Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study
Abstract Background Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is cond...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12913-022-08189-7 |
| _version_ | 1811224187471134720 |
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| author | Martien J. P. van Bussel Gaby J. Odekerken–Schröder Carol Ou Rachelle R. Swart Maria J. G. Jacobs |
| author_facet | Martien J. P. van Bussel Gaby J. Odekerken–Schröder Carol Ou Rachelle R. Swart Maria J. G. Jacobs |
| author_sort | Martien J. P. van Bussel |
| collection | DOAJ |
| description | Abstract Background Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer. Methods Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared. Results The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention. Conclusions Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA. |
| first_indexed | 2024-04-12T08:44:18Z |
| format | Article |
| id | doaj.art-b6c96056e1804a8faed0f14e95d5abf4 |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-04-12T08:44:18Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-b6c96056e1804a8faed0f14e95d5abf42022-12-22T03:39:45ZengBMCBMC Health Services Research1472-69632022-07-0122112310.1186/s12913-022-08189-7Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods studyMartien J. P. van Bussel0Gaby J. Odekerken–Schröder1Carol Ou2Rachelle R. Swart3Maria J. G. Jacobs4Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+Department of Marketing and Supply Chain Management, Maastricht UniversityTilburg School of Economics and Management, Department of Management, Tilburg UniversityDepartment of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+Tilburg School of Economics and Management, Department of Management, Tilburg UniversityAbstract Background Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer. Methods Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared. Results The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention. Conclusions Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA.https://doi.org/10.1186/s12913-022-08189-7Virtual assistants (VAs)ChatbotsConversational agentsHealthcareCancerPatients |
| spellingShingle | Martien J. P. van Bussel Gaby J. Odekerken–Schröder Carol Ou Rachelle R. Swart Maria J. G. Jacobs Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study BMC Health Services Research Virtual assistants (VAs) Chatbots Conversational agents Healthcare Cancer Patients |
| title | Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study |
| title_full | Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study |
| title_fullStr | Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study |
| title_full_unstemmed | Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study |
| title_short | Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study |
| title_sort | analyzing the determinants to accept a virtual assistant and use cases among cancer patients a mixed methods study |
| topic | Virtual assistants (VAs) Chatbots Conversational agents Healthcare Cancer Patients |
| url | https://doi.org/10.1186/s12913-022-08189-7 |
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