput Stand Interf 33(1):50–58

    Article  Google Scholar 

  • Yang H, Lee H, Zo H (2017) User acceptance of smart home services: an extension of the theory of planned behavior. Ind Manag Data Syst 117(1):68–89

    Article  Google Scholar 

  • Yoon A, Jeong D, Chon J (2021) The impact of the risk perception of ocean microplastics on tourists’ pro-environmental behavior intention. Sci Total Environ 774:144782

    Article  Google Scholar 

  • Zelezny LC, Schultz PW (2000) Psychology of promoting environmentalism: promoting environmentalism. J Soc Issues 56(3):365–371

    Article  Google Scholar 

  • Zhang W, Liu L (2021) Unearthing consumers’ intention to adopt eco-friendly smart home services: an extended version of the theory of planned behavior model. J Environ Plann Manage. https://doi.org/10.1080/09640568.2021.1880379

    Article  Google Scholar 

  • Zografakis N, Sifaki E, Pagalou M, Nikitaki G, Psarakis V, Tsagarakis KP (2010) Assessment of public acceptance and willingness to pay for renewable energy sources in Crete. Renew Sustain Energy Rev 14(3):1088–1095

    Article  Google Scholar 

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Acknowledgements

We express our genuine appreciation to the Natural Science Foundation of Jiangsu Province of China (BK20190792) and Innovation and Entrepreneurship Doctoral Program of Jiangsu for supporting this study.

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Appendix: Measurement items and constructs

Appendix: Measurement items and constructs

Constructs and measurement items

Knowledge about eco-friendly smart home services

KN1: I know the performance (such as usage cost, operation procedure, improvement efficiency) of eco-friendly smart home services

KN2: I also know the environmental protection function of eco-friendly smart home services

KN3: I know eco-friendly smart home services more than other people around me

Perceived risk

PR1: I would not feel totally safe when using eco-friendly smart home services

PR2: I worry about whether eco-friendly smart home services will really perform as well as traditional home services

PR3: Repairing eco-friendly smart home services may involve important time losses

Environmental consciousness

EC1: I always purchase products that are less harmful to the environment

EC2: I have switched products for environmental reasons

EC3: I have convinced my family or friends NOT to buy products that are

harmful for the environment

EC4: I make every effort to buy paper products made of recycled paper

EC5: I do not buy household products that harm the environment

EC6: I will not buy products which have excessive packaging

Perceived ease of use

PEU1: I think there is much difference between eco-friendly smart home services and traditional home services

PEU2: I think my interaction with eco-friendly smart home services is clear and understandable

PEU3: I think the function of eco-friendly smart home services is not complicated

PEU4: In other words, eco-friendly smart home services are easy for me to use

Perceived usefulness

PU1: I think using eco-friendly smart home services can save energy

PU2: I think eco-friendly smart home services help reduce water pollution

PU3: I think using eco-friendly smart home services can reduce my electricity bill

PU4: I think using eco-friendly smart home services can help improve air quality

PU5: In other words, eco-friendly smart home are useful to environmental protection

Intention to use

INT1: I am willing to adopt eco-friendly smart home services in the near further

INT2: I plan to adopt eco-friendly smart home services in the near further

INT3: I will make an effort to adopt eco-friendly smart home services in the near further

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Zhang, W., Liu, L. How consumers’ adopting intentions towards eco-friendly smart home services are shaped? An extended technology acceptance model. Ann Reg Sci 68, 307–330 (2022). https://doi.org/10.1007/s00168-021-01082-x

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