Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study

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Understanding the factors that influence this demographic is essential for promoting digital inclusion and enhancing the quality of life. Therefore, this study aims to explore these factors based on a qualitative research approach. Methods This study employed a structured interview approach to investigate the factors influencing the adoption of mobile apps among older adults. The study uses the purposive sampling method. The only older adults who use smartphones are included in the interviews. Fourteen participants aged 60 years and above were interviewed. All the interviews were audio-recorded, transcribed verbatim, and analysed using thematic analysis. The data were coded, and several factors were identified and categorized based on existing theories and models, such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, and Perceived Risk Theory. Results Based on the thematic analysis, several factors were identified and categorized in relation to existing theories and models, including the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, and Perceived Risk Theory. The factors that emerged were Perceived Usefulness, Perceived Ease of Use, Social Influence, Facilitating Condition, and Perceived Risk. The study also develops a conceptual model and propositions based on the identified factors. Conclusion Based on the qualitative insights and thematic analysis, this study identifies the factors influencing the mobile adoption of older adults. The findings reveal that older adults are likely to adopt mobile apps when they perceive them as useful and easy to use. Privacy and financial risk emerged as major barriers for older adults in using the mobile apps. However, older adults are likely to use the mobile apps if proper training is provided to them. 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F1000Research 2026, 15 :552 ( https://doi.org/10.12688/f1000research.176730.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study [version 1; peer review: 1 not approved] Raghavendra G https://orcid.org/0000-0002-9708-1040 1 , Jyothi Mallya https://orcid.org/0000-0003-4496-4680 2 , Shashidhara Y N https://orcid.org/0000-0003-1703-9086 3 , Poornima Panduranga Kundapur https://orcid.org/0000-0002-8271-524X 4 , Lena Ashok 5 Raghavendra G https://orcid.org/0000-0002-9708-1040 1 , Jyothi Mallya https://orcid.org/0000-0003-4496-4680 2 , [...] Shashidhara Y N https://orcid.org/0000-0003-1703-9086 3 , Poornima Panduranga Kundapur https://orcid.org/0000-0002-8271-524X 4 , Lena Ashok 5 PUBLISHED 18 Apr 2026 Author details Author details 1 IT, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India 2 Library, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India 3 Community Health Nursing, Manipal College of Nursing, Manipal Academy of Higher Education, Manipal, India 4 School of Computer Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 5 Department of Social and Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India Raghavendra G Roles: Conceptualization, Data Curation, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Jyothi Mallya Roles: Formal Analysis, Software, Writing – Original Draft Preparation, Writing – Review & Editing Shashidhara Y N Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Poornima Panduranga Kundapur Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Lena Ashok Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Manipal Academy of Higher Education gateway. Abstract Background The rapid growth of mobile technology has significantly influenced individuals' daily lives; however, there are limited studies on how older adults adopt these technologies. Understanding the factors that influence this demographic is essential for promoting digital inclusion and enhancing the quality of life. Therefore, this study aims to explore these factors based on a qualitative research approach. Methods This study employed a structured interview approach to investigate the factors influencing the adoption of mobile apps among older adults. The study uses the purposive sampling method. The only older adults who use smartphones are included in the interviews. Fourteen participants aged 60 years and above were interviewed. All the interviews were audio-recorded, transcribed verbatim, and analysed using thematic analysis. The data were coded, and several factors were identified and categorized based on existing theories and models, such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, and Perceived Risk Theory. Results Based on the thematic analysis, several factors were identified and categorized in relation to existing theories and models, including the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, and Perceived Risk Theory. The factors that emerged were Perceived Usefulness, Perceived Ease of Use, Social Influence, Facilitating Condition, and Perceived Risk. The study also develops a conceptual model and propositions based on the identified factors. Conclusion Based on the qualitative insights and thematic analysis, this study identifies the factors influencing the mobile adoption of older adults. The findings reveal that older adults are likely to adopt mobile apps when they perceive them as useful and easy to use. Privacy and financial risk emerged as major barriers for older adults in using the mobile apps. However, older adults are likely to use the mobile apps if proper training is provided to them. READ ALL READ LESS Keywords perceived usefulness, perceived ease of use, social influence, facilitating conditions, barriers, financial risk, privacy risk. Corresponding Author(s) Shashidhara Y N ( [email protected] ) Close Corresponding author: Shashidhara Y N Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 G R et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: G R, Mallya J, Y N S et al. Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :552 ( https://doi.org/10.12688/f1000research.176730.1 ) First published: 18 Apr 2026, 15 :552 ( https://doi.org/10.12688/f1000research.176730.1 ) Latest published: 18 Apr 2026, 15 :552 ( https://doi.org/10.12688/f1000research.176730.1 ) Introduction Mobile applications, also known as mobile apps, have revolutionized the way people interact with technology and various aspects of daily life ( Kwon, 2014 ). They have become an integral part of modern life, offering convenience in multiple areas, including communication, healthcare, banking, and social engagement. However, despite its advantages, one of the important segments of the population-older adults-often remains marginalized due to challenges such as cognitive overload, lack of digital literacy, and accessibility barriers that persist ( Amouzadeh et al., 2025 ). This hindrance not only restricts access to many services but also impacts their overall quality of life. Global aging, also known as population aging, is a significant demographic trend that is expected to continue for the next 50 to 60 years, peaking around 2080. By 2050, one in six people worldwide will be over the age of 65, up from one in 11 in 2019 ( World Health Organization, 2025 ). The inclusion of older adults in digital ecosystems is essential for promoting independence, social connectivity, and overall well-being. Mobile apps have the potential to enhance the quality of life ( Mattli et al., 2025 ) by providing access to healthcare services ( Siripipatthanakul & Siripipattanakul, 2025 ), facilitating financial transactions ( Bhosale et al., 2023 ), and promoting social interaction ( Pratap Singh et al., 2025 ). Yet, adoption among older adults remains relatively low compared to younger populations ( Gu et al., 2025 ). Thus, understanding the underlying barriers and challenges is crucial for designing inclusive technologies. Older adults face several challenges in adopting mobile apps, including usability issues, cognitive barriers, and socio-economic factors. For example, usability issues include the small fonts, complex navigation, and unintuitive design ( Khamaj & Ali, 2024 ; Li & Luximon, 2020 ). Studies have shown that they require initial support from family or caregivers, which can significantly enhance their adaptation to mobile apps ( Lai et al., 2025 ; Nautiyal et al., 2024 ). Older adults who receive family support and find emotional satisfaction in using apps are more likely to continue using them ( Lai et al., 2025 ). However, fear of committing mistakes may hinder their use of mobile apps ( Wong et al., 2018 ). Findings from earlier studies indicate that perceived usefulness and ease of use are crucial for older adults. Mobile apps perceived as beneficial and easy to use are more likely to be adopted by older adults ( Lai et al., 2025 ). While existing studies focus on quantitative measures of mobile app adoption among older adults, the qualitative exploration of factors impacting older adults' usage, perception, and barriers remains unexplored. This study aims to fill this gap by providing rich, qualitative insights into the factors influencing mobile app adoption among older adults. Thus, the primary objective of this qualitative study is to investigate the age-specific factors that influence the adoption of mobile apps among older adults. To achieve the stated purpose, this study answers the following questions: 1) What is the current usage of mobile apps by older adults? 2) What is the experience of older adults while using mobile apps? and 3) What barriers, challenges, and behavioral intentions of older adults using mobile applications? Accordingly, the study has the following research objectives. • To examine the purpose, usefulness, and different contexts of mobile app usage. • To understand the experience of older adults and to identify the primary sources of support. • To identify the major barriers, challenges, and behavioral intention to use the mobile app. The remainder of the study is as follows. The research methodology section includes research design, sampling methods, data collection, ethical considerations, data analysis, and participant profile. The results section analyses the qualitative insights provided by the participants in line with the study's objectives. The discussion section includes detailed information on the identified factors influencing the adoption of mobile apps by older adults. Additionally, based on the identified factors and variables, the study proposes a conceptual framework for future research directions. The theoretical and practical implications are also discussed. Based on the study's limitations, future research directions are also recommended. Research methodology Research design This study employed an exploratory qualitative research design to gain an in-depth understanding of older adults' lived experiences and perceptions regarding their adoption of mobile apps. This design was considered appropriate because it allows for the exploration of older adults' perceptions, experiences, barriers, challenges, and factors influencing the actual usage of mobile apps that cannot be adequately captured with other methods, such as quantitative ones. Given the inductive nature of the study, no prior theoretical frameworks were applied before the data collection. However, relevant theories/frameworks will be considered during the interpretation phase to contextualize findings within broader scholarly discourse. Sampling method The study employed a purposive sampling method. The participants with smartphones are only included in the study. Initially, the lead researcher explained the study's objectives. Participants who have agreed to and expressed their willingness are only included in the interview. All participants were previously unknown to the interviewer, reducing potential interviewer bias. There are 14 participants aged between 60 and 75. Some participants were members of registered elderly welfare organisations. The older adults who are not part of any elderly welfare organisations were also included. Data collection Data were collected through semi-structured interviews ( Gopal, 2026a ). The interviews were conducted in person at the premises of the elderly welfare organisations and other convenient locations as recommended by the participants. Each interview lasted approximately 30 minutes. It focused on participants' awareness of mobile applications, the types of apps they used, their frequency of usage, usefulness, perceived benefits, barriers, challenges, and factors influencing their adoption. Ethical considerations Ethical approval for the study was obtained from the institutional ethics committee (No. 214/2020) of Kasturba Medical College and Kasturba Hospital, Manipal Academy of Higher Education, Manipal, India (Registration No.: ECR/146/Inst/KA/2013/RR-19). This study is also registered under the Clinical Trials Registry - India (CTRI/2020/09/027977). All participants were well-informed about the study's objectives and expectations during the interview. Additionally, written informed consent was also obtained from the participants. Participants were assured of the confidentiality and anonymity of personal data. They were also informed that participation in this survey was voluntary and that they could withdraw from the study at any time. The data was for this study was collected between 1 st October 2022 and 31 st June 2023. Data analysis We conducted in-depth content analysis within a constructivist paradigm ( Green & Thorogood, 2018 ). All structured interviews were audio-recorded and transcribed. The two authors independently performed open coding, followed by axial coding, grouping codes into abstract ones. This process resulted in initial concepts. In the next step, the resulting themes and subthemes were discussed and finalized. During this process, all authors were involved. We used the constant comparison technique to compare existing concepts and themes with new data ( Green & Thorogood, 2018 ). The participants' demographic details are analysed using MS Excel. Participants profile The study comprised 14 older adults, including nine males (64.3%) and five females (35.7%). In terms of occupation, seven participants were retired (50.0%), three were housewives (21.4%), two were engaged in business (14.3%), and 2 (14.3%) did not specify. Educationally, 7 participants held an undergraduate degree (50.0%), 5 had education below undergraduate level (35.7%), and 2 were postgraduates (14.3%). Regarding marital status, seven were married (50.0%), four were widowers (28.6%), one was a widow (7.1%), and two did not disclose their status (14.3%). Geographically, eight participants resided in urban areas (57.1%), while six were from rural locations (42.9%). Results Before moving on to the main research question, we asked the participants about their current smartphone usage and the purposes for which they used their smartphones, followed by a series of research questions aligned with the research objectives. Smart phone usage First, we asked the participants about their smartphone usage. The responses are coded as follows. Phrases such as "since inception," "since the beginning," and "since the availability of smartphones" were coded as referring to early adopters. Similarly, phrases such as "recently" were coded as "recent adopters." Responses that specify duration (For example, seven years, about five years) are classified as duration-based adopters. The analysis suggests that there were 50% early adopters, indicating long-term smartphone usage. Recent adopters (20%) mentioned starting "recently," suggesting limited exposure and potential need for onboarding support, such as digital literacy programs. The remaining 30% specified durations of 5–7 years (mean = 6 years), highlighting sustained use among some participants. This variation highlights the differing usage, level of confidence, and training needs within the senior citizen cohort. Purpose The next question was about the purpose of using a mobile app. We counted the number of times an app or category was mentioned across all responses. The top mentions were communication. Of the 89 mentions, 30 were related to communication, including WhatsApp calls, emails, and SMS. Payments and banking had emerged as the second-most-used mobile app. The other usage of mobile apps is presented in the following table ( Table 1 ). Table 1. Mobile app usage. Category Mentions Example apps Communication 30 WhatsApp; Phone Call; SMS; Email/Gmail Payments/Banking 13 Google Pay; PhonePe; Mobile/Net Banking; Bank (general) Entertainment 11 YouTube; Entertainment; Music; Sports; Yakshagana Social media 8 Facebook; Instagram; Twitter; Koo Travel/Ticketing 5 IRCTC; RailYatri; Cleartrip; ixigo Utility 5 Reminder apps; Camera/Photos; Video capture Shopping 4 Amazon; Tata CLiQ Others 13 Health & wellness (3), Health Insurance (2), e‑Government (2) (life certificate apps), News (2), Food/Ordering (1) (Swiggy), Productivity (1) (Zoom), Security (1) (CCTV), Work/Professional (1) (LIC Agent app) Usefulness When we asked about the usefulness of mobile apps, participants overwhelmingly described them as useful, particularly for communication and staying connected. Several respondents emphasized the role of mobile apps in maintaining relationships and exchanging information, stating "Very useful to contact and for information exchange" (R1) and "Useful to communicate with family members and customers" (R7). "To send greetings, it is very useful" (R7). The importance of apps during the pandemic was highlighted through comments like "Very useful, including online meetings in the period of COVID" (R2) and "Covid time online meetings, phone calls, WhatsApp were only used to be in touch" (R4), "I remember it was useful at the time of COVID" (R11), "Pandamic time time, it was useful to connect" (R12). WhatsApp emerged as a preferred tool, with one participant noting, "Use only WhatsApp to be in touch and communicate" (R5). At the same time, another shared, "I like the Voice Call and WhatsApp apps, easy and helps to communicate and also to gossip (she laughs)" (R10). Despite these positive views, a note of caution appeared, suggesting that "Even though useful, maybe it is risky sometimes" (R8), which raises concerns about privacy or misuse. Overall, the narratives reflect strong perceived usefulness for social interaction and information exchange, with occasional reservations about safety. Contexts To understand the different types of mobile apps used by older adults, we asked them to name a few. Since no single mobile app emerged, the interviewer provided the names of various types of mobile apps, including fitness apps, health apps, and reminder apps. Many respondents reported not using any mobile health (mHealth) apps. Most respondents said, "Not using any apps related to healthcare" and a few of them added, "I personally visit the hospital as per the regular timings" (R4), "Phone Call is easy" (R8), "I don't know about medication or healthcare apps" (R11) , suggesting a preference for the traditional method of personal visits. However, three respondents reported using the Mediclaim app for consultations and hospitalization, and they found it very useful. One respondent was using reminders for that purpose, saying "Reminder apps are used" (R9), which indicates their unawareness. The most common reason for not using any mHealth app is a lack of awareness. Additionally, we also inquired about the mobile app's usage for other purposes, such as food ordering, banking, and travel. The insights are discussed below. Food ordering The responses from the older adults revealed that many of them are aware of food-related apps, but their actual usage is limited. Several respondents expressed that they are dependent on family members for such activities. For example, one respondent stated, " Grandchildren do it for me, so I do not want food ordering apps" (R2), and another respondent said, "Children do it for me, so I do not want food ordering apps" (R5). Others reported complete non-usage, stating "Not using any food-related apps" or "Never used." However, situational adoption was noted during the pandemic: "Covid time, a few food ordering Apps were very useful" (R4). A few respondents mentioned using recipe apps rather than ordering apps: "I use apps to watch Food Preparation" (R10) . Therefore, it can be concluded that there is a preference for traditional methods amongst older adults, which is evident in responses such as "Whenever food order is required, I use phone call. I find it difficult using so many options on the phone" (R10). These findings suggest that complexity, perceived lack of need, and reliance on family are major barriers to adoption. Banking, payment, and purchase Regarding the usage of Banking, Payment, and Purchase apps, responses indicate a mixed level of comfort. It is found that some older adults actively use apps like Google Pay for payment and Amazon for purchasing online, as reflected in "Using mobile banking and mobile payment apps like Google Pay, Phone pay. For purchase, also comfortable in using apps. Also using Amazon for purchase" (R1) . Additionally, they found it easy to use these apps, as reflected in the statement " Using payment, banking, and finance related apps. I find it very easy. For purchase, Amazon is used the most " (R9). However, hesitation and fear were also common among older adults. A few respondents said, "I hesitate to use" (R3), "Sometimes I am scared" (R10), "I am not aware and also do not know how to use so hesitate to use these apps" (R3), and "I don't know using financial related, and I am also scared" (R13). Several respondents expressed their preference for traditional methods, stating "Using Debit cards only, I find easy than mobile apps" (R4) and "ATM Card are used to purchase" (R7). Most importantly, some expressed willingness to adopt if provided with guidance: "If I get more details and assistance may be useful" (R7). This highlights the role of digital literacy and trust in influencing the adoption of new technologies. Travel The responses from the older adults suggest that the usage of travel-related apps is minimal, with only a few respondents reporting comfort with platforms like IRCTC and Clear Trip as seen in comments " Using IRCTC, Clear Trip, ixigo are comfortable " (R1) and "Rail Yatra is used occasionally" (R2). Most respondents do not use apps for travel, as seen in "Not using any of these apps" (R3, R5) and "I use only Phone Calls; I have contact with them (travel operators), and it is easy for all services" (R8), "Apps are not available, or I am not aware. If available, I want to use I am currently using phone call to get these services" (R9), and " I heard about only booking, but never used " (R11). The preference for phone calls and personal contacts suggests that convenience and familiarity outweigh perceived benefits of app-based services for this demographic. Experience and sources of support The second research objective was to understand the overall experience of older adults with using mobile apps and to identify the sources of support for using these apps. Older adults reported a range of experiences when they first started using mobile applications. Several participants noted that they "found useful and started using" (R1, R4) or "slowly started using" (R2), reflecting a cautious and gradual approach. Others conveyed discomfort, saying they "hesitate to use the mobile apps" (R3) or were "not very comfortable" (R6). Fear was also evident, as one respondent shared, "I was scared in the beginning, if anything goes wrong" (R10), "A Little worried" (R11, R14). Another emphasized deliberate pacing: " I wanted to use slowly one by one" (R8), "Just started when I came to know one by one" (R11). These narratives illustrate an initial apprehension, balanced by a perceived usefulness, leading to a slow but steady adoption. Participants expressed mixed feelings about their current experience with mobile applications. Many described a sense of comfort and utility, stating "Using mobile apps is very useful, and I am very comfortable" (R1) , "It gives a lot of options, apps are user-friendly, and good" (R1) , and "Apps are very useful" (R5) . Others highlighted the selective use, saying , "I like the apps for communication like WhatsApp and Facebook" (R5) . However, hesitation persists for some, as reflected in "I hesitate to use these apps" (R6), " Little worried to use mobile apps " (R14), and concerns such as "Apps use a lot of our time" (R4) . A few participants noted progress, sharing, "Now I am comfortable using the applications which I am using at present" (R10). These narratives reveal a spectrum from confidence and appreciation to lingering reluctance, underscoring the need for ongoing support and time-saving strategies. When respondents were asked about assistance in using a mobile app, family assistance was identified as the primary source of help. There were some respondents learnt themselves. Two respondents stated that they did not receive any assistance, and one respondent reported receiving help from the business organisation. Barriers and challenges Our third research objective was to identify the major barriers and challenges of using mobile apps. The feedback from the older adults reveals several concerns. Respondents expressed, " I do not know much about these apps " (R3) and " We do not have that much confidence to use payment or finance apps" (R6), "If information is available may be easy to use " (R11), highlighting knowledge and confidence gaps. Some respondents also expressed the stress they experienced due to technical issues while using such apps. One respondent noted, " If any app hangs or responds slowly, we get anxiety " (R5). Privacy and security fears are also very prominent. For example, a few respondents said, " We are concerned about our data, information related to bank accounts, etc., What if something goes wrong? " (R1) and " I think it is risky using payment or banking-related apps " (R8, R14). Additionally, discomfort with unfamiliar applications was evident, as indicated by responses such as " Other Apps I do not want to use, as I am not comfortable with unknown apps" (R2) and " I don't like receiving unknown and unnecessary calls" (R2, R13). Thus, based on the feedback provided by the older adult, the following items of perceived barriers were identified ( Table 2 ). Table 2. Barriers to mobile app adoption. Barrier variable Evidence (Respondent statements) Knowledge Deficit I do not know much about these apps; We do not know how to use many apps; I am not aware of other apps. Lack of confidence or Self-efficacy We do not have that much confidence to use payment or finance apps. Security Anxiety We are concern about our data, information related to bank accounts etc., What if something goes wrong? I think it is risk using payment or banking related apps. Performance Anxiety If any app hangs or responds slowly, we get anxiety. Discomfort with Unknown apps Other Apps I do not want to use, as I am not comfortable with unknown apps; By watching my husband using them and by using only few known apps. Distress I don't like receiving unknown and unnecessary calls. That is troublesome. Support Dependency If I get help may be useful; We senior citizens need assistance to use these applications; My son tells me to use mobile apps and sometimes he teaches. Limited Exposure/Orientation If a person is exposed, like awareness or by watching influence people use these mobile applications; Exposing to mobile apps influence people to use it. Fear/Risk Perception I am scared if anything goes wrong; What if something goes wrong. Discouraging Social Influence My daughter also says don't use all applications. Ease of use If information is available may be easy to use. Availability/Convenience Constraint When I need and find available. Challenges Older adults expressed concern about their data and information related to bank accounts, asking, "What if something goes wrong?" (R1) . They emphasized that, as senior citizens, they require assistance in using these applications. Many expressed discomfort with unknown apps, saying, "Other apps I do not want to use, as I am not comfortable with unknown apps" (R2) and highlighted that receiving unknown and unnecessary calls is troublesome. Additionally, they admitted, "We do not know how to use many apps" (R5), and " Yet to explore many apps, but not immediately " (R14), which reflects a lack of digital literacy and confidence. These responses indicate that privacy concerns, fear of misuse, and difficulty navigating technology are major barriers, suggesting the need for personalized support and simplified interfaces to improve mHealth app adoption among older adults. The following table ( Table 3 ) represents the challenges identified from the responses of older adults. Table 3. Challenges of mobile app adoption. Challenges Original response Fear of data breach, financial risk We concern about our data, information related to bank accounts etc., What if something goes wrong. Need for help, lack of confidence We senior citizens need assistance to use these applications. Discomfort with unfamiliar apps Other Apps I do not want to use, as I am not comfortable with unknown apps. Annoyance from unsolicited calls I don't like receiving unknown and unnecessary calls. That is troublesome. Lack of digital literacy We do not know how to use many apps. Behavioral intention Our next question was their willingness to use Apps. The responses to this reveal a mixed level of willingness among older adults to adopt mobile Apps. While some expressed clear interest, such as "I wish to use" (R1), "I may use sometime later" (R14) their readiness is conditional on receiving support and guidance, as indicated by statements like "If any training program is there, it will be useful" (R4) , " We need to know what apps are all available and their purpose. With that, if any training is provided, it will be easy" (R5) , and "Assistance if available makes it easy to handle these mobile apps" (R9) . These responses highlight that the lack of awareness and digital literacy are major barriers. Few respondents showed selective interest, prioritizing essential apps over non-essential ones, as reflected in "May be Banking related apps are useful for me" (R7, R11). Conversely, some respondents expressed resistance or a perceived lack of need, stating, "I don't want to use any other mobile apps" (R8) and "I don't want to try other apps" (R11). Altogether, the responses suggest that the intention to use apps among older adults depends largely on perceived usefulness, simplicity, and the availability of training and assistance. Discussion The rapid advancement of technology has led to a significant increase in smartphone usage among older adults ( Choi et al., 2024 ). However, it has been found that older adults face numerous challenges. Therefore, understanding the factors influencing their mobile app adoption is crucial because it can enhance the digital inclusion of older adults by providing access to various essential services, such as health, banking, travel, and food ordering, which are vital to their quality of life. Therefore, this study aimed to explore the factors influencing older adults' mobile app use using a qualitative approach. The study conducted a structured interview of 14 older adults. Based on their qualitative insights, the study identifies several themes and subthemes ( Figure 1 ) and proposes several propositions. Figure 1. Proposed model for mobile app adoption among older adults. This is a conceptual framework developed based on the qualitative insight provided by the older adults on mobile app adoption. This model is developed by integrating three existing theories. Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) Fred Davis developed the Technology Acceptance Model (TAM) in 1989 ( Davis, 1989 ). It focuses on the user acceptance of technology. The PU and PEOU are the two main constructs of the Technology Acceptance Model (TAM). Davis defines PU as "the degree to which a person believes that using a particular system would enhance their job performance" and PEOU as "the degree to which a person believes that using a particular system would be free from effort". Since its inception, this model has been widely used in understanding the adoption of various health apps. For example, a study conducted in Korea highlights that PU and PEOU significantly influence user satisfaction, which in turn drives their behavioral intention to use mobile health apps ( Park et al., 2025 ). Similarly, a mixed-method study conducted in Indonesia reveals that PU was the most significant factor influencing positive user responses ( Aqilah et al., 2024 ). Furthermore, multiple studies confirm the positive and significant role of PU and PEOU on users' acceptance and behavioral intention ( Lin et al., 2019 ; Park et al., 2025 ; Saare et al., 2019 ; Xie & Or, 2020 ). Specifically, a study conducted in Iraq revealed that PU and PEOU were significant predictors of adoption of mHealth apps among older adults ( Saare et al., 2019 ). Thus, based on the above literature and findings of our qualitative study, the propositions are proposed: Proposition 1: PU is a significant predictor of behavioral intention Proposition 2: PEOU is a significant predictor of behavioural intention Social Influence (SI) and Facilitating Condition (FC) The Unified Theory of Acceptance and Use of Technology (UTAUT) model ( Venkatesh et al., 2003 ), integrates constructs from eight prominent TAM frameworks to explain user intentions and usage behavior. It identifies four core constructs: Performance Expectancy (PE), Effort Expectancy (EE), SI, and FC. According to this model, PE refers to the degree to which using the technology will provide benefits to users in performing specific activities. Whereas EE is associated with technology, such as PEOU. While SI refers to the extent to which individuals perceive that important others believe they should use the technology, FC deals with the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system. In the context of health app adoption, SI refers to the impact that others (e.g., family, friends, healthcare providers) have on an individual's decision to adopt apps. Past literature has mixed evidence on the significance of SI on individuals. Several studies suggest that SI has a significant impact on the behavioral intention to use mHealth apps. For instance, SI was a substantial predictor of mHealth app adoption among high-income individuals ( Akiogbe et al., 2025 ). SI was also found to be an important influencing factor in mHealth adoption during the COVID-19 pandemic ( Meng & Guo, 2024 ). Additionally, social influencers, such as healthcare workers, have been found to have a significant impact on mHealth adoption among hospital patients ( Lee & Chong, 2021 ). On the other hand, some studies have not established a significant relationship between SI and BI. Research conducted during the COVID-19 pandemic in Indonesia revealed that SI had no significant effect on mHealth app adoption ( Candra et al., 2025 ). Similarly, a Tanzanian study also found that SI had no impact on the adoption of mHealth among health workers ( Mbelwa et al., 2019 ). These contradictory findings highlight the need for further research to elucidate the role of SI in shaping BI across diverse contexts and populations, including older adults. Accordingly, we propose the following proposition: Proposition 3: Social Influence positively influences Behavioral Intention Facilitating Condition (FC) As mentioned earlier, FC refers to the resources and support available to users that facilitate the use of mHealth apps. The empirical evidence regarding the relationship between FC and BI remains inconclusive. FC was found to significantly impact the user's BI in mHealth adoption. In high-income countries, FC were identified as a significant predictor in shaping user acceptance of mHealth apps ( Akiogbe et al., 2025 ). Likewise, in an African study, FC were a crucial element for the adoption of mHealth apps among youth ( Mtshali et al., 2024 ). In contrast, a few studies reported a weak or insignificant impact of FC on BI. For example, a survey conducted during the COVID-19 pandemic in Indonesia revealed that FC had a very weak effect on mHealth usage intention. Similarly, the Tanzanian study did not establish a significant impact of FC on BI. Therefore, additional research is warranted to explore why FC produces divergent effects in users' BI. Thus, the following proposition is put forward. Proposition 4: Facilitating Conditions positively influence Behavioral Intention Perceived Risk (PR) According to the Perceived Risk Theory (PRT) ( Cox, 1967 ), PR is a combination of uncertainty and the severity of possible consequences when choosing products or services. PRT identifies several risks, including financial, performance, social, psychological, and privacy-related. When PR exceeds an acceptable threshold, individuals engage in risk-reduction strategies. They may seek additional information or rely on trusted sources. This theory is widely applied in technology adoption research, particularly in the context of mHealth adoption, where privacy and security are crucial ( Velverthi et al., 2024 ; Wei et al., 2010 ; Zhao et al., 2025 ). PR has emerged as a significant barrier to the adoption of mHealth apps ( Halvadia et al., 2025 ; Wang et al., 2023 ). Thus, based on the qualitative feedback from the respondents of our study and the above discussion, we propose the following proposition. Proposition 5: Perceived risk negatively influences Behavioral Intention Moderators Digital Competence (DC) The concept of the DC has emerged alongside technological development. It refers to the set of knowledge, skills, and attitudes required to use digital technologies effectively, responsibly, and safely in various contexts, such as work, study, and everyday life ( Ncube et al., 2025 ). DC is one of the key competencies for lifelong learning ( Vuorikari et al., 2022 ), defined in 2006, involves the confident, critical, and responsible use of, and engagement with, digital technologies for learning, at work, and for participation in society. It includes information and data literacy, communication and collaboration, media literacy, digital content creation, safety, intellectual property-related questions, problem solving, and critical thinking ( Directorate-General for Education, Youth, 2019 ). Research findings suggest that the DC impacts technology adoption across various contexts. For example, it has been found that teachers' competence is a significant predictor of integrating digital resources in schools ( Codina & Estebanell, 2023 ). DC as a moderator within the TAM framework Prior research has shown that the DC has a significant impact on the PEOU and PU. Higher digital competence enhances users' perception of usability and usefulness, which in turn positively influences their BI ( Faradiba et al., 2025 ; Nikou et al., 2020 ; Rodafinos et al., 2024 ). For instance, digital capability was found to moderate the effects of perceived usefulness and ease of use on business performance ( Faradiba et al., 2025 ). In the context of AI adoption in education, it has been found that digital literacy significantly enhances the adoption of AI ( Tomczyk & Majkut, 2025 ). Similarly, digital skills were also found to have a significant impact on PEOU, PU, satisfaction, and actual use, confirming their dual role as both predictor and moderator ( Netinant et al., 2025 ). Therefore, it can be concluded that the DC could be a critical factor in enhancing the use of technology, such as mHealth, and its adoption within the TAM framework. By enhancing DC, trainers, educators, and policymakers can significantly improve the integration and use of mobile apps among older adults. Thus, based on the above discussion, we propose the following propositions. Proposition 6: DC positively moderates the relationship between PU and Behavioral Intention Proposition 7: DC positively moderates the relationship between PEOU and Behavioral Intention DC as a moderator within the UTAUT framework UTAUT is one of the is widely used frameworks to understand the technology acceptance and usage intention. In this framework, DC can play significant role as a moderator that influences various relationships among UTAUT constructs, such as performance expectancy, effort expectancy, facilitating conditions, and social influence on behavioral intention. For example, a study integrating the Digital Competence Framework (DigComp) with UTAUT2 found that DC variables, such as problem-solving skills and ethical considerations, significantly influenced students' intention to use GenAI tools ( Caner-Yıldırım, 2025 ). Another study exploring the link between AI usage and DC among college teachers found that SI and DC, both predicted the teachers' BI ( Yi et al., 2025 ). The findings of this study further suggest individuals with higher DC are more likely to act on social cues because they feel confident and capable. However, with regards to the FC, there are no empirical evidence for DC as moderator between FC and BI in the literature. This represents a clear research gap and opportunity for the future research directions. Therefore, the following propositions are suggested. Proposition 8: DC positively moderates the relationship between social influence and Behavioral Intention Proposition 9: DC positively moderates the relationship between facilitating condition and Behavioral Intention DC as moderator within the Perceived Risk Theory PR generally has a negative impact on mobile app adoption. For instance, privacy risk perceptions negatively influence continued usage intention ( Mishra et al., 2020 ). A study conducted in the context of mobile banking app usage intention revealed that PR, encompassing financial risk, social risk, time risk, privacy risk, security risk, and performance risk, was negatively related to trust and the intention to use mobile banking ( Van et al., 2020 ). While the role of PR and BI is well established in the literature, the moderating role of DC between PR and BI is less explored. There are certain indications that DC can play a moderating role. Previous studies have demonstrated that enhancing DC helps in modifying risk associated with digital activities by improving users' skills and confidence in using digital tools ( Lam et al., 2025 ; J. Xu & Aumeboonsuke, 2025 ; Z. Xu et al., 2024 ). Thus, based on the above literature, it can be concluded that DC can play a significant role in shaping behavioral outcomes among older adults in using mobile apps by enhancing their confidence and reducing PR. Thus, we propose the following proposition. Proposition 10: DC positively moderates the relationship between perceived risk and Behavioral Intention Theoretical implications By extending and refining existing technology adoption frameworks, this study makes several theoretical contributions to the literature on mobile adoption among older adults. The study identifies important age-specific factors, such as hesitation, fear, and anxiety, which are not adequately addressed in traditional technology models, including TAM and UTAUT. Incorporating these age-specific factors can improve the predictive power of the models for the older adult population. The study indicates that the role of SI, including support from family, friends, and caregivers, is a critical determinant of mobile app adoption. These findings suggest that future research models should investigate the role of social support from family members, friends, peers, and caretakers on BI of older adults. Third, identifying perceived risks, such as financial and privacy risks, when using mobile apps. Practical implications This study also has several practical implications for mobile app developers and policymakers. First, app developers and designers should prioritize user-friendly interfaces with simplified navigation, larger fonts, and clear instructions to accommodate age-related physical and cognitive limitations. Second, the study emphasizes the importance of training and support. Therefore, mobile app service providers should focus on conducting community workshops and family-assisted learning to enhance the digital competence of older adults. Third, mobile app developers and service providers should prioritize integrating personalized assistance features into their apps. For example, integrating voice guidance and help buttons can reduce dependency on others. Finally, policymakers should focus on privacy and security issues through a transparent and easy-to-understand process and communication to build trust in using mobile apps. Future research directions The study proposes future research directions for the adoption of mobile apps among older adults. We recommend that future research utilize the proposed framework and propositions to advance beyond qualitative insights. The future studies should incorporate rigorous quantitative validation of the proposed model. Additionally, experimental and quasi-experimental designs could be employed to examine causal relationships and the effectiveness of interventions, particularly the moderating role of DC. Furthermore, longitudinal research is recommended to track changes in attitudes, confidence, and usage patterns over time. The findings of such studies will provide insights into the sustainability of interventions (training and assistance) and the ongoing intention to adopt mobile apps. Ultimately, future research should also focus on integrating policy-level initiatives and structured training programs to assess their practical applicability in real-world settings. Limitations While this study has both theoretical and practical implications, it also has a few limitations. The study employed a structured interview method within the framework of qualitative research. The structured interview method might have restricted participants' ability to elaborate or introduce new ideas. This approach can impact the richness and complexity of data compared to semi-structured or unstructured interviews. The second limitation is the small size of 14 participants, drawn from a specific age group, i.e., 60 to 75 years. This age-specific study may reduce the generalizability of the findings. Third, since this is a qualitative study, the proposed conceptual framework requires further validation through a larger, more diverse sample using quantitative approaches. Conclusion Based on the qualitative insights provided by older adults during semi-structured interviews, the study identified five main factors that influence their adoption of mobile apps. They are PU, PEOU, SI, FC, and PR. The study has several practical and theoretical implications. While the findings of this study are helpful for mobile app developers, service providers, and policymakers, it also contributes to the literature by proposing a conceptual model that integrates three existing models/theories. The proposed conceptual model works as a foundation for future quantitative study. Declaration of generative AI In preparing this manuscript, GAI and AI-assisted technology (ChatGPT, Co-pilot and Scopus AI) were used solely to enhance readability and clarity of language. All AI-generated content was carefully reviewed and edited to ensure accuracy and integrity. The authors take full responsibility for the content and affirm that AI was not used for data analysis or generating research insights. Any potential limitations of AI-generated text were addressed through human verification. Data availability statement The qualitative data of this study is openly available on Mendeley: RMG-Qualitative-file at https://data.mendeley.com/datasets/z5729286m6/1 ( Gopal, 2026b ). This study contains the following data: • Updated Data - Jan 2025.xlsx Extended data Mendeley: RMG-Qualitative-file. The interview guide is available at https://data.mendeley.com/datasets/rjd8xp3jxb/1 ( Gopal, 2026a ). This study contains the following data: • Interview Guide.pdf Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgments We sincerely thank Manipal Academy of Higher Education for the constant support, encouragement, and resources provided throughout this work. References Akiogbe O, Kobayashi Y, Kageyama I, et al. : Determinants and implications of mobile health application acceptability in high-income countries: an assessment. Drug Discovery Today. Elsevier Ltd.; 2025; Vol. 30 (10). Publisher Full Text Amouzadeh E, Dianat I, Faradmal J, et al. : Optimizing mobile app design for older adults: systematic review of age-friendly design. Aging Clin. Exp. Res. 2025; 37 (1): 248. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 18 Apr 2026 ADD YOUR COMMENT Comment Author details Author details 1 IT, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India 2 Library, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India 3 Community Health Nursing, Manipal College of Nursing, Manipal Academy of Higher Education, Manipal, India 4 School of Computer Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 5 Department of Social and Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India Raghavendra G Roles: Conceptualization, Data Curation, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Jyothi Mallya Roles: Formal Analysis, Software, Writing – Original Draft Preparation, Writing – Review & Editing Shashidhara Y N Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Poornima Panduranga Kundapur Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Lena Ashok Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 18 Apr 2026, 15:552 https://doi.org/10.12688/f1000research.176730.1 Copyright © 2026 G R et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article G R, Mallya J, Y N S et al. Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :552 ( https://doi.org/10.12688/f1000research.176730.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 18 Apr 2026 Views 0 Cite How to cite this report: Hassan HMK. Reviewer Report For: Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :552 ( https://doi.org/10.5256/f1000research.194823.r477289 ) The direct URL for this report is: https://f1000research.com/articles/15-552/v1#referee-response-477289 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 May 2026 H.M. Kamrul Hassan , University of Chittagong, Chattogram, Bangladesh Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.194823.r477289 The authors state that "existing studies focus on quantitative measures of mobile app adoption among older adults, while qualitative exploration remains unexplored" – and use this as the central justification for the study. However, no specific quantitative ... Continue reading READ ALL The authors state that "existing studies focus on quantitative measures of mobile app adoption among older adults, while qualitative exploration remains unexplored" – and use this as the central justification for the study. However, no specific quantitative studies are cited to substantiate this claim. This is a critical gap because the entire rationale for a qualitative design rests on this assertion. The authors should address relevant studies on mobile app adoption among older adults and clearly contrast their limitations with those of qualitative studies. Similarly, if there are any existing qualitative studies that should be acknowledged and positioned against to demonstrate what is genuinely unexplored, they should be. Without this, the claim of a research gap lacks scholarly credibility. The introduction notes that older adults face barriers to mobile app adoption but does not explain why this problem has been inadequately addressed in the literature. For instance, whether older adults have been systematically excluded from technology adoption studies, or whether the heterogeneity of the older adult population makes it difficult to study, needs to be clarified. Without this explanation, readers cannot assess whether the authors genuinely understand the scholarly landscape they claim to contribute to. The paper's primary objective is to investigate "the age-specific factors that influence the adoption of mobile apps among older adults". The term "age-specific" is never defined, operationalised, or justified. What makes a factor age-specific as opposed to general? Does this refer to physical limitations (motor and visual), retirement-related changes in social structure, or something else entirely? If the authors intend to distinguish factors that are unique to older adults from those that apply across all age groups, this distinction must be stated explicitly and theoretically grounded. As written, the phrase is ambiguous and gives the impression that the authors are using "age-specific" as a rhetorical device rather than as a meaningful term. The authors should clearly define the term, explain what constitutes an age-specific versus a population-general factor, and connect this to the existing literature. The three research objectives — (a) to examine the purpose, usefulness, and different contexts of mobile app usage; (b) to understand the experience of older adults and identify primary sources of support; and (c) to identify the major barriers, challenges, and behavioural intention are formulated at a level of generality that renders them indistinguishable from a basic descriptive survey. Objective compounds the problem by bundling barriers, challenges, and behavioural intention into a single objective, three conceptually distinct constructs that each warrant their own focused inquiry. Furthermore, the distinction between "barriers" and "challenges" is never clarified; Tables 2 and 3 in the results confirm that these categories overlap substantially. The objectives appear to have been drafted based on general intuition rather than theoretical gaps in the literature. The authors should reformulate the objectives to be narrower, theory-informed, and clearly differentiated from one another. The current manuscript moves directly from the gap statement to the research questions and methodology without clearly stating what the reader can expect to gain from the study. What novel insight, theoretical extension, or practical guidance will this study provide that existing work does not? The authors should add a dedicated paragraph, ideally as the final paragraph of the introduction, that explicitly articulates the study's contributions across theoretical, methodological, and practical dimensions. The abstract states "structured interview", the data collection section cites "semi-structured interviews", and the conclusion again uses "semi-structured". This is not a minor terminology error. Structured and semi-structured interviews yield fundamentally different data, reflect different epistemological assumptions, and are evaluated differently in qualitative research. The authors must resolve this inconsistency and consistently describe the actual method used throughout the manuscript. The methodology section explicitly states that "no prior theoretical frameworks were applied before data collection". This is presented as a deliberate methodological choice to ensure inductive inquiry. However, the results are then directly and systematically mapped onto the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Perceived Risk Theory constructs, which serve as the organising structure for the entire discussion and the development of propositions. If TAM/UTAUT categories were used as coding categories, even informally, during the analysis phase, the approach is deductive or abductive, not inductive. The claim of inductive inquiry followed by framework-confirmatory findings gives the impression either that the frameworks were used to guide coding (which was not disclosed) or that the findings are so general as to be confirmable by any technology adoption theory. In either case, the authors undermine the credibility of their analytical process. The authors should either: (a) honestly describe the analysis as abductive and explain how theory was brought in during interpretation, or (b) demonstrate through rich, specific thematic descriptions how the data independently generated categories that were subsequently compared with existing theories. Furthermore, the study findings largely confirm what TAM and UTAUT already predict—perceived usefulness, ease of use, social influence, and facilitating conditions. A study that claims to generate novel qualitative insights but merely reaffirms well-established quantitative constructs does not constitute a meaningful contribution to the literature unless the qualitative depth substantially enriches or challenges those constructs. The authors should focus on what the qualitative data reveals beyond what TAM/UTAUT already predicts. The manuscript lacks a standalone literature review section, a notable structural omission for a study. The authors should add a dedicated literature review section that builds a coherent scholarly foundation for the current study's design and contribution. Tables 2 (Barriers to Mobile App Adoption) and 3 (Challenges of Mobile App Adoption) present what appear to be two distinct analytical categories, yet a close reading reveals that they share substantially overlapping constructs and, in several instances, near-identical verbatim quotations from participants. For example, "fear of data breach/financial risk" appears in Table 3 with the same supporting quote used to illustrate "Security Anxiety" and "Fear/Risk Perception" in Table 2. Similarly, "lack of digital literacy" and "need for help/lack of confidence" in Table 3 correspond directly to "Knowledge Deficit", "Lack of Confidence or Self-Efficacy", and "Support Dependency" in Table 2. This overlap raises a fundamental analytical concern: what is the conceptual distinction between a "barrier" and a "challenge" as used in this study? The authors should either consolidate the two tables into a single, analytically coherent framework with clearly defined and non-overlapping categories, or provide a theoretically grounded justification for treating barriers and challenges as distinct constructs Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: My area of work is consumers' intention to adopt smart technology in the service industry. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hassan HMK. Reviewer Report For: Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :552 ( https://doi.org/10.5256/f1000research.194823.r477289 ) The direct URL for this report is: https://f1000research.com/articles/15-552/v1#referee-response-477289 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 18 May 2026 Jyothi Mallya , Library, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India 18 May 2026 Author Response Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the ... Continue reading Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the revised version in due course. Jyothi Mallya Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the revised version in due course. Jyothi Mallya Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 18 May 2026 Jyothi Mallya , Library, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India 18 May 2026 Author Response Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the ... Continue reading Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the revised version in due course. Jyothi Mallya Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the revised version in due course. Jyothi Mallya Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 18 Apr 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 18 Apr 26 read H.M. Kamrul Hassan , University of Chittagong, Chattogram, Bangladesh Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Hassan H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 May 2026 | for Version 1 H.M. Kamrul Hassan , University of Chittagong, Chattogram, Bangladesh 0 Views copyright © 2026 Hassan H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors state that "existing studies focus on quantitative measures of mobile app adoption among older adults, while qualitative exploration remains unexplored" – and use this as the central justification for the study. However, no specific quantitative studies are cited to substantiate this claim. This is a critical gap because the entire rationale for a qualitative design rests on this assertion. The authors should address relevant studies on mobile app adoption among older adults and clearly contrast their limitations with those of qualitative studies. Similarly, if there are any existing qualitative studies that should be acknowledged and positioned against to demonstrate what is genuinely unexplored, they should be. Without this, the claim of a research gap lacks scholarly credibility. The introduction notes that older adults face barriers to mobile app adoption but does not explain why this problem has been inadequately addressed in the literature. For instance, whether older adults have been systematically excluded from technology adoption studies, or whether the heterogeneity of the older adult population makes it difficult to study, needs to be clarified. Without this explanation, readers cannot assess whether the authors genuinely understand the scholarly landscape they claim to contribute to. The paper's primary objective is to investigate "the age-specific factors that influence the adoption of mobile apps among older adults". The term "age-specific" is never defined, operationalised, or justified. What makes a factor age-specific as opposed to general? Does this refer to physical limitations (motor and visual), retirement-related changes in social structure, or something else entirely? If the authors intend to distinguish factors that are unique to older adults from those that apply across all age groups, this distinction must be stated explicitly and theoretically grounded. As written, the phrase is ambiguous and gives the impression that the authors are using "age-specific" as a rhetorical device rather than as a meaningful term. The authors should clearly define the term, explain what constitutes an age-specific versus a population-general factor, and connect this to the existing literature. The three research objectives — (a) to examine the purpose, usefulness, and different contexts of mobile app usage; (b) to understand the experience of older adults and identify primary sources of support; and (c) to identify the major barriers, challenges, and behavioural intention are formulated at a level of generality that renders them indistinguishable from a basic descriptive survey. Objective compounds the problem by bundling barriers, challenges, and behavioural intention into a single objective, three conceptually distinct constructs that each warrant their own focused inquiry. Furthermore, the distinction between "barriers" and "challenges" is never clarified; Tables 2 and 3 in the results confirm that these categories overlap substantially. The objectives appear to have been drafted based on general intuition rather than theoretical gaps in the literature. The authors should reformulate the objectives to be narrower, theory-informed, and clearly differentiated from one another. The current manuscript moves directly from the gap statement to the research questions and methodology without clearly stating what the reader can expect to gain from the study. What novel insight, theoretical extension, or practical guidance will this study provide that existing work does not? The authors should add a dedicated paragraph, ideally as the final paragraph of the introduction, that explicitly articulates the study's contributions across theoretical, methodological, and practical dimensions. The abstract states "structured interview", the data collection section cites "semi-structured interviews", and the conclusion again uses "semi-structured". This is not a minor terminology error. Structured and semi-structured interviews yield fundamentally different data, reflect different epistemological assumptions, and are evaluated differently in qualitative research. The authors must resolve this inconsistency and consistently describe the actual method used throughout the manuscript. The methodology section explicitly states that "no prior theoretical frameworks were applied before data collection". This is presented as a deliberate methodological choice to ensure inductive inquiry. However, the results are then directly and systematically mapped onto the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Perceived Risk Theory constructs, which serve as the organising structure for the entire discussion and the development of propositions. If TAM/UTAUT categories were used as coding categories, even informally, during the analysis phase, the approach is deductive or abductive, not inductive. The claim of inductive inquiry followed by framework-confirmatory findings gives the impression either that the frameworks were used to guide coding (which was not disclosed) or that the findings are so general as to be confirmable by any technology adoption theory. In either case, the authors undermine the credibility of their analytical process. The authors should either: (a) honestly describe the analysis as abductive and explain how theory was brought in during interpretation, or (b) demonstrate through rich, specific thematic descriptions how the data independently generated categories that were subsequently compared with existing theories. Furthermore, the study findings largely confirm what TAM and UTAUT already predict—perceived usefulness, ease of use, social influence, and facilitating conditions. A study that claims to generate novel qualitative insights but merely reaffirms well-established quantitative constructs does not constitute a meaningful contribution to the literature unless the qualitative depth substantially enriches or challenges those constructs. The authors should focus on what the qualitative data reveals beyond what TAM/UTAUT already predicts. The manuscript lacks a standalone literature review section, a notable structural omission for a study. The authors should add a dedicated literature review section that builds a coherent scholarly foundation for the current study's design and contribution. Tables 2 (Barriers to Mobile App Adoption) and 3 (Challenges of Mobile App Adoption) present what appear to be two distinct analytical categories, yet a close reading reveals that they share substantially overlapping constructs and, in several instances, near-identical verbatim quotations from participants. For example, "fear of data breach/financial risk" appears in Table 3 with the same supporting quote used to illustrate "Security Anxiety" and "Fear/Risk Perception" in Table 2. Similarly, "lack of digital literacy" and "need for help/lack of confidence" in Table 3 correspond directly to "Knowledge Deficit", "Lack of Confidence or Self-Efficacy", and "Support Dependency" in Table 2. This overlap raises a fundamental analytical concern: what is the conceptual distinction between a "barrier" and a "challenge" as used in this study? The authors should either consolidate the two tables into a single, analytically coherent framework with clearly defined and non-overlapping categories, or provide a theoretically grounded justification for treating barriers and challenges as distinct constructs Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise My area of work is consumers' intention to adopt smart technology in the service industry. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 18 May 2026 Jyothi Mallya, Library, Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education, Manipal, India Dear Professor Kamrul Hassan, Thank you so much for your time and feedback on our manuscript. We will work on the manuscript based on your feedback and submit the revised version in due course. Jyothi Mallya View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Hassan HMK. Peer Review Report For: Factors Influencing Mobile app adoption among older adults: An Explorative Qualitative Study [version 1; peer review: 1 not approved] . F1000Research 2026, 15 :552 ( https://doi.org/10.5256/f1000research.194823.r477289) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-552/v1#referee-response-477289 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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