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Adoption of Artificial Intelligence in Chatbot Recommendation Systems for Complex Customer Preferences: A Case Study of Shopee E-Commerce

Scientific Journal of Informatics · Vol. 13 No. 2 (2026) · pp. 465–482

DOI
10.15294/sji.v11i2.48989
ISSN
2407-7658, 2460-0040
Published
2026-06-22
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Purpose: This study identifies and analyzes factors influencing AI chatbot recommendation system adoption and proposes optimization strategies based on user perceptions and organizational decisions. Methods: A qualitative case study was conducted with Shopee as the unit of analysis. Nine participants three internal Shopee personnel and six active users were interviewed and selected via purposive sampling. Thematic analysis followed three stages (open, axial, and selective coding) guided by the TOE–TAM framework. Trustworthiness was ensured through member checking, peer debriefing, and four criteria: credibility, transferability, dependability, and confirmability. Result: Shopee's AI chatbot delivers personalized recommendations but users frequently experience information overload from irrelevant results. Three TOE dimensions technology readiness, organizational readiness, and external pressure, were found to drive adoption, while four TAM factors perceived usefulness, ease of use, trust, and satisfaction, shape user acceptance. Five strategic recommendations are proposed: algorithm enhancement, data quality improvement, adaptive personalization, deeper customer profiling, and information overload reduction. Novelty: Prior studies examine organizational adoption (TOE) or user acceptance (TAM) of AI chatbots in isolation, leaving a gap in understanding how macro-level institutional readiness interacts with micro-level user cognitive barriers. This study addresses that gap by integrating TOE and TAM as a dual-perspective lens, explaining how institutional readiness spanning technology, organization, and environment directly reduces cognitive barriers during automated recommendations. The study further foregrounds the "Complex Customer Preferences vs. Information Overload" paradox as a central challenge: AI chatbots deployed to manage complex preferences often generate overload that undermines user trust and satisfaction, a tension prior TOE–TAM integrations have not addressed.

Keywords

Collaboration network of the authors

38 authors · 103 co-authorship links · 30 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Indonesia
  • Institut Pariwisata Trisakti
  • National Research and Innovation Agency
  • Universitas Teknokrat Indonesia
  • Badan Pusat Statistik
  • Bakrie University
  • STMIK Bumigora Mataram
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sofian LusaInstitut Pariwisata Trisakti5
Erisva Hakiki PurwaningsihUniversity of Indonesia3
Deden Sumirat HidayatNational Research and Innovation Agency3
Ryan Randy SuryonoUniversitas Teknokrat Indonesia2
Nandhita Zefania MaharaniUniversity of Indonesia2
Shabrina Salsabila KurniawanUniversity of Indonesia2
Prasetyo Adi Wibowo PutroUniversity of Indonesia2
Rini MuliahatiAffiliation unknown1
Herlambang PermadiBadan Pusat Statistik1
Puja Putri AbdullahUniversity of Indonesia1
Rina RahmawatiUniversity of Indonesia1
Assaf AriefUniversity of Indonesia1
Prasetyo AdiUniversity of Indonesia1
Ferdian Maulana AkbarUniversity of Indonesia1
Robby HermansyahUniversity of Indonesia1
Nadya SafitriUniversity of Indonesia1
Damayanti ElisabethUniversity of Indonesia1
Yeni ApriyanaAffiliation unknown1
Raden TrimanadiAffiliation unknown1
Rias Kumalasari DeviUniversity of Indonesia1
KautsarinaAffiliation unknown1
Muhamad Adhytia Wana Putra RahmadhanUniversity of Indonesia1
Kautsarina KautsarinaAffiliation unknown1
Rahma DestrianiUniversity of Indonesia1
Raihansyah Yoga AdhitamaUniversity of Indonesia1
Imairi EitiveniUniversity of Indonesia1
Elin CahyaningsihBakrie University1
Iqbal Caraka AltinoUniversity of Indonesia1
Wahyu Setyawan WibowoAffiliation unknown1
Husain HusainSTMIK Bumigora Mataram1
Handika MasbudiAffiliation unknown1
Sofianti IndriasariAffiliation unknown1
Nur Isni NirwanAffiliation unknown1
Adi Suryaputra ParamitaUniversitas Ciputra1
Harjanto PrabowoAffiliation unknown1
Arief RamadhanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Adoption of Artificial Intelligence in Chatbot Recommendation Systems for Complex Customer Preferences: A Case Study of Shopee E-Commerce</dc:title>
	<dc:creator xml:lang="en">Sijabat, Apriyanti</dc:creator>
	<dc:creator xml:lang="en">Sensuse, Dana Indra </dc:creator>
	<dc:subject xml:lang="en">AI  Chatbot</dc:subject>
	<dc:subject xml:lang="en">Recommendation</dc:subject>
	<dc:subject xml:lang="en">Preferences</dc:subject>
	<dc:subject xml:lang="en">E-Commerce</dc:subject>
	<dc:description xml:lang="en">Purpose: This study identifies and analyzes factors influencing AI chatbot recommendation system adoption and proposes optimization strategies based on user perceptions and organizational decisions.
Methods: A qualitative case study was conducted with Shopee as the unit of analysis. Nine participants three internal Shopee personnel and six active users were interviewed and selected via purposive sampling. Thematic analysis followed three stages (open, axial, and selective coding) guided by the TOE–TAM framework. Trustworthiness was ensured through member checking, peer debriefing, and four criteria: credibility, transferability, dependability, and confirmability.
Result: Shopee's AI chatbot delivers personalized recommendations but users frequently experience information overload from irrelevant results. Three TOE dimensions technology readiness, organizational readiness, and external pressure, were found to drive adoption, while four TAM factors perceived usefulness, ease of use, trust, and satisfaction, shape user acceptance. Five strategic recommendations are proposed: algorithm enhancement, data quality improvement, adaptive personalization, deeper customer profiling, and information overload reduction.
Novelty: Prior studies examine organizational adoption (TOE) or user acceptance (TAM) of AI chatbots in isolation, leaving a gap in understanding how macro-level institutional readiness interacts with micro-level user cognitive barriers. This study addresses that gap by integrating TOE and TAM as a dual-perspective lens, explaining how institutional readiness spanning technology, organization, and environment directly reduces cognitive barriers during automated recommendations. The study further foregrounds the "Complex Customer Preferences vs. Information Overload" paradox as a central challenge: AI chatbots deployed to manage complex preferences often generate overload that undermines user trust and satisfaction, a tension prior TOE–TAM integrations have not addressed.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-06-22</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/48989</dc:identifier>
	<dc:identifier>10.15294/sji.v11i2.48989</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 13 No. 2: Regular Issue; 465-482</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v13i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/48989/10269</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
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