SOCIAL INFLUENCE & ELDERLY PERCEPTION
OF HOME HEALTH CARE DEVICES DESIGNED
TO IMPROVE QUALITY OF LIFE
The social dimension of sustainability
Camille Pfeiffer
University of Groningen
Faculty of Economics and Business
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SOCIAL INFLUENCE & ELDERLY PERCEPTION
OF HOME HEALTH CARE DEVICES DESIGNED
TO IMPROVE QUALITY OF LIFE
The social dimension of sustainability
Camille Pfeiffer
Faculty of Economics and Business
Department of Marketing
Master Thesis
20/06/2017
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MANAGEMENT SUMMARY
Health care and technology are two domains that are now working together to offer to earth’s populations the best solutions to improve health and quality of life. Home Health care Equipments are one the numerous medical innovations that aim to serve patients’ needs. Besides this trend of including technological competencies in the medicine world, populations are facing another phenomenon: the ageing of the population. Elderly are becoming a bigger section of the global population and hence, the medical needs of age-related pathologies are increasing as well.
To better understand the challenges of these two combined phenomena, i.e. development of technological medical devices and ageing of the population, the present research aims to provide insights on elderly adoption behaviors concerning an innovative product class, i.e. Home Health care Equipments designed to improve quality of life. The research develops and tests the validity and strength of five variables, i.e. Perceived Ease of Use, Perceived Usefulness of the medical device, and the Informational Influence exerted by Health care Providers, by Friends, and by Family Members on the elderly’s Behavioral Intention to Use Home Health care Equipments. The conducted analysis revealed a positive and direct relation between Perceived Usefulness, Perceived Ease of Use, the Informational Influence exerted by Family Members and the elderly’s Behavioral Intention to use such devices. The study also revealed that, in terms of distance and means of transport, the Ease of Access to the doctor is a necessary aspect in elderly’s life to take into account when measuring the Home Health care Equipments acceptance.
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PREFACE
"Collaboration between the two domains, medical and technological, is a major challenge",
Denis Abraham, 2017 (cited in Vernier, 2017) Innovation Director at Institut Mines-Télécom, France
After an exciting work experience in an innovative company of the medical sector, I am passionate about the innovations that have the capacity to revolutionize traditional health care systems. Invest time and effort in the improvement of quality of life of earth’s populations is a valuable task and represents one of my biggest challenges.
Hence, this paper tries to bring additional information about this passionate and fast moving topic, i.e. connecting medicine and technology through innovation to continue better improving health of earth’s populations. Conscious that this paper is certainly a drop in the ocean, I am proud of it and would like to thank the persons who actively help me achieve this paper.
First, I would like to express my sincere thanks to my thesis supervisor, Dr. Wander Jager for his valuable advice and kindness. He guided me through this study. For each difficulty encountered, my supervisor knew how to encourage me in finding the answers to my questions and supporting me until the finalization of this paper.
I would also like to express my thanks to Dr. Keyvan Dehmamy, Professor of Marketing Research Methods, who helped me become skilled and self-confident in using SPSS.
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LIST OF FIGURES AND TABLES
Figures
1. Evolution of the European adults aged 50 and over since 2001 ... 11
2. Conceptual Model ... 23
3. Structural Model with the Significant Variables and their Standardized Coefficients ... 40
Tables 1. Demographics Aspects of the Participants in the Study (N=181) ... 29
2. Means of the Main Variables and their Corresponding Items (N=181) ... 32
3. Reliability Analysis – Cronbach’s Alpha for the new sum variables ... 33
4. Variance Explained by the Model and Significance of the Overall Model ... 33
5. Significance of the Overall Model and Coefficients Table ... 36
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TABLE OF CONTENTS
MANAGEMENT SUMMARY ... 3
PREFACE... 4
LIST OF FIGURES AND TABLES ... 5
1. INTRODUCTION ... 8
2. THEORETICAL FRAMEWORK ... 14
2.1 Technology Acceptance Model ... 14
2.1.1 Perceived Usefulness ... 15
2.1.2 Perceived Ease of Use ... 16
2.1.3 Davis’ Model and Elderly Populations ... 16
2.2 Social Influence ... 18
2.2.1 Social Influence in Davis’ Model ... 18
2.2.2 Social Influence and Elderly Populations ... 18
2.2.3 Informational Influence and Elderly Populations ... 19
2.3 Control Variables ... 20
2.4 Conceptual Model ... 21
3. RESEARCH DESIGN ... 24
3.1 Sampling ... 24
3.1.1 Nature and Size ... 24
3.1.2 Data Collection ... 24
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3.2.1 Measurement and Scales ... 25
3.2.2 Self-Administered Questionnaire... 27
3.3 Data Analysis: Procedure... 27
4. RESULTS ... 28
4.1 Profile of the Participants ... 28
4.2 Results from the Questionnaire ... 30
4.3 Results from the Regression Analysis ... 32
4.3.1 Independent Variables ... 33
4.3.1.1 Technology Acceptance by the Elderly ... 33
4.3.1.2 Informational Influence exerted on the Elderly ... 34
4.3.2 Control Variables ... 35
4.4 Feedback from Participants ... 36
5. DISCUSSION ... 37
5.1 Conclusions and Scientific Implications ... 37
5.1.1 Utility of Home Health care Equipments ... 37
5.1.2 Family Members as the Most Credible Information Source ... 38
5.1.3 Ease of Access an Intention to Use: an inverse relation ... 39
5.2 Managerial Implications ... 40
5.3 Study Limitations and Suggestions ... 42
APPENDIX ... 44
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1. INTRODUCTION
For 25 years, the concept of sustainability has become widespread within societies. This concept defined by Brundtland in 1987 claims that sustainability is composed of three main principles: protect the environment, share earth resources more equally and offer improved quality of life to earth’s populations (Brundtland, 1987; Guidotti, 2015). Providing health to all and improving quality of health care plays a big role in the development of sustainable societies. According to the World Health Organization (2017), health has an impact on the overall quality of life of populations and ensuring a good level of health is necessary to sustainable societies. Thus, health and sustainability go together because it is impossible to create a sustainable society without providing to communities the possibility to feel socially, physically and mentally healthy: “health
is integral to the cluster of values that constitute sustainability” (Guidotti, 2015, What is Health
section, para. 1). Hence, health is one of the pillars of sustainability.
Since several decades, technology has been improved to gain more potential to support health of certain segments of the population, and hence reinforce the concept of sustainability. Nowadays technology is not only used by health care providers or health care institutions to alleviate patients’ health issues, but also by the patients themselves in their daily environment. This is a growing trend and in-home technologies are moving “from the research and development stage
to mass production” (Mostaghel, 2016, p. 4896). Various technological devices can now be used
by patients at home, and these assistive devices are products that improve quality of life of people with disabilities, illnesses or simply elderly patients who need continuous care. Numerous in-home products, i.e. in-home health care equipments exist to facilitate and improve people’s daily life; these products enable persons to perform a medical behavior in their environment, e.g. home and without help from health professionals. Examples of these products are GPS emergency response systems that allow persons to contact health professionals at any time, medication management system reminders, fall detection sensors, or even dinnerware to enable easier eating. Home health care equipments are characterized by their ease of use and affordability (Mostaghel, 2016).
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possibility to better monitor their health status. Over time, new devices have been introduced to help patients alleviate their health issues. One of the main examples is the blood glucose meters that help diabetes patients to manage their glucose levels by themselves (Rho, 215). However, this device is exclusively designed for persons with health issues already and only a very few appliances give persons the capacity to monitor their health whether or not they have pathologies. Beta-BioLEDTM is one of these new devices. It is an innovative Home Health care Equipment not on the market yet, is the first connected, mobile and personal blood analyzer (BetaBioled, 2017) the size of a mobile phone (Loumé, 2014). The device enables populations to gain instantaneous and up to date insights on their health status. Beta-BioLEDTM is a medical assistive device created by a French innovative start-up. Thanks to the company’s “disruptive proprietary
technology: SPECTROSCOPY 2.0®”, the product enables everyone to rapidly and easily analyze
blood at home (see Appendix 1: Beta-BioLEDTM: how does it work?). Beta-BioLEDTM provides same blood data than traditional blood tests. This includes information about the heart, liver, pancreas, kidney status, and lipid profile of the user (Loumé, 2014). Moreover, the application compares blood results to the reference blood data originally saved by the system, and this allows users to monitor their pathologies and better understand their health status with the help of a personal health historic available on the application. Users of Beta-BioLEDTM also have the option to share their results to their generalist and/or specialist (BetaBioled, 2017). Hence, Beta-BioLEDTM gives everyone the chance to access to his/her actual health status thanks to a compact, easy to use, and affordable device (BetaBioled, 2017). Independently monitoring of pathologies becomes easier for persons using Beta-BioLEDTM.
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established age range by the company to precisely define elderly – the target group analyzed in this paper, but it seems wiser to focus on the literature to define an appropriate age range for the elderly consumers of Home Health care Equipments, e.g. Beta-BioLEDTM. According to the existing literature, several definitions can be applied to the term elderly. Most of the researchers define elderly as adults aged 65 years old and over, who are more cautious than younger people, and resistant to change their habits (Gilly & Zeithaml, 1985). However, literature found that despite widely used definitions, there is no accepted theory on the best age that describes the time a person becomes old. Literature found three kinds of aging that help to define sections of the population: social, chronological and psychological kinds of aging (Sudbury & Simcock, 2009). Chronological age should not be the only kinds of aging to focus on, and hence the commonly used age 65 does not properly define the elderly section of the population. Moreover, it is necessary to mention that the choice of an age range to analyze an old section of the population depends on the aim of the research: to understand how the elderly segment process information and learns new products information, the segment of consumers aged over 45 needs to be chosen (Phillips & Sternthal, 1977). Finally, most of the preventive campaigns related to age-related pathologies target persons aged 50 years old and over. For these three reasons, the definition given to elderly in this paper and related to the potential target market of the product category Home Health care Equipments is adults aged 50 years old and over.
Adults aged 50 years old and over are then a highly interesting target group for Home Health care Equipments, such as Beta-BioLEDTM (Mostaghel, 2016) in terms of consumer profile. Indeed, elderly consumers want to have more independency and keep “themselves involved, active, and
fit” (Schiffman & Sherman, 1991, p. 189). An increasing amount of elderly persons have a daily
access to internet at home (Szmigin & Carrigan, 2001), search for “new, just-good-enough, easy
to use and affordable” products (Mostaghel, 2016, p. 4896), and are willing to embrace a healthy
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population (Mostaghel, 2016). According to the World Health Organization (2015), the ageing of the population generates a rise of the demand for medical needs of elderly, i.e. the implementation of home health care devices and/or home care services. This increase is due to an augmentation of losses in physical, mental capacity and an augmentation of age-related diseases. These diseases particularly affect elderly and include diabetes, cardiovascular diseases, respiratory diseases, or even cancers (WHO, 2015). As elderly health generally becomes progressively weaker, most of the elderly people need continuous care (Mostaghel, 2016).
When looking closer to the demographic statistics of the European population, these statistics state that the European population is becoming older since the 2000’s. The graph below shows the evolution of the European population since 2001. Elderly population has been divided in age groups and each bar illustrates the percentage of each group in the total European population. An overall increase of the elderly can be observed: in 2001, the European population aged 50+ represented 31.1% of the total population and in 2015, it represented 39.4% of the total population. Thus, the percentage of European population aged 50+ increased by 8.3 points. The demographic trend is expected to continue: the expected demographic forecasts for 2080 indicate a high increases of the elderly, i.e. adults aged 85+ (EU, 2017).
Figure 1: Evolution of the European adults aged 50 and over since 2001 0 1 2 3 4 5 6 7 8 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85 +
% of the total European population
A
ge 2080*
2015 2001
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This phenomenon becomes visible in all European countries (EU, 2017), and it seems necessary to mention that this trend is not only a phenomenon that threatens countries of the European Union: other parts of the world are threatened as well. According to the demographic predictions given by Sudbury and Simcock (2009, p. 22), a growing part of the worldwide population (living in developed as in less developed countries) “will be age 60 or older” by 2050.
As the population is getting older over time, societies should be able to offer strong solutions to continue to apply the concept of sustainability, i.e. improve quality of life and well-being of elderly. Adding years to life to elderly is not a weakness for societies as long as they are able to link this demographic phenomenon with improved quality of life. According to the literature, home health care devices, such as Beta-BioLEDTM, are the best solution to adapt health care systems to the growing demographic trend, and hence to the growing elderly needs (Porter & Lee, 2013).
In terms of medical aspects, Home Health care Equipments, e.g. Beta-BioLEDTM have the capacity to serve the growing elderly medical needs: in providing up to date information on health status, these devices can help them better monitor their pathologies and, hence help them be better informed on their own health. In terms of quality of life, Home Health care Equipments, e.g. Beta-BioLEDTM’s users will feel more independent and secure. Their health will be also potentially improved as users have the possibility to control potential evolution of pathologies as well as to detect diseases before symptoms can be felt. Finally, benefits of using Home Health care Equipments will indirectly help elderly to continue enjoying their life and experience a healthier life (WHO, 2015). Thus, this type of technology seems to have a high chance to be successful: the target market is growing, needs of elderly people are growing and one of the values of the sustainability concept is likely to be reinforced.
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to be asked: How do social influence and elderly perception of home health care equipments
designed to improve quality of life affect elderly acceptance of such health care equipments?
On one hand, this paper will give useful insights to the field of marketing, i.e. medical marketing, and aims to better predict elderly acceptance of home health care devices. Indeed, this paper will help marketers of the medical field to gain recent insights on how elderly perceive home health care products designed to improve their quality of life and how their perception influences their intention to use these products. Moreover, this study aims to highlight the type of social influences that most affect elderly behavioral intention in a medical context. Thus, this study aims to fill the gap in the literature by looking at the elderly perception and social influences exerted on this segment, and manners to better predict their intention to accept technology (Sudbury & Simcock, 2009). On the other hand, this paper also has a social contribution. This paper focuses on a medical solution, i.e. home health care products, to understand how such devices impact elderly lives and how this contributes to the well-being and health of the elderly, one of the sustainability society challenges.
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2. THEORETICAL FRAMEWORK
In this part the concepts used in this study will be defined and discussed. At the end of each part, a hypothesis will be introduced. The conceptual model for this study will be then presented and described.
2.1 Technology Acceptance Model
Presented in the introduction, Home Health care Equipments, i.e. Beta-BioLEDTM have the potential to help elderly monitor their health, continue enjoying their life and experience a healthier life (WHO, 2015). Hence, this product seems to be the ideal Home Health care Equipment for each adult aged 50 years old and over who desires to easily and personally take care of his/her health. However, although Beta-BioLEDTM seems to be a highly useful tool for adults aged 50 years old and more, comprehend under what conditions elderly will be likely to intent accepting the new technology is necessary. Lots of research has been conducted to understand how and why people accept or reject certain technology. The widely used technology acceptance model is the one from Davis, and has been introduced in 1989 (Davis, 1989).
Davis used the Theory as Reasoned Action (TRA) from Fishbein and Ajzen as a basis for his Technology Acceptance Model (TAM) (Davis, 1989). These two models aim to predict a certain behavior of the target group. While the TRA focuses on diverse characteristics of the target group that can influence the latter to do a specific action, the TAM uses two specific variables: the
Perceived Ease of Use and the Perceived Usefulness to predict the acceptance of a technology by
the target group. These two new variables aim to predict the future behavior of the target group regarding the technological innovation (Schepers & Wetzels, 2007; Mostaghel & Oghazi, 2016). At the beginning, the model was used to understand the determinants in the acceptance or rejection of computers by people (Davis, Bagozzi, & Warshaw, 1989). Since the creation of the model, the TAM has been widely analyzed in the literature and researchers applied the model to the whole technology world and not only to computers acceptance or rejection. Davis’ model has been widely validated among international literature (Davis, Bagozzi, & Warshaw, 1989).
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new technology among a certain population. According to multiple empirical studies conducted to measure the effectiveness of the TAM in adopting an innovation, it has been found that Davis’ model “explains a substantial proportion of the variance (typically about 40%) in usage
intentions and behavior” (Venkatesh & Davis, 2000, p. 186). When comparing the TAM with
similar models, e.g. the Theory of Reasoned Action or the Theory of Planned Behavior, it has also been stated that the more reliable model to predict a behavior is the TAM (Venkatesh & Davis, 2000).
The TAM, as introduced by Davis, is composed of five variables: Perceived Ease of Use,
Perceived Usefulness, Attitude toward Using, Behavioral Intention to Use and Actual Use. These
are the five determinants of the technology acceptance or rejection by a target group. Contrary to the TRA defined by Fishbein and Ajzen, Davis did not include the variable Subjective Norm: according to the developers of the TRA, Subjective Norm was the least understood determinant of the model at this time (Davis, Bagozzi, & Warshaw, 1989). In Davis’ model, Perceived Ease of
Use and Perceived Usefulness have an important role in the TAM and have the capacity to
predict the Behavioral Intention to Use (Davis, Bagozzi, & Warshaw, 1989). Analyzing these two variables helps marketers to understand how consumers perceive an innovation and its attributes. For this reason, this paper will focus on these variables to understand how elderly perceive home health care devices, i.e. Beta-BioLEDTM.
2.1.1 Perceived Usefulness
Perceived Usefulness refers to the term useful and this means that the use of a product will bring
advantages to the user: better effectiveness in the task or better achieving of a specified goal (Davis, 1989). This term is the basis of the TAM determinant definition, i.e. Perceived
Usefulness. According to Davis’ empirical support for his model, Perceived Usefulness refers to
“the degree to which a person believes that using a particular system would enhance his or her
job performance" (Davis, 1989, p. 320). This means that the use of a useful “particular system”
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in elderly quality of life (Chen & Chan, 2011).
The variable Perceived Usefulness is a strong determinant of the variable Behavioral Intention to
Use. Across the numerous empirical studies that have been conducted, Perceived Usefulness has
always been presented as the main determinant of the TAM “with standardized regression
coefficients typically around 0.6” (Venkatesh & Davis, 2000, p. 187).
2.1.2 Perceived Ease of Use
Perceived Ease of Use refers to the term ease of use, which means that an action is linked to a
low difficulty level or effort when this action is performed (Davis, 1989). According to Davis’ empirical support for his model, Perceived Ease of Use refers to “the degree to which a person
believes that using a particular system would be free of effort” (Davis, 1989, p. 320). Hence, use
of a “particular system” that is easy to use would decrease the effort during the task, making the “particular system” more likely to be adopted (Davis, 1989).
The variable Perceived Ease of Use is a key determinant of TAM. According to empirical studies, Perceived Ease of Use is not as strong as Perceived Usefulness in predicting behaviors. The reason for this is that most of studies focused on the effects of the variable Perceived
Usefulness rather than the effects of Perceived Ease of Use in consumers’ behaviors (Venkatesh
& Davis, 2000). However, it should be noted that Perceived Ease of Use is a very important variable in two contexts. Indeed, in the context of innovation the variable Perceived Ease of Use becomes as relevant as the variable Perceived Usefulness: “research on the adoption of
innovations […] suggests a prominent role for perceived ease of use” (Davis, 1989, p. 322). Also
as elderly populations have a “higher technology anxiety” than younger adults, Perceived Ease of
Use has a significant role in the context of technology acceptance by an elderly population (Chen
& Chan, 2011, p. 3).
2.1.3 Davis’ Model and Elderly Populations
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with different target groups, e.g. students, employees, organizational groups (Chen & Chan, 2011).
However, elderly populations have always been neglected in TAM studies. Indeed, although the TAM has been widely applied to different contexts, no agreement on how elderly perception affects their behavioral intention has been adopted and validated (Mostaghel & Oghazi, 2016).
Empirical TAM studies state that the variable Perceived Usefulness has a direct and strong effect on the variable Behavioral Intention to Use, and also state that the variable Perceived Ease of Use has a smaller and indirect effect on the variable Behavioral Intention to Use (Davis, 1989; Venkatesh & Davis, 2000). However, according to gerontology literature, “different age groups
may think differently and make different decisions when it comes to the adoption and use of technology” and this might influence the strength of the TAM key determinants, i.e. Perceived Usefulness and Perceived Ease of Use (Chen & Chan, 2011, p. 2). Indeed, as mentioned above,
elderly populations have a stronger “technology anxiety” (Chen & Chan, 2011, p. 3): in this context the variable Perceived Ease of Use becomes a highly relevant determinant in technology acceptance, and is as strong as the variable Perceived Usefulness. Thus, Perceived Ease of Use can have a direct effect on the variable Behavioral Intention to Use when the TAM model is tested with elderly populations.
Therefore, the first goal of this paper is to fill this gap in extending Davis’ model, i.e. the two fundamental drivers Perceived Usefulness and Perceived Ease of Use, to the elderly segment, and understand if these two variables are good and strong enough predictors of elderly behavioral intentions to perform a behavior, i.e. use Beta-BioLEDTM1. Hence, we hypothesize:
Hypothesis H1: Elderly are more likely to intent adopting the new technology, i.e.
Beta-BioLEDTM when they perceive it as easy to use.
Hypothesis H2: Elderly are more likely to intent adopting the new technology, i.e.
Beta-BioLEDTM when they perceive it as useful.
1
18 2.2 Social Influence
Relationships between social influence, i.e. subjective norm and consumers’ behaviors have been widely analyzed (Venkatesh & Davis, 1989). The variable Subjective Norm refers to the “person’s perception that most people who are important to him think he should or should not
perform the behavior in question” (Fishbein & Ajzen, 1975, p. 302 as cited in Venkatesh &
Davis, 2000, p. 187). This means that people with a high motivation to comply with valuable referents’ opinions will intend to perform a specific action.
2.2.1 Social Influence in Davis’ Model
The direct effect of Subjective Norm on the intention to perform a specific behavior has been widely assessed in technology acceptance studies. Regarding the effect of this variable in Davis’ model, no agreement has been found on this effect: “user acceptance research […] has yielded
mixed results” (Venkatesh & Davis, 2000, p. 187). Several important studies, and in particular the
empirical comparison of the TAM and the TRA of Davis, Bagozzi, & Warshaw (1989) found no significant effect of the variable on the behavioral intention. For this reason, the TAM does not include the notion of social influence, i.e. subjective norm (Venkatesh & Davis, 2000). Hence, in the recent literature, the effect of Subjective Norm on the behavioral intention still remains blurred.
However, it has been recognized that future investigations of “conditions and mechanisms
governing the impact of social influences” was needed (Venkatesh & Davis, 2000, p. 187).
Therefore, it seems necessary to investigate the notion of social influence and its impact on Davis’ model, and especially when this model is applied to the elderly segment.
2.2.2 Social Influence and Elderly Populations
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process (Phillips & Sternthal, 1977). While some research show that elderly rely more upon social influence that younger persons, especially when they “perceive themselves to lack the
competence to make a decision” or “isolated from contact with others” (Phillips & Sternthal,
1977, p. 450), other research state that “there is no substantial body of broadly accepted theory
about [elderly] behavior” (Wolfe, 1997, p. 294 as cited in Sudbury & Simcock, 2009, p. 23).
Thus, elderly are affected by a social influence different from the one impacting the younger segment of the population, but this assumption stays vague. Further research needs to be done to identify the type of social influence, i.e. reference group influence that affects elderly intention to perform a behavior.
2.2.3 Informational influence and Elderly Populations
Available literature distinguishes between two types of social influence: informational influence and normative influence (Park & Lessig, 1977). However, compared to younger persons, elderly are more likely to be influenced by an informational influence than by a normative influence (Schepers & Wetzels, 2007; Bozan, Davey, & Parker, 2015). Moreover, it seems that any technology acceptance study includes this type of social influence, i.e. informational influence as a predictor to the variable Behavioral Intention to Use. Hence, this paper will focus on the informational influence, i.e. the informational reference groups of the elderly that are likely to influence elderly acceptance of home health care equipment, i.e. Beta-BioLEDTM.
Informational reference group refers to the search for guidance from people who lack specific knowledge (Deutsch & Gerard, 1955), and people rely upon reference groups they perceive as being the most credible to ask for relevant information (Park & Lessig, 1977). “Tendency to
accept information from others as evidence about reality” is a typical aspect of the informational
influence (Bearden, Netemeyer, & Teel, 1989, p. 474). As already mentioned above, elderly regularly rely upon credible information sources to gain reliable information in their daily life and the most important reference groups for elderly are their family members, friends and/or health care providers (Chen & Chan, 2011; Gilly & Zeithaml, 1985; Mostaghel, 2016; Phillips & Sternthal, 1977). Indeed, as these reference groups refer to the “search for information from
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elderly would probably rely upon these reference groups to decide whether they should perform the intended behavior, i.e. use Beta-BioLEDTM. Thus, informational influence of elderly family members, friends and health care providers seems to play a significant role in elderly life. However, it seems that there is no accepted theory on the type of informational reference groups that are the most influential group for elderly in a medical context. Therefore, assessing the impact of informational reference groups on elderly intention to perform a behavior, i.e. use Beta-BioLEDTM aims to uncover a potentially existing direct effect of Informational Influence on the
Behavioral Intention to use, to assess the strength of this effect and to compare it to the effects of Perceived Ease of Use and Perceived Usefulness on Behavioral Intention to Use. Hence, we
hypothesize:
Hypothesis H3: Elderly are more likely to intent adopting the new technology, i.e. Beta-BioLEDTM when they are positively affected by an Informational Influence.
Hypothesis H3a: Elderly who are positively influenced by an informational reference group, i.e.
health care providers are more likely to intent adopting Beta-BioLEDTM.
Hypothesis H3b: Elderly who are positively influenced by an informational reference group, i.e.
friends are more likely to intent adopting Beta-BioLEDTM.
Hypothesis H3c: Elderly who are positively influenced by an informational reference group, i.e.
family members are more likely to intent adopting Beta-BioLEDTM.
2.3 Control Variables
Including control variables in this study seems necessary since it has been found that demographic variables, such as gender, educational level and living environment can potentially impact social influence analysis (Park & Lessig, 1977). Women are likely to be more influenced by social factors than men (Park & Lessig, 1977), and the living environment can also plays a role. Indeed, people living alone will potentially have less opportunity to be socially influenced (Chen & Chan, 2011; Phillips & Sternthal, 1977).
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social, chronological and psychological aging (Sudbury and Simcock, 2009), “more age-related
characteristics” need to be included in this study to properly identify factors affecting elderly
intention to adopt a home health care device, i.e. Beta-BioLEDTM (Chen & Chan, 2011, p. 8). Thus, the variables health conditions and self-rated general health will be included in the study as it can have an impact on the elderly susceptibility to informational influence (Chen & Chan, 2011; Bozan, Davey, & Parker, 2015; Schiffman & Sherman, 1991).
Finally, the variables frequency of doing a blood test and the ease of access to consult a medical professional also seem to have a significant role in people’s intention to adopt a product from the category Home Health care Equipments.
For these several reasons, gender, educational level, living environment, health conditions and self-rated general health, frequency of doing a blood test and ease of access to consult a medical professional will be included as control variables in this analysis.
2.4 Conceptual Model
After having analyzed and described the key concepts, the conceptual model of this paper can be now introduced.
Validated literature presents Perceived Usefulness and Perceived Ease of Use as the two key determinant of Davis’ model, with Perceived Usefulness the direct predictor of Behavioral
Intention to Use (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989; Venkatesh & Davis, 2000).
However, gerontology literature presents the attributes ease of use and low effort rate as highly important determinants for elderly populations in the context of technology acceptance studies (Chen & Chan, 2011; Mostaghel, 2016). These attributes refers to the Perceived Ease of Use. Therefore, these two variables, i.e. Perceived Usefulness and Perceived Ease of Use are included in the conceptual model as two variables that both have a direct effect on the variable Behavioral
Intention to Use.
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important for elderly populations: health care providers, friends and family members (Gilly & Zeithaml, 1985; Mostaghel, 2016; Phillips & Sternthal, 1977). Elderly use these reference groups as credible sources to gain more information, and accept this information as evidence about reality (Chen & Chan, 2011; Gilly & Zeithaml, 1985; Phillips & Sternthal, 1977). Therefore, these reference groups, i.e. health care providers, friends and family members are likely to exert an informational influence on elderly behavioral intention. The variable Social Influence exerted
on Elderly, i.e. Informational Influence is then included in the conceptual model and is likely to
have a direct effect on the dependent variable Behavioral Intention to Use.
Thus, the conceptual model of this paper is composed of three main independent variables, i.e. elderly Perceived Ease of Use (IV1), elderly Perceived Usefulness (IV2) and the Social Influences
exerted on elderly, i.e. the Informational Influence (IV3).
The effect of the three independent variables on the Behavioral Intention to Use (DV) Beta-BioLEDTM will be first quantified in order to identify the existence of direct effect(s) on the dependent variable. As explained above, Perceived Usefulness, Perceived Ease of Use and
Informational Influence are likely to have a direct effect on the dependent variable, i.e. Behavioral Intention to Use.
Then, the three independent variables will be compared to determine the strongest relationship between an independent variable and the dependent variable. Indeed, it is likely that one of the three independent variables is a stronger predictor than the others.
The control variables, i.e. gender, living environment, health conditions and self-rated general health, educational level, ease of access to consult a medical professional, and frequency of doing a blood test are included in the conceptual model as well as they can influence relationships between the independent variables and the dependent variable.
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Figure 2: Conceptual Model
H1: Elderly are more likely to intent adopting the new technology, i.e. Beta-BioLEDTM when they
perceive it as easy to use.
H2: Elderly are more likely to intent adopting the new technology, i.e. Beta-BioLEDTM when they
perceive it as useful.
H3: Elderly are more likely to intent adopting the new technology, i.e. Beta-BioLEDTM when they are positively affected by an Informational Influence.
H3a: Elderly who are positively influenced by an informational reference group, i.e. health care
providers are more likely to intent adopting Beta-BioLEDTM.
H3b: Elderly who are positively influenced by an informational reference group, i.e. friends are
more likely to intent adopting Beta-BioLEDTM.
H3c: Elderly who are positively influenced by an informational reference group, i.e. family
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3. RESEARCH DESIGN
In this part the nature of the sampling and methods used to conduct the questionnaire will be described, followed by the procedure to analyze the collected data.
3.1 Sampling
3.1.1 Nature and Size
The study was conducted in France exclusively, with persons aged 50 years old and over. Precise inclusion criteria for this survey were the following: 50 years old and over, independent persons, able to easily communicate and to understand diffused information in their living environment.
The study took place in May 2017 and a total of 181 completed questionnaires were collected. Questionnaires were sent to elderly via e-mail or via social media. As it is hardly possible to precisely estimate the quantitative benefits of the social media on the response rate of the study, the ratio of the number of completed questionnaires (181) and the number of opened but not completed questionnaires (84) has been calculated to obtain the response rate: on a total of 265 opened questionnaires, 181 have been completed. This method produced a response rate of 68.3%.
3.1.2 Data collection
A self-administered online questionnaire has been conducted to test hypotheses introduced in the previous section. Online questionnaires have been completed by participants, and without the intervention of the researcher. Advantages of self-administered online questionnaires are the ease of distribution, and time efficiency. However, a disadvantage of this research method is the low response rate. Thus, to overcome this difficulty, researcher-administered questionnaires have been conducted as well to reach the targeted number of participants.
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Respondents that are easily reachable via private e-mails and social networks were firstly targeted. The social network that has been used is Facebook to contact persons with different profiles and backgrounds. Also, personal contacts, i.e. nurses working in the medical sector have been used to diffuse the questionnaire to respondents via e-mail. This enabled to improve the diversity of the potential respondents and collect as many different points of view in the study as possible. Online questionnaires were designed using the Qualtrics’ software. The questionnaire has been administered online and each respondent used the provided link to respond to the measured items (which are described in the following part).
A questionnaire in paper form has also been administered to other participants in order to include in the study persons who don’t have access to internet or who are not easily reachable via e-mail and social networks. Personal contacts, e.g. leaders of elderly associations have been used to distribute the questionnaire in paper form to elderly. For people who were not fully able to respond to the survey, e.g. adults aged 80 years old or more, an assistive help has been provided to read the questionnaire and the measured items to those people. Those associations had diverse themes: chorale groups, computer classes, walking clubs, and sewing classes. Again, this method enabled to include respondents with diverse interests in the study.
Confidentiality of the responses has been preserved in giving access to the responses, i.e. collected data to the researcher only.
The sampling technique is sampling without replacement which means that any element has been included more than once in the study. Finally, to measure participants’ responses and explain the obtained results, numerical values assigned to the chosen scales have been compared and analyzed.
3.2 Questionnaire items, reliability and validity
3.2.1 Measurement and scales
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The scale used to measure the effects of Perceived Ease of Use (IV1) and Perceived Usefulness
(IV2) on the Behavioral Intention to Use (DV) was adapted from the initial scales of Davis’
model. The initial scale introduced by Davis in 1989 to measure the determinants of the TAM was composed of 14 items. However, according to Venkatesh and Davis (2000), Perceived Ease
of Use, Perceived Usefulness and Behavioral Intention to Use should be measured on a four-item
scale. Indeed, after having tested and defined the 14 items many times, Davis realized that using a four-item scale per variable enables researchers to collect more reliable data (Davis, Bagozzi, & Warshaw, 1989). The use of this scale enables to detect whether Perceived Ease of Use (IV1) has
a direct and positive impact on Behavioral Intention to Use (DV). It also enables to detect whether Perceived Usefulness (IV2) has a direct and positive impact on Behavioral Intention to
Use (DV).
The second scale used in this study is the Bearden’s scale (1989) developed to measure informational and normative influence (Bearden, Netemeyer, & Teel, 1989). This scale was used to quantify the effect of the third independent variable Social Influence exerted on elderly, i.e.
Informational Influence (IV3) on Behavioral Intention to Use (DV). Bearden’s scale is based on
the scale of Park and Lessig (1977) which included 14 items. However, this scale had limitations which could cause errors during the data collection: the items were neutral and not product or situation specific, and no test of consistency or reliability had been conducted. Thus, few years later, Bearden introduced a new scale including 12 items (Bearden, Netemeyer, & Teel, 1989). After numerous reliability tests and many uses in the literature, Bearden’s scale appears today as the most reliable scale to measure social influence, i.e. informational and normative influences. As this paper focus on the informational influence exerted on a target group, only the second part of the scale has been used. This part is composed of three items. Moreover, in order to assess what elderly informational reference groups, i.e. family members, friends, health care providers affect the most elderly intention to perform a behavior, i.e. Home Health care Equipment use, the three selected items have been adapted and partially reworded.
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method seems to be the best manner to get reliable results (Davis, Bagozzi, & Warshaw, 1989; Bearden, Netemeyer, & Teel, 1989).
3.2.2 Self-administered questionnaire
First, a precise introduction on the rise of the product category Home Health care Equipments has been presented to participants. To help them better understand the product category and its benefits, a specific example has be introduced: the product Beta-BioLEDTM.
Three sets of questions have been displayed to participants: (1) questions concerning the independent variables Perceived Ease of Use (IV1) and Perceived Usefulness (IV2), (2) questions
concerning the third independent variable Social Influence exerted on elderly, i.e. Informational
Influence (IV3), and (3) demographic questions related to the control variables introduced in the
previous part of this paper (see Appendix 2: Measured Items).
3.3 Data Analysis: Procedure
The conceptual model is composed of three independent variables (IV1: Perceived Ease of Use,
IV2: Perceived Usefulness, and IV3: Social Influence exerted on elderly, i.e. Informational
Influence), and one dependent variable (DV: Behavioral Intention to Use) that are measured on
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4. RESULTS
In this part, answers and feedback from respondents received after filling out questionnaires will be described. The statistical parameters of the analysis conducted to analyze answers of respondents will be presented as well.
4.1 Profile of the participants
Participants of the study were persons aged 50 years old to 90 years old and the average age was 61.76 years old. Almost two third of the respondents were women (61%), and most of them had a professional degree (38.7%), followed by respondents graduated from high school (22.1%). About 86% of the respondents were living with a household member at home, while only about 14% were living alone at home.
Concerning the health of respondents, 48.1% of respondents were healthy and 51.9% of respondents had pathologies. Cholesterol was the most frequent recorded pathology with 45.75%, followed by diabetes (18.08%) and cardiovascular diseases (12.77%). Other diseases were also recorded: high blood pressure (11.7%), respiratory diseases, cancers, and osteoarthritis (8.5%), thyroid problems (6.38%) and epilepsy (3.19%). However, more than two third of the respondents rated their general health status as “Good” (68.5%), followed by 21% of the respondents who rated their general health status as “Average”.
Concerning their health monitoring, i.e. doing blood tests, almost half of the respondents reported visiting their doctor to do blood tests “Yearly” (47.5%), and one fourth of the respondents visit their doctor to do blood tests “Less than once a year” (26%). 20.3% of the participants do a blood test more than once a year, followed by participants who do a blood test monthly (2.8%). Participants who do blood tests more frequently, i.e. “More than once a month” (1.7%), “Weekly” (1.1%), and “More than weekly” (0.6%), represent a little part of the respondents.
29 CATEGORIES N % MEAN (SD) Age Na 181 100 61.76 (8.224) Gender Female Male 111 70 61.3 38.7 Educational Level No diploma Professional Degree High School Bac + 2 Higher studies 6 70 40 37 28 3.3 38.7 22.1 20.4 15.5 Living Environment2
With a household member at home Alone at home 156 25 86.2 13.8 Pathology Healthy Sickness 87 94 48.1 51.9 Self-rated Health Excellent Good Average Poor Don’t know 17 124 38 2 0 9.4 68.5 21.0 1.1 0
Frequency of doing a blood test
Less than once a year Yearly
More than once a year Monthly
More than once a month Weekly
More than weekly
47 86 37 5 3 2 1 26.0 47.5 20.3 2.8 1.7 1.1 0.6 Ease of Access Very difficult Difficult Neutral Easy Very Easy 24 65 37 35 20 13.3 35.9 20.5 19.3 11.0
Table 1: Demographics Aspects of the Participants in the Study (N=181)
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30 4.2 Results from the questionnaire
The questionnaire was composed of two main dimensions: the Technology Acceptance of Home Health care Equipments by the elderly and the Informational Influence exerted on elderly. To measure the Home Health care Equipment acceptance, three variables were used, i.e. Perceived
Ease of Use measured with four items, Perceived Usefulness measured with four items as well,
and the Behavioral Intention to Use, measured with two items. The items were measured on a 7-point Likert scale: this means that a mean close to 1 reflects a weak agreement of respondents with the measured item, and a mean close to 7 reflects a strong agreement of respondent with the measured item.
Regarding the measured items of Perceived Ease of Use, respondents generally agreed with the statement that it is “easy to get the home health care equipment do what I want to do (do blood tests)”. Indeed, this item has the highest mean, 6.00. In contrary, respondents only somewhat agreed with the statement that the device, i.e. Beta-BioLEDTM enables “clear and understandable interaction” since this item has the lowest mean, 5.70. Concerning the variable Perceived
Usefulness, participants agreed with the statement that Home Health care Equipments enable
them to “get health information faster”; this item has the highest mean, 5.72. In contrast to this, the item “save time for other activities” has the lowest mean, 5.23 which means that respondents only somewhat agreed with this statement. Among the two items used to measure the Behavioral
Intention to Use Home Health care Equipments, the item “I intend to use it” has a greater mean
than the item “I expect I would use it”, with 5.26 and 5.24, respectively. Thus, participants agreed slightly more with the item “I intend to use it” than the item “I expect I would use it”.
When comparing means of the three variables used to measure the acceptance of Home Health care Equipment designed to improve elderly’s quality of life, it can be observed that Perceived
Ease of Use has the highest mean, 5.8398, followed by Perceived Usefulness and Behavioral Intention to Use with 5.5497 and 5.2486, respectively. Thus, although respondents seem to
slightly more agree with the Perceived Ease of Use of the device, i.e. Beta-BioLEDTM, they generally agreed with the three main variables.
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and three reference groups, i.e. health care providers, friends, and family members have been integrated in the measured items to determine their influence on elderly behavior. Three items were used to measure the variable Informational Influence.
Regarding the items used to measure the Informational Influence of Health care Providers, the item with the highest mean 6.20 is “ask for information when little or no experience with the product class”: respondents totally agreed with this item and hence, approved it. The item “frequently gather information about the product class” has the lowest mean, 5.90 but respondents still generally agreed with this statement. Among the items used to measure the
Informational Influence of elderly Friends, the mean 4.05 of the item “ask for information when
little or no experience with the product class” means that participants neither agreed nor disagreed with this statement. The item “consult others to choose the best alternative from the product class” has the lowest mean, 3.89 and this value means that participants somewhat disagreed with this item. Concerning the Informational Influence of Family Members, the item “consult others to choose the best alternative from the product class” has the highest mean, 4.52, while the item “ask for information when little or no experience with the product class” has the lowest mean, 4.38. Both values show that participants neither agreed nor disagreed with these two statements.
Finally, after comparing means of the main variables, i.e. Informational Influence of health care
providers, Informational Influence of friends, and Informational Influence of family members, it
can be observed that the variable Informational Influence of health care providers has the highest mean, 6.0792, followed by Informational Influence of family members and Informational
Influence of friends, with 4.4420 and 3.9650, respectively. This means that participants in this
study generally agreed with the items of the variable Informational Influence of health care
providers, neither agreed nor disagreed with the measured items of the variable Informational Influence of family members, and somewhat disagreed with the items of the variable Informational Influence of friends.
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MEASURED ITEMS MEAN SD
Perceived Ease of Use 5.8398 0.84895
Learning to do blood tests and understand my health status Become skillful to do blood tests and monitor my health Easy to get the product do what I want to do (blood tests) Clear and understandable interaction
5.92 5.74 6.00 5.70 0.951 1.008 0.913 1.001 Perceived Usefulness 5.5497 1.25621
Improve my health monitoring Save time for other activities Get health information faster Make my life more convenient
5.58 5.23 5.72 5.67 1.342 1.513 1.297 1.291
Behavioral Intention to Use 5.2486 1.44878
I intend to use it I expect I would use it
5.26 5.24
1.458 1.488
Informational Influence of Health care Providers 6.0792 1.01129
Consult others to choose the best alternative from the product class
Ask for information when little or no experience with the product class
Frequently gather information about the product class
6.14 6.20 5.90 1.063 0.987 1.388
Informational Influence of Friends 3.9650 1.60382
Consult others to choose the best alternative from the product class
Ask for information when little or no experience with the product class
Frequently gather information about the product class
3.89 4.05 3.96 1.729 1.723 1.649
Informational Influence of Family Members 4.4420 1.54516
Consult others to choose the best alternative from the product class
Ask for information when little or no experience with the product class
Frequently gather information about the product class
4.52 4.38 4.42 1.659 1.691 1.640
Table 2: Means of the Main Variables and their Corresponding Items (N=181)
4.3 Results from the regression analysis
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4.3.1 Independent Variables
The measured items of each variable have been combined in five different sum variables, i.e.
Perceived Ease of Use, Perceived Usefulness, Informational Influence of Health care Providers, Informational Influence of elderly Friends, Informational Influence of Family Members, and Behavioral Intention to Use. Reliability analysis for each sum variable confirmed the ability of all
measured items to be combined in six corresponding sum variables mentioned above since Cronbach’s Alpha values were greater than 0.60 for all sum variables. Table 3 presents the reliability analysis for each new sum variables.
SUM VARIABLES NR. OF ITEMS CRONBACH’S
ALPHA Technology Acceptance by the Elderly
Perceived Usefulness Perceived Ease of Use Behavioral Intention to Use
4 4 2 0.899 0.940 0.966
Informational Influence exerted on the Elderly
Informational Influence of Health care Providers Informational Influence of Friends
Informational Influence of Family Members
3 3 3 0.843 0.938 0.921
Table 3: Reliability Analysis – Cronbach’s Alpha for the new sum variables
The regression analysis reported that 63% (adjusted R²) of the variance, i.e. intention to use Home Health care Equipments is explained by the model. Moreover, the model is significant since the p-value of the overall model (0.000) is lower than the alpha-value (0.05). This information is presented in the table 4.
Adjusted R² 0.630 (63%)
ANOVA Sign. 0.00
* Dependent variable: Behavioral Intention to Use
Table 4: Variance Explained by the Model and Significance of the Overall Model
4.3.1.1 Technology Acceptance by the Elderly
Regarding the variables used to measure the technology acceptance of Home Health care Equipments by elderly, it can be observed that the two explanatory variables Perceived Ease of
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than the alpha-value (0.05). Hence, it can be claimed that Perceived Ease of Use and Perceived
Usefulness predict and are related to the dependent variable Behavioral Intention to Use.
Furthermore, when the Technology Acceptance Model is applied to an elderly population,
Perceived Ease of Use and Perceived Usefulness are determinants of Behavioral Intention to Use,
with both a direct and positive effect on this dependent variable: the variable Behavioral
Intention to Use is positively and directly predicted by the Perceived Ease of Use and Perceived Usefulness by the elderly.
Standardized coefficients of these two variables are interesting to analyze as well. The coefficient of Perceived Usefulness, 0.512, is more than two times higher than the coefficient of the variable
Perceived Ease of Use, 0.213. Thus, the variable Perceived Usefulness has a stronger effect on
the dependent variable Behavioral Intention to Use than the variable Perceived Ease of Use.
4.3.1.2 Informational Influence exerted on the Elderly
The variable Informational Influence exerted on elderly regrouped three variables, i.e. Informational Influence of Health care Providers, Informational Influence of elderly Friends, and Informational Influence of Family Members.
The regression analysis reported two non-significant variables and one significant variable. While the p-values of the variables Informational Influence of Health care Providers, and Informational
Influence of elderly Friends, 0.933 and 0.656 respectively are non-significant (lower than the
alpha-value, 0.05), the variable Informational Influence of Family Members appears to be significant with a p-value 0.036, smaller than the alpha value, 0.05. This means that the variables
Informational Influence of Health care Providers and elderly Friends do not predict the
dependent variable Behavioral Intention to Use. However, the variable Informational Influence of
Family Members predicts the elderly’s intention to use Home Health care Equipments.
When looking at the standardized coefficients of the significant variable, i.e. Informational
Influence of Family Members, it can be observed that this coefficient 0.150 is lower than the two
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directly predict the dependent variable Behavioral Intention to Use, and hence is a determinant of the elderly acceptance of Home Health care Equipments.
The variable with the highest coefficient, and hence the strongest effect on the dependent variable, Behavioral Intention to Use a Home Health care Equipment, is the Perceived Usefulness (0.512), followed by the Perceived Ease of Use (0.213), and the Informational Influence exerted
on Elderly by their Family Members (0.150). Hence, these three variables have the capacity to
predict the dependent variable, with different influence strengths.
4.3.2 Control Variables
Control variables have been included in the regression analysis to measure their influence in the overall model. Among all seven control variables included in the analysis, i.e. gender, educational level, living environment, self-rated health, pathology, ease of access to the doctor, and frequency of doing a blood test, only the variable Ease of Access to the doctor has a p-value 0.000 smaller than the alpha-value 0.05. All the other control variables included in the analysis are non-significant and have a p-value higher than the alpha-value. Hence, the variable Ease of
Access influences the dependent variable Behavioral Intention to Use.
The standardized coefficient of this significant control variable, -0.190, has a negative sign. This means that the relation between this variable and the dependent variable is inverse: when the ease of access to the doctor decreases, the behavioral intention to use home health care equipments increases, or vice versa.
Hence, the variables that are significant and predict the elderly behavioral intention to use home health care equipments to monitor their health are the Perceived Usefulness, Perceived Ease of
Use, Informational Influence of elderly’ Family Members and the Ease of Access to the doctor.
Regarding the standardized coefficients of these predicting variables, it can be claimed that
Perceived Usefulness has the overall strongest effect (0.512), followed by Perceived Ease of Use
(0.213), Ease of Access to the doctor (- 0.190), and Informational Influence exerted on elderly by
their Family Members (0.150). Table 5 presents the variables that mostly matter in the overall
36 VARIABLES STANDARDIZED COEFFICIENTS SIGNIFICANCE (P-VALUE) Independent Variables Perceived Usefulness Perceived Ease of Use
Informational Influence of Health care Providers Informational Influence of Friends
Informational Influence of Family Members
0.512 0.213 - 0.004 - 0.031 0.150 0.000 0.000 0.933 0.656 0.036 Control Variables Gender Educational Level Living environment Self-rated Health Presence of Pathology Diabetes Cholesterol Cardiovascular Diseases Respiratory Diseases Other Types of Diseases
Ease of Access to the Doctor Frequency of Doing a Blood Test
- 0.032 - 0.048 0.029 - 0.041 - 0.032 - 0.061 0.039 - 0.027 - 0.026 - 0.056 - 0.190 0.054 0.500 0.335 0.531 0.423 0.727 0.318 0.544 0.575 0.594 0.450 0.000 0.336 * Dependent variable: Behavioral Intention to Use
Table 5: Significance of the Overall Model and Coefficients Table
4.4 Feedback from participants
During the diffusion of the questionnaire, feedbacks from participants have been collected. While some persons reported their rejection of Home Health care Equipments, others communicated their high interest for such devices.
For some participants, Home Health care Equipments won’t positively contribute to societies because this technology, according to them, is likely to substitute traditional medical laboratory and will contribute to the creation of medically deserted area.
In contrast to this, respondents working in hospital, i.e. surgeons reported their affection for home devices designed to help elderly better monitor their health. They reported that “this new
connected technology is the future and will help societies to overcome difficulties of people living in medically deserted area” (male participants, 69 years old). According to them, this new
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5. DISCUSSION
The purpose of this study was to examine whether the elderly intention to use Home Health care Equipments, e.g. Beta-BioLEDTM can be predicted by the elderly perception of these devices and by one type of social influence, i.e. informational influence exerted on elderly. In this part, the new knowledge and insights generated by the results will be described, followed by the managerial implications those new findings give rise to. Finally, the study limitations and suggestions for further research will conclude this paper.
5.1 Conclusions and Scientific Implications
5.1.1 Utility of Home Health care Equipments
The first category of hypotheses that has been tested addressed the relation between the elderly perception and their intention to use Home Health care Equipments, e.g. Beta-BioLEDTM. The analysis revealed that the original TAM determinants, i.e. Perceived Ease of Use and Perceived
Usefulness have been validated. Thus, these two factors are appropriate predictors to test the
acceptance of medical devices, e.g. Beta-BioLEDTM designed for elderly populations in a marketing setting.
More precisely, it has been found that the product utility perceived by elderly stronger influences and determines their intention to use Home Health care Equipments than the perceived ease of use of the product does. This means that the utility of the product class Home Health care Equipments is a highly important aspect for elderly. Perceived Ease of Use also predicts the elderly intention to use Home Health care Equipments but to a lesser degree. Hence, elderly are more likely to intend use Home Health care Equipments, e.g. Beta-BioLEDTM if they perceived it as useful more than easy to handle.
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These results could be aligned with findings related to the notion of New-Age Elderly: elderly who cognitively feel younger than their actual chronological age (Eastman, 2005; Parida, 2016; Sherman, 2001). New age elderly are described as different from the traditional elderly: they are fearless, take more risks and are more willing to accept new goods (Sherman, 2001). Recent studies also observed that new age elderly tend to become regular users of social media and internet (Parida, 2016). With these characteristics, the gap in technology use between younger and elderly consumers tends to decrease and hence, elderly become more skillful at using new technology (Eastman, 2005; Parida, 2016). Probably for this reason, participants in this study rated the variable Perceived Usefulness as the main determinant of their intention to use Home Health care Equipment, rather than the variable Perceived Ease of Use.
There are two major consequences of these results. First, these results highlight the adaptability of the TAM when applied to an elderly population and tested with the product class Home Health care Equipments, e.g. Beta-BioLEDTM. Secondly, the results enable to actualize common used elderly definitions: the “technology anxiety” aspect generally used to describe elderly seems to become a weaker and smaller characteristic of this population.
5.1.2 Family Members as the Most Credible Information Source
The second category of hypotheses that has been tested addressed the relation between the
Informational Influence of three reference groups exerted on elderly and the intention to use
Home Health care Equipments, e.g. Beta-BioLEDTM. The analysis revealed that the reference group Family Members plays a major role in elderly’s behavior when they intend to use Home Health care Equipments.
This finding represents a true contribution to TAM studies targeting elderly populations. Although elderly are not influenced by subjective norms, i.e. type of social influence traditionally tested in most TAM studies, elderly are affected by an informational influence, i.e. informational influence exerted by family members.
This indicates that elderly’s intention to use Home Health care Equipments, e.g. Beta-BioLEDTM
