Using means-end theory to understand technology acceptance: a systematic literature review


Context: Means-end chains consist of a sequence of attributes, consequences, and values. These attributes can be understood as characteristics of a product; the consequences can be seen as benefits arising from this product's use; the values represent the end goals important to the consumer or the motivation to use the product. Means-end chain analysis originates in marketing and relates consumers and products. In the context of systems, what drives a product to be accepted by users. Problem: To understand the application of the means-end theory in the IS area to identify factors that lead to user acceptance of a system. Solution: To conduct a Systematic Literature Review (SLR) to understand how means-end theory helped understand user acceptance of technology. IS Theory: This work was inspired by the Technology Acceptance Model Theory, which helps to determine an individual's intention to use a system. Methodology adopted: The SLR searched for scientific articles published in the last five years that contained the keywords “means-end”, “technology adoption”, “technology acceptance”, “technology resistance”, “user adoption”, “user acceptance” and “user resistance”. The search was carried out in the databases of ACM, Elsevier (ScienceDirect), and IEEE, and also by Google Scholar. Summary of results: 13 articles were selected. From the analysis of these articles, we extracted guidelines on data collection, number of recommended participants, indicators of data reliability, and data analysis. Contribution and Impact in the IS area: The results can benefit further studies applying means-end theory for analysis and survey of software requirements, particularly considering the most important values for end users.
Palavras-chave: Means-end, technology adoption, technology acceptance, literature review


Vero Vanden Abeele, Erik Hauters, and Bieke Zaman. 2012. Increasing the Reliability and Validity of Quantitative Laddering Data with LadderUX. In CHI ’12 Extended Abstracts on Human Factors in Computing Systems (Austin, Texas, USA) (CHI EA ’12). Association for Computing Machinery, New York, NY, USA, 2057–2062.

Vero Vanden Abeele, Brenda Schraepen, Hanne Huygelier, Celine Gillebert, Kathrin Gerling, and Raymond Van Ee. 2021. Immersive Virtual Reality for Older Adults: Empirically Grounded Design Guidelines. ACM Trans. Access. Comput. 14, 3, Article 14 (aug 2021), 30 pages.

Ahlam Mohammed Al-Abdullatif and Hibah Khalid Aladsani. 2021. Understanding Instructors’ Cognitive Structure Toward the Academic Use of Social Network Sites: The Means-End Chain Theory. SAGE Open 11, 3 (2021), 21582440211029927.

Grace Ambrose, Juan Meng, and Paul Ambrose. 2020. Why do millennials use Facebook? Enduring insights. Qualitative Market Research: An International Journal ahead-of-print (01 2020).


Chrysostomos Apostolidis, David Brown, Dinuka Wijetunga, and Eranjana Kathriarachchi. 2021. Sustainable value co-creation at the Bottom of the Pyramid: using mobile applications to reduce food waste and improve food security. Journal of Marketing Management 37 (01 2021), 1–31.

Ubiratan Bueno, Ronaldo Zwicker, and Mauri Aparecido de Oliveira. 2004. Um estudo comparativo do modelo de aceitação de tecnologia aplicado em sistemas de informações e comércio eletrônico. In CONGRESSO INTERNACIONAL DE GESTÃO DE TECNOLOGIA E SISTEMAS DE INFORMAÇÃO (São Paulo, SP). 

Fred Davis. 1985. A Technology Acceptance Model for Empirically Testing New End-User Information Systems. (01 1985). 

Maarten Denoo, Niels Bibert, and Bieke Zaman. 2021. Disentangling the Motivational Pathways of Recreational Esports Gamblers: A Laddering Study. Association for Computing Machinery, New York, NY, USA.

Josivania Silva Farias, Susy Sanders, Carlos Denner dos Santos Jr., and Késia Rozzett. 2014. Aceitação de tecnologia em terminais de autosserviço aeroportuários: explorando os efeitos dos moderadores idade, experiência e gênero. In Anais do X Simpósio Brasileiro de Sistemas de Informação (Londrina). SBC, Porto Alegre, RS, Brasil, 66–77.

Klaus G. Grunert and Suzanne C. Grunert. 1995. Measuring subjective meaning structures by the laddering method: Theoretical considerations and methodological problems. International Journal of Research in Marketing 12, 3 (1995), 209–225.

J. Gutman. 1982. A Means-End Chain Model Based on Consumer Categorization Processes. Journal of Marketing 46, 2 (1982), 60–72.

Jonathan Gutman and George Miaoulis. 2003. Communicating a quality position in service delivery: An application in higher education. Managing Service Quality 13 (04 2003), 105–111.

Songyee Han, Gheorghita Ghinea, and Tor-Morten Groenli. 2017. Critical Success Factors in Virtual-Reality Based Marketing Ecosystems. In Proceedings of the 9th International Conference on Management of Digital EcoSystems (Bangkok, Thailand) (MEDES ’17). Association for Computing Machinery, New York, NY, USA, 216–223.

Jörg Heinze, Matthias Thomann, and Peter Fischer. 2017. Ladders to m-commerce resistance: A qualitative means-end approach. Computers in Human Behavior(2017), 362 – 374. 

Mei-Yuan Jeng, Tsu-Ming Yeh, and Fan-Yun Pai. 2020. Analyzing Older Adult's Perceived Values of Using Smart Bracelets by Means-End Chain. Healthcare 8 (nov. 2020).

Barbara Kitchenham, O. Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman. 2009. Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology 51, 1 (2009), 7–15. Special Section - Most Cited Articles in 2002 and Regular Research Papers.

Cheng-Shih Lin, Mei-Yuan Jeng, and Tsu-Ming Yeh. 2018. The Elderly Perceived Meanings and Values of Virtual Reality Leisure Activities: A Means-End Chain Approach. International Journal of Environmental Research and Public Health 15, 4(2018).

Hong-Wen Lin and Yu-Ling Lin. 2014. Digital educational game value hierarchy from a learners’ perspective. Computers in Human Behavior 30 (2014), 1 – 12.

Artur Martins Mol, Hebert Phillipe Martins Pereira, Ana Luiza do Nascimento Guercy, Michelle Nery Nascimento, Mônica da Consolação Machado, and Lucila Ishitani. 2021. Atributos e Valores para o Desenvolvimento de Jogos para Idosos. Abakós 9, 2 (nov. 2021), 3–24.

Gabriele Morandin, Richard P. Bagozzi, and Massimo Bergami. 2013. Brand community membership and the construction of meaning. Scandinavian Journal of Management 29, 2 (2013), 173–183. SI: On Being Branded.

E.Y. Nakagawa, K.R.F. Scannavino, S.C.P.F. Fabbri, and F.C. Ferrari. 2017. Revisão Sistemática da Literatura em Engenharia de Software: Teoria e Prática. Elsevier Brasil.

J C. Olson and J. P. Peter. 2008. Consumer Behavior and Marketing Strategy. McGraw-Hill, New York. 

Carlos Osorio, Rob Wilson, and Savvas Papagiannidis. 2017. Social Networking Sites Withdrawal. In International Conference on Social Informatics. Springer, 391–408.

Ha Park, Sheau-Fen Yap, and Marian Makkar. 2019. A laddering study of motivational complexities in mobile shopping. Marketing Intelligence and Planning 37, 2 (2019), 15. 

J.P. Peter and J.C. Olson. 2009. Comportamento Do Consumidor:Estrategias De Mkt. AMGH. 

J.P. Peter and J.C. Olson. 2010. Consumer Behavior & Marketing Strategy. McGraw-Hill Irwin. 

J Paul Peter and JC Olson. 1996. Consumer Behavior and Marketing Strategy, Chicago: Richard D. Irwin. 

Thomas J Reynolds and Jonathan Gutman. 1988. Laddering theory, method, analysis, and interpretation. Journal of advertising research 28, 1 (1988), 11–31. 

T. J. Reynolds and J. C. Olson. 2001. Understanding Consumer Decision Making. Routledge, London. 

Raja Sankaran and Shibashish Chakraborty. 2020. Why customers make mobile payments? Applying a means-end chain approach. Marketing Intelligence & Planning ahead-of-print (07 2020).

Jagdish N Sheth, Banwari Mittal, and Bruce I Newman. 1999. Customer behavior: Consumer behavior and beyond. Dryden Press Fort Worth, TX. 

Patrícia Maria Silva and Guilherme Ataíde Dias. 2008. Teorias sobre aceitação de tecnologia: por que os usuários aceitam ou rejeitam a tecnologia da informação?Brazilian Journal of Information Science: research trends 1, 2 (jul. 2008), 69–91.

Timothy Teo. 2011. Technology Acceptance Research in Education. SensePublishers, Rotterdam, 1–5.

Mei-Hsiang Wang, Tarng-Yao Yang, and Chih-Cheng Wei. 2018. Creating Social Networking Environment for Workers through a Means-End Chain Model. Journal of e-Business 20, 1 (Jun 2018), 1–32.

Yu-Lung Wu, Chi-Wen Chang, and Chi-Jui Chang. 2014. Exploring the Value of Cloud Services and QoE Factors on IPCentrex via the Means-End Chain Framework. In Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia (Kaohsiung, Taiwan) (MoMM ’14). Association for Computing Machinery, New York, NY, USA, 363–366.

Lin Xiao, Zixiu Guo, and John D'Ambra. 2017. Analyzing consumer goal structure in online group buying: A means-end chain approach. Information & Management 54, 8 (2017), 1097–1119.
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MOL, Artur Martins; MACHADO, Mônica da Consolação; ISHITANI, Lucila. Using means-end theory to understand technology acceptance: a systematic literature review. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 18. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 .