The Role of AI Chatbots in Relationship-Oriented Marketing Communication: A Study on Restaurant Managers' Perspectives
Author
Khan, Iffat Anis
Term
4. Semester
Publication year
2026
Submitted on
2026-06-01
Abstract
When restaurants adopt AI chatbots for customer contact, managers must balance efficiency with relationship quality, standardization with personalization, and automation with human empathy. Prior research has focused mainly on customer reactions, while managers’ own reasoning and day-to-day choices have received less attention. This study examines how restaurant managers view the role of AI chatbots in relationship-oriented marketing communication (customer communication aimed at building long-term relationships). Based on semi-structured interviews with nine restaurant managers in Aalborg, Denmark, and analyzed using reflexive thematic analysis (a systematic approach to identifying patterns in interview data with researcher reflexivity), the study finds that managers do not see AI as purely beneficial or purely threatening. They draw clear boundaries: complaints, allergy-related questions, and emotionally charged situations should stay with humans, while general and repetitive queries—such as opening hours—can be automated without harming the relationship. A central paradox emerges: managers value deep, memory-based recognition of customers but resist AI-driven personalization because its mistakes feel like broken trust rather than simple factual errors. In addition, the monitoring burden and a perceived loss of control can cancel out efficiency gains, leading some managers to reject AI altogether. Across the sample, three stances appear: proactive conditional adopter, conditional adopter, and rejector. All are guided by the same decision rule: the more emotional or high-stakes the interaction, the less suitable it is for AI. The study offers a task-based allocation framework (which tasks to automate versus keep human) and a practical typology to help managers navigate AI adoption in relationship-intensive service settings.
Når restauranter tager AI-chatbots i brug til kundekontakt, skal lederne balancere effektivitet mod relationskvalitet, standardisering mod personalisering og automatisering mod menneskelig empati. Forskningen har hidtil primært set på kunders reaktioner, mens ledernes egne overvejelser og praksis er mindre belyst. Dette studie undersøger, hvordan restaurantledere opfatter AI-chatbots’ rolle i relationsorienteret markedsføringskommunikation (kundekommunikation, der skal opbygge langvarige relationer). Udgangspunktet er semistrukturerede interviews med ni restaurantledere i Aalborg, Danmark, analyseret med refleksiv tematisk analyse (en systematisk måde at finde mønstre i interviewdata, hvor forskeren også reflekterer over sin rolle). Resultaterne viser, at lederne ikke ser AI som entydigt gavnlig eller truende. De sætter en klar grænse: klager, allergi- og andre følelsesladede henvendelser skal håndteres af mennesker, mens generelle og gentagne spørgsmål – som åbningstider – kan automatiseres uden at skade relationen til gæsterne. Et centralt paradoks er, at lederne værdsætter dyb, hukommelsesbaseret genkendelse af gæster, men afviser AI-drevet personalisering, fordi fejl opleves som brud på tillid snarere end som neutrale faktuelle fejl. Desuden kan behovet for overvågning og oplevet kontroltab udligne de forventede effektivitetsgevinster, hvilket får nogle til helt at afvise AI. På tværs af interviewene fremkommer tre tilgange: proaktiv betinget adopter, betinget adopter og afviser. Alle følger den samme beslutningsregel: jo mere følelsesmæssig eller risikofyldt en interaktion er, desto mindre egner den sig til AI. Studiet tilbyder et opgavebaseret fordelingsrammeværk (hvilke opgaver bør automatiseres, og hvilke bør forblive menneskelige) og en praktisk typologi, der kan hjælpe ledere med at navigere i AI-adoption i relationstunge servicemiljøer.
[This apstract has been rewritten with the help of AI based on the project's original abstract]