What Will Entail Adoption of a Mobile Coaching Service? The Case of Smoking Cessation Services - Silvia Cacho-Elizondo, Niousha Shahidi, Vesselina Tossan

Niousha Shahidi, Vesselina Tossan & Silvia Cacho-Elizondo


There is a growing tendency to use smartphones or other mobile devices for healthcare purposes, which offers a huge opportunity to improve public health worldwide and at the same time generates cost efficiencies and higher performance. In that vein, mobile devices make it easier to provide enhanced coaching and follow-up services through text or video messages and also through two-way interaction via social networks (e.g., Facebook) or virtual reality devices (e.g., Oculus). This delivery mode supports individuals or patients trying to break addictions, such as smoking or drinking.

This chapter concerns a mobile coaching service providing support for people trying to stop smoking. The service takes the form of short text messages (SMS or MMS) sent to cell phones to help individuals in a range of situations or anti-smoking activities. In their research Oosterveen and colleagues (2017) in their systematic review suggest that eHealth interventions are more effective in asserting behavioral change in the short-term, however they identified very few studies comparing eHealth intervention to more traditional modes of delivery such as face-to-face coaching. The principal objective of this study is to identify drivers fostering the intention to adopt a mobile coaching service for the young smokers’ segment in France.

The authors propose and validate an explanatory model for the intention to adopt a mobile coaching service and applied it in the context of helping people in their smoking cessation efforts. This chapter uses the concepts of vicarious innovativeness, social influence, perceived monetary value, perceived enjoyment, and perceived irritation as key variables explaining the adoption patterns of this type of mobile coaching service.

The hypotheses were tested using the structural equations technique. The maximum likelihood fit function was applied. A two-stage approach was used as recommended by Anderson and Gerbing (1988). First, the measurement instruments for the constructs were assessed by examining the reliability and validity of scales. Then, the relationships were tested.

The model includes also the classification of smokers by number of cigarettes smoked per day and age.

The authors obtained a fairly robust model in which vicarious innovativeness, social influence and the perceived enjoyment exert a positive influence on the intention to adopt the mobile coaching service. For the authors, this framework could be used as a starting point to explore adoption patterns of other mobile counseling and mentoring services.

At the methodological level, this research contributes with an improved approach to test the acceptance and potential adoption of new services, not yet available, through the use of a scenario script where the mobile coaching service to quit smoking is described.

In future research, it would be interesting to explore further both the enjoyment aspect of the service and the socio-economic conditions of potential adopters. On this point, male-female differences also require more attention, as our model turned out to be more suited to women. One possible explanation for this could be the level of curiosity associated with this segment, and its usage of mobile applications.

An additional lesson to be drawn is the discovery of smokers’ preference for multimedia communications including images and videos. This trend would certainly help to compensate for the lack of real human contact that has often been considered as one of

the weaknesses of this type of remote coaching service.

The future of mobile health or m-health services is promising, it is necessary to continue encouraging further interdisciplinary research to improve them and spread their adoption and wide application in the health sector worldwide. Special focus should be given to developing countries where m-health services could have a significant impact on low-income vulnerable groups. Research suggests that individuals with different cultural backgrounds may respond differently to adoption of new technologies and services.

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