Chapter 2 Revised Corrected Copy

docx

School

New Jinnah Degree College, Havelian, Abbottabad *

*We aren’t endorsed by this school

Course

MISC

Subject

Psychology

Date

Nov 24, 2024

Type

docx

Pages

Uploaded by SuperOpossum65

WhatsApp as an aid for recovery of persons with mental disorders. A case study of the group USP (Users & Survivors of Psychiatry). By Grace Nyambura Njenga 14-0622 Concept paper Daystar University Nairobi, Kenya In partial fulfillment of the requirements for the degree of MASTER OF ARTS In Communication 2021

CHAPTER ONE INTRODUCTION AND BACKGROUND TO THE STUDY Introduction The communication landscape is rapidly changing due to the introduction of the internet and social media, and this includes health communication. This has facilitated a better understanding of these technologies and their impact on health communication. (Manikonda & Choudhury, 2017). Patients or people with mental disorders have not been left behind in this ever changing technology that is the internet and more so social media. Studies show that people with mental disorders use the internet in search of help during their recovery journey. Research has reported that people living with mental disorders use social media at similar rates as the general population. This has varied with about, “ 70% among middle-age and older individuals to upwards of 97% among younger individuals,” (Aschbrenner et al., (2018); Birnbaum et al., (2017); Brunette et al., (2019); Naslund et al., 2016). Various studies have shown that individuals living with mental disorders turn to social media in search of information about their mental health, as well as, treatment options, (Bucci et al., 2019); (Naslund et al., 2016). Research has further shown that some people, “may prefer to seek help for mental health concerns online rather than through in-person encounters,” (Batterham and Calear, 2017). Those in a study say Schrank et al., and living with schizophrenia said, “greater anonymity, the ability to discover that other people have experienced similar health challenges and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information,” (2010). According to a survey done on those with lived experiences of mental disorders some of the reasons they used social media include,

“sharing personal experiences about living with mental disorders and opportunities to learn about strategies for coping with mental disorders from others,” (Naslund et al., 2017). “Social media has been used in the context of mental health behavioural intervention technologies (BITs) primarily in the form of internet support groups (ISGs), often used by patients as a source of information and support,” Mohr et al (2013). BITs Mohr stated are “the application of behavioral and psychological interventions strategies through the use of technology features to address behavioral, cognitive and affective targets that support physical, behavioral and mental health”. Mohr et al (2013) added “One emerging area is the embedding of web-based intervention tools into social networks that are engineered to encourage peer interactions that align with principles of supportive accountability.” “Harnessing peer social networks to encourage collaborative learning and adherence to web-based interventions may prove useful,” says Mohr et al (2013). As per a review done by Biagianti et al. (2018), “peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions and also may improve perceived social support.” “Understanding the structure of peer interactions and the effects that such interactions have on target outcomes may lead to better engineered ISGs and websites,” states Mohr et al (2013). Furthermore, “embedding online collaborative learning tools in networks may improve outcomes both by teaching skills and by encouraging communication that is therapeutically beneficial,” Mohr et al (2013). Though research has been conducted on social media and mental health, “there remains uncertainty regarding the risks and potential harms o f social media for mental health,” (Orben and Przybylski, 2019) “and how best to weigh these concerns against potential benefits,” (Naslund,

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

2020). According to Mohr et al (2013), the health technology sector has been, “developing and marketing Behavioral Intervention Technologies ( BITs) for a wide variety of mental health problems.” However, he stated the evaluation of their use or efficacy is minimal by the time they are introduced to the market, Mohr et al (2013). Therefore says Mohr, there is a, “need to evaluate BITs to protect stakeholders (patients, payers, providers & families)”, establish standards of proof of quality and benefit, guide choice of treatment by providing stakeholder with information and support development of guidelines and policies for the use, evaluation and development of BITs. Studies on the use of social media for mental health recovery have not been adequately investigated, said Mohr et al (2013). “ More research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts,” (Naslund, 2020). A n emerging area of research with promises of, “incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes, “are underway, as evidenced by several important effectiveness trials,” (Alvarez-Jimenez et al., 2019; Aschbrenner et al. 2018), “including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services,” (Gleeson et al., 2017). It was indeed against this background that the study intends to Therefore, as social media permeates the society its use and effects by and on people with mental disorders cannot be overlooked. ThusThus, this research will investigate Investigate how people with mental disorders utilize social media for or in their recovery journey. USP (Users &

Survivors of Psychiatry) WhatsApp group will be used as a case study.

Background to the Study Social media dates back to 1971 when Email was invented and though Email is not considered a social media by majority it did mark the beginning of much collaborative social media years later. The first social networking site was invented in 1995 called classmates.com. From 2005 the term Web 2.0 was coined, “closely associated with the Tim O’Reilly Media Web 2.0 conference of 2004,” say Sajithra & Patil (2013). Its applications and user generated content propelled its growth as it was known to facilitate interactive information sharing, interoperability, user centered design, and collaboration on the World Wide Web. A Web 2.0 site offers its users an opportunity to interact and collaborate with each other in a social media dialogue as creators (prosumer) of user-generated content in a virtual community, in contrast to websites where user (consumers) are limited to the passive viewing of content that was created for them Sajithra & Patil (2013). Social media may thus be said to have been accelerated by Web 2.0 as per Kwanya et al (2012), “Web 2.0 is a service for social media”. The revolution of social media has led to its being used by people of various classes and backgrounds and this includes people with mental disorders. This ease of accessibility to communication and information has transformed how people interact with each other. Forthcoming evidence suggests that people with mental disorders make use of this advantageous aspect of social media, Haker et al (2005); Schrank et al (2010) & Rice et al (2014). Research shows that social media is ubiquitous among youth with mental disorders, “with virtually all young people diagnosed with psychosis or depression using social media daily - an average of 2 or 3.5 hours per day, respectively,” Birnbaum et al (2015). Research from 2015, “found that nearly half of a sample of psychiatric patients were social media users, with greater

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

use among younger individuals,” (Trefflich et al., 2015). Brunette et al. (2019), states that in current times, use of social media among mental patients has increased as shown in research, “with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental disorders in treatment as compared with low-income groups from the general population,”. According to a study by Naslund (2020), people, “with mental disorders express interest in accessing mental health services through social media platforms. The study involved a One such study was a survey done on, “social media users with mental disorders that found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system,” (Naslund et al. 2017). “People with serious mental di s orders are increasingly turning to popular social media, including Facebook, Twitter or YouTube, to share their illness experiences or seek advice from others with similar health conditions,”(Naslund et al, 2016). “This emerging form of unsolicited communication among self-forming online communities of patients and individuals with diverse health concerns is referred to as peer-to-peer support,” (Naslund et al, 2016). Posts among people with mental disorders convey the reciprocity of connecting with online peers and demonstrates validation, hope and acceptance. “Increasingly, individuals with serious mental illnesses like schizophrenia, schizoaffective disorder, or bipolar disorder are turning to social media to talk about their illness experiences, seek advice and learn from and support each other,” Gowen et al (2012), Naslund et al (2014) & Miller et al (2015). “This unsolicited communication occurs naturally and involves self-forming online communities of individuals who share an understanding of living with mental disorders,” (Naslund et al, 2016).

This online peer-to-peer support maybe, as Ziebland & Wyke (2012) say, “one of the most transformational features of the internet,” that Naslund et al. (2016) posit, “may present new opportunities to promote recovery, self-esteem and mental and physical wellbeing among individuals with serious mental disorders.” A research study by Naslund et al (2015) showed, “the potential of online peer-to-peer support to reduce stigma, promote social connectedness and ultimately, improve the wellbeing of people with serious mental disorders”. In another study where, “the majority of participants (95%) engaged with the peer-to-peer networking feature of the program, and many reported increases in perceived social connectedness and empowerment in their recovery process,” (Alvarez-Jimenez et al. 2013). Alvarez-Jimenez et al. state that, “early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis,” (2018). It is important to note as Naslund (2020) says, “ individuals with serious mental disorders have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.” Therefore, social media offers an opportunity to examine its effects in peer-to-peer support of people with mental disorders as well as the role practitioners play in this. This study will look at the aspect of social media in aiding recovery of people with mental illness through peer-to-peer support as well as inclusion of mental health practitioners in the online fora. Consider my previous comment on what your background should be.

Statement of the Problem The researcher seeks to find out the role WhatsApp plays in the recovery journey of people with mental disorders through peer-to-peer support and professional input. Kalckreuth, Trefflich & Rummel-Kluge (2014) state that the internet has great value in the present day health sector and that internet users use it for health purposes, “to search for information on medical conditions or medication,” and the mental health sector is no exception. Kalckreuth, Trefflich & Rummel-Kluge continue to say, “the internet offers a wide range of options for patients suffering from psychiatric disorders, as well as mental health professionals” (2014). Brusilovskiy (2016) concurs with Kalckreuth, Trefflich & Rummel-Kluge (2014) stating, little research exists to date on the social media use among psychiatric patients. Social media is diverse and vastly changing hence the need to evaluate its current use among psychiatric patients to gain insights about the “preconditions for mental health related online options” Kalckreuth, Trefflich & Rummel-Kluge (2014). The benefits of social media on people’s mental health has not been adequately addressed especially for people with mental disorders. As Naslund (2020) states: Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. This study is essential as most research on this topic comes from higher income s countries such as the UK, USA and Australia (Naslund 2020). Naslund et al. (2019) so succinctly puts it:

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide. Therefore, the more people and especially persons with mental disorders know of the effects of social media on their mental health the better they can use it especially for their recovery journey. As Naslund points out: To capitalize on the widespread use of social media and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental disorders use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm (2020). The study hence seeks to find the extent to which persons with mental disorders utilize WhatsApp particularly for their recovery journey The research focuses on a Whatsapp group, USP (users and survivors of psychiatry) as the researcher is familiar with this group and is a member as well. Gaining better comprehension of the extent and success of social media use among people with mental disorders would assist them in their recovery journey as well as mental health practitioners to better handle them. 2 pages is a bit too long for a statement of the problem.

Purpose of the Study The researcher seeks to examine the extent to which people with mental disorders use WhatsApp in their recovery journey. The study seeks to evaluate the benefits of WhatsApp use on people with mental disorders. This will allow such individuals to make better and full use of WhatsApp to their advantage for their recovery. The study hopes to reshape the health communication landscape particularly the mental health aspect to a shift from only the traditional in-person patient-practitioner visits to a more peer-to-peer support system with the interjection of mental health practitioners in the fora from time to time. It seeks to challenge the mere passive treatment to a more proactive patient, seeking extended treatment beyond the four walls of a doctor’s office. However, it should be noted that this form of peer-to-peer recovery support is not a replacement of the traditional patient- practitioner treatment, but a supplement.

Research Objectives ● To determine the magnitude to which people with mental disorders use WhatsApp. ● To establish prevalence of WhatsApp use amongst people with mental disorders in their treatment journey. ● To examine the benefits of WhatsApp to people with mental disorders.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Research Questions ● How frequently do people with mental disorders use WhatsApp? ● To what degree do people with mental disorders use WhatsApp specifically for their recovery journey? ● What benefits does WhatsApp pose to people with mental disorders?

Justification This study highlights the use of WhatsApp in aiding the recovery journey of people with mental disorders. As Pavalanathan & Choudhury (2015) say, “social media is increasingly being adopted in health discourse.” “Recently, social media sites have begun to emerge as increasingly adopted platforms wherein health information seeking and sharing practices are apparent”. It is well noted that very few persons with mental disorders, “have access to adequate mental health services,” Patel et al (2018). Naslund, Bondre, Torous & Aschbrenner (2020) say, the ubiquitous and wide reach of social media may allow new opportunities to, “address shortfalls in existing mental health care, by enhancing the quality, availability and reach of services.” “Health information seeking and sharing practices online are known to be effective in helping people cope with respective problems,” (Fox, 2013). Studies have shown that online fora and support groups provide a conducive environment allowing people to get connected with others who share similar difficulties, misery, pain, condition, or distress and thus act as inexpensive and convenient vehicles for obtaining help and advice around health challenges, Eysenbach et al (2004). “Content shared on social media platforms has been identified to be valuable in gaining insights into people’s mental health experiences,” Manikonda & Choudhury (2017).

Significance of the study The study will provide more information on the benefits of WhatsApp use by people with mental disorders in their recovery journey. There is increased evidence indicating, “high rates of social media use among,” (Naslund et al., 2020) persons with mental disorders including those that show their interaction with “popular platforms across diverse settings and disorder,” (Naslund et al., 2020). Through this research persons with mental disorders may learn that utilizing social media can aid in their recovery process. This is the most important significance. The study is not only likely to impact persons with mental disorders, but also practitioners of mental healthcare. It can help mental health care practitioners develop systems that they can use in treating persons with mental disorders to better aid their patients in their recovery process through deliberate and genuine involvement. This collaborative relationship can form a good connection between persons with mental disorders and mental healthcare practitioners where one can receive treatment and the other assist in the treatment journey. The findings of this research could help social media developers develop social media that would encourage mental healthcare practitioners to integrate more social media into their practice. “Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones,” say Firth et al (2015), Glick et al (2016) & Torous et al (2014). The discoveries of the research will add to scholarly the data and information needed by communicators to better understand and create social media that enables persons with mental disorders to succeed in their recovery journey.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Assumptions of the study The researcher assumed that the participants involved were willing to participate in the pre-piloting and piloting of the questionnaires. The researcher also assumed that the respondents would reply within the time allotted for the response.

Scope of the study The research is to be conducted on the social media; WhatsApp; with the case study of the group known as USP (Users & Survivors of Psychiatry); which is used to aid in the recovery journey of persons with mental disorders. This social media was selected as the researcher is familiar with it; being a member; and also, because it supports and incorporates healthcare practitioners in its forum.

Limitations and delimitations of the study This research will look at only the USP WhatsApp group and this may be limiting in respect to the overall scope of social media which varies in type, in its use and application. This the researcher will delimit by using another social media group as a pre-pilot to ensure a harmonious representation of social media. The researcher may be biased as they suffer from a mental disorder and as such may advocate for the use of social media to their benefit; being an avid social media user. The researcher will delimit this by ensuring that the study is as objective as possible by basing it on previous studies, facts and respondents’ responses. In addition to this, accuracy may be another limiting factor which is highly dependent upon the honesty of the respondent ’ s responses to questionnaires and in-depth interviews. The researcher will delimit this by ensuring the confidentiality of the respondent ’ s responses.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Definition of Terms ● WhatsApp - WhatsApp is a communication application on smartphones that facilitates, “‘the exchange of instant messages, pictures, videos and voice calls via an Internet connection”. ● Social media - the definition as used in this study refers to use of computers, laptops, tablets or smartphone mediated technologies with “Web-based services that allow individuals, communities and organizations to collaborate, connect, interact and build community by enabling them to create, co-create, modify, share and engage with user- generated content that is easily accessible,” Sajithra & Patil(2013). ● Peer-to-peer support - in this study refers to “ self-forming online communities of patients and individuals with diverse,” mental health concerns that “share their illness experiences or seek advice from,” each other, according to Naslund et al (2016). ● Operational definition of performance expectancy: the degree to which a person believes that using instant messenger applications would enhance his or her need to access information and communications in real time, to be easily reached at any time and place. (Indrawati & Gusti Ayu Made Mas Marhaeni, G. A. M., 2015). This paper defines PE as the degree to which an individual with mental disorder(s) believes that using WhatsApp will help them in their recovery journey, by accessing information and communications in real time. “It takes into account the perception of the usefulness of WhatsApp and is measured in terms of the use of it in daily life. This is measured using four items adapted from Venkatesh et al. (2012),” (Praveena, K., 2016). Age and gender as moderating variables of people with mental disorders are also measured. ● Operational definition of effort expectancy: the degree of ease associated with using

WhatsApp by persons with mental disorders. It describes the ease of effort in using it and is measured in terms of the ease of learning to use it. “Effort Expectancy is measured using four items adapted from Venkatesh et al. (2012),” says Praveena, K. (2016). The moderating variables age, gender and experience of people with mental disorders are also measured. Experience is measured by the length of time that a person with mental disorder(s) has used a WhatsApp group along with their intention to continue using it in the future. ● Operational definition of social influence: the extent to which people with mental disorders perceive that people who are near and have influence over them (eg. family, friends, colleagues) believe that they should use WhatsApp. “It is measured using three items adapted from Venkatesh et al. (2012),” (Praveena, K., 2016). Age, gender and experience; as moderating variables; of people with mental disorders are measured. ● Operational definition of facilitating conditions: the perceptions that people with mental disorders have the resources and support available to them to enable them use WhatsApp (Praveena, K., 2016). Using Indrawati & Gusti Ayu Made Mas Marhaeni, G. A. M. (2015) definition we may define it as, “the degree to which,” people with mental disorders “believe that factors, such as availability of devices, knowledge, guidelines, and staff or people from social groups exist to support their use,” of WhatsApp. “It is measured in terms of the availability of resources (devices and technology) and help/support,” that people with mental disorders get to be able to use WhatsApp (Praveena, K., 2016). “Facilitating Conditions is measured using four items from Venaktesh et al. (2012),” (Praveena, K., 2016).} The gender, age and experience of people with mental disorders is measured as moderating variables to the effect of

facilitating conditions on behavioural intention. ● Operational definition of hedonic motivation: the fun or pleasure people with mental disorders get when they use WhatsApp. “This is measured using the three items adapted from Venaktesh et al. (2012),” (Praveena, K. 2016). The 3 moderating variables experience, gender and age of people with mental disorders are measured. ● Operational definition of price value: a persons with mental disorders cognitive trade-offs between the perceived benefits and cost of using WhatsApp (Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. 2020). Gender and age of the people with mental disorders is measured. ● Operational definition of habit: the extent to which people with mental illness tend to use WhatsApp automatically because of learning. It is measured in terms of trying to use WhatsApp in daily life, the use becoming natural and feeling addicted to use it. “Habit is measured using four items adapted from Venkatesh et al.(. (2012),” as per Praveena K. (2016). ● Operational definition of behavioural intention: indications of how hard people with mental disorders are willing to try, or how much of an effort they are planning to exert, in order to use WhatsApp in the future (Indrawati, I & Ariyanti, M., 2015 & Praveena, K., 2016). This is measured in terms of the intention to continue the use of WhatsApp in daily life and frequently. It is measured using three items from Venkatesh et al. (2012). ● Operational definition of usage behaviour: the type of use of and the actual time taken to use WhatsApp by persons with mental disorders. The type of use is measured by the extent of use of the different varied features offered by WhatsApp. “Nine main features are used to measure the type of use and the actual time is measured by one item which

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

asks for the time actively spent for using SNS,”* as per Praveena, K. (2016).

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

CHAPTER TWO LITERATURE REVIEW Introduction This chapter examines the theory or theoretical framework used to address this study. It also looks at the relevant academic studies relating to this research paper. This is discussed as the general literature review; a more holistic overview of literature relating to this research topic; and the empirical literature review; an explication of empirical literature as pertaining to the theoretical framework. Thereafter a conceptual framework is presented that shows the relationship among the concepts in the study. The literature herein discussed is interpreted and expounded on in regards to the conceptual framework, and the research gap(s) in this area of study are addressed. The close of the chapter is marked with a summary of the chapter’s discussion.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Theoretical framework Defining The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) History of UTAUT2 Researchers in various institutional environments have studied “different models based on the target groups’ social psychology,” in order to determine the varying factors that regulate people’s individual “intention to accept new technology” (Venkatesh et al. 2003; Ajzen, I., 1991; Ajzen, I. & Fishbein, M., 1975; Davis, F. D., 1989). UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) model has been credited as being the most significant and “widely accepted” model for understanding why and how “people accept technologies,” states Slade, E. L., Williams, M. & Dwivedi, Y. (2013). This theoretical model was proposed by Venkatesh et al. (2012) based on the model by Venkatesh et al. (2003); UTAUT (Unified Theory of acceptance and Use of Technology). Its goal was to produce a “rigorous framework,” specially created to explain use and acceptance of technology (Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M., 2017). These two models, says Praveena, K. (2016), “have been used by researchers in studying the acceptance of different technologies…and across different countries”. They have been found to be, “one of the best models with good explanatory power on the intention to use and the use behaviour behavior of a technology,” avers Praveena, K. (2016). UTAUT which is for an organizational context was extended by Venkatesh et al. (2012) to UTAUT2, a consumer context, and made to fit along with the factors that influence their

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

“intentions to use (and actual use of) new technologies,” affirms Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017). UTAUT2 is researchers Venkatesh et al. (2012) attempt to unify the technology acceptance models into a single theory (Slade, E. L., Williams, M. & Dwivedi, Y. 2013). Much like UTAUT, UTAUT2 explains the use and acceptance of technology “based on 8 previous theoretical models,” expounds Praveena, K. (2016). These 8 theoretical models include: theory of reasoned action (TRA), technology acceptance model (TAM), motivational model (MM), theory of planned behavior (TPB), Combined TAM and TPB (C- TAM-TPB), model of PC utilization (MPCU), innovation diffusion theory (IDT) and Social Cognitive Theory (SCT) (Taiwo, A. A. & Downe, A. G., 2013; Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D., 2003) “Venkatesh et al. (2012) proposed UTAUT2 by integrating additional constructs and relationships to UTAUT,” states Praveena, K. (2016). UTAUT2 has “7 key constructs,” 4 of which are from UTAUT and “were identified from prior research on adoption and use of technologies,” while the rest are suited for the “consumer context” state (Xiuyan, S. & Mikko, S., 2011) & (Praveena, K., 2016). Praveena, K. (2016) expounds, “the 4 key constructs of UTAUT adapted in UTAUT2 are; performance expectancy (PE), effort expectancy (EE), Social Influence (SI) and Facilitating Conditions (FC),” while “the 3 variables (constructs) added to UTAUT to make it UTAUT2 are; Hedonic Motivation (HM), Price value (PV) and Habit (HA)”. Praveena, K. (2016) affirms: In addition to this, UTAUT2 was modeled altering certain relationships in UTAUT and adding new relationships. As per UTAUT, PE, EE, and SI influence the behavioural intention to use a technology and FC and Behavioural Intention (BI) determine the technology use or usage behaviour behavior (UB). In UTAUT2, FC is hypothesized to

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

have a direct effect on behavioural intention, which makes the concept differ from UTAUT. HA also determines use behaviour behavior . Tenets This theory has 3 main tenets and 4 main aspects with regard to its predecessor UTAUT. The 3 tenets are: firstly, 7 constructs or explanatory independent variables including: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value & habit, and 2 dependent variables: behavioural intention & usage behaviour behavior (Venkatesh et al., 2012). Secondly, the relationships that cover the effect of the 7 constructs or explanatory variables. The behavioral intention to use a technology is theorized to be determined by the 7 explanatory variables (Venkatesh et al., 2012). Moreover, behavioral intention, facilitating conditions, and habit determine technology use or usage behaviour behavior (Venkatesh et al., 2012). The third tenet is the relationship amongst the constructs are influenced through various combinations of its moderators namely: age, gender and experience (Tamilmani, K., Rana, N. & Dwivedi, Y., 2017). The 4 main aspects in comparison to its predecessor UTAUT according to Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017) are: firstly, “redefining the 4 explanatory variables included in the original UTAUT: performance expectancy, effort expectancy, social influence, and facilitating conditions to adapt them to consumption contexts”. Secondly, “identifying 3 additional key constructs from prior research on both the general adoption and use of technologies and the consumer adoption and use of technologies that are: hedonic motivation, price value and habit”. Thirdly, altering some of the existing relationships in the original formulation of the UTAUT and introducing new relationships. The UTAUT2 reformulates the

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

relationships established in the UTAUT and introduces new relationships to cover the effects of the new explanatory variables included in the model such as, facilitating conditions also affect behavioural intention in UTAUT2, so do hedonic motivation, price value and habit. Also, habit affects usage behaviour behavior . Lastly, maintaining 3 of the 4 moderating factors which are: age, gender and experience, removing voluntariness to use because as Tamilmani, K., Rana, N. & Dwivedi, Y. (2017) states, “consumers have no organizational mandate and in many situations, consumer behavior is voluntary” (Venkatesh et al., 2012). The 7 constructs and 2 dependent variables, the relationship amongst them, as well as the moderators that influence them are explained below: A. Performance expectancy (PE): Theoretical definition - performance expectancy is defined as “the degree to which using a technology will provide benefits to customers in performing certain activities,” states Praveena, K. (2016). Venkatesh et al. (2012) reaffirmed the impact, “of PE on the usage of systems in a consumer context” says (Praveena, K., 2016). It is one of the 4 key constructs developed in the UTAUT model, and adopted in the UTAUT2 model after changing it to suit the consumer context. It was derived from, “conceptually similar variables from previous user acceptance research and formed into one called performance expectancy,” avers Vinnik, V. (2017). These variables include, extrinsic motivation from MM (Davis et al., 1992), relative advantage from IDT (Moore and Benbasat, 1991), perceived usefulness from TAM/TAM2 and C-TAM-TPB (Davis, 1989; Davis et al., 1989), job-fit from MPCU (Thompson et al., 1991), and outcome expectations from SCT (Compeau and Higgins 1995) (Vinnik, V., 2017). This construct was reported to have a notable effect in both UTAUT and UTAUT2 on

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

“user decision to adopt the researched technology” proving, “to be a strong predictor of behavioral intention (Venkatesh, 2003; Venkatesh 2012),” and in “numerous empirical investigations,” (Vinnik, V., 2017 & Gharaibeh, N, Gharaibeh O., & Gharaibeh, M., 2020). (Vinnik, V., 2017). This construct “represents such features as efficiency, effectiveness, time/money saving, benefit seeking, etc. that are known to attract users to mobile applications,” says Vinnik, V. (2017). “Customers appear to be all the more strongly inspired to accept and use new technology in the event that they notice that this innovation is increasingly advantageous in their day by day life,” (Gharaibeh, N, Gharaibeh O., & Gharaibeh, M., 2020). According to Venkatesh et al. (2003) the effect of performance expectancy on behavioural intention is moderated by both gender and age being more salient to younger men. Younger men therefore have a higher performance expectancy due to their age compared to the elderly. B. Effort Expectancy (EE): Theoretical definition - Venkatesh et al. (2012) redefined EE when proposing UTAUT2 in the consumer context as “the degree of ease associated with consumers, “use of technology”. Vinnik, V. (2017) asserts: The construct concept was first formulated in the UTAUT model and originated from the findings of the earlier technology acceptance models which proved that perceived ease of use from TAM/TAM2 (Davis, 1989; Davis et al. 1989), complexity from MPCU (Thompson et al., 1991) and ease of use from IDT (Moore and Benbasat, 1991) have significant influence on the behavioral intention. Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020) aver that, “the intention of customers and users to accept or use a new technology is not only predicted by,” how the

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

technology is valued, but also by how, “this technology is easy,” to, “use and requires less efforts”. “In the starting periods of adoption of new technology, ease of use is significant, and it was found that effort expectancy influences the intentions to use significantly,” and, “only for inexperienced users”(Gharaibeh, N, Gharaibeh O., & Gharaibeh, M., 2020 & Vinnik, V., 2017). Venkatesh et al. (2003) affirms that, “when users gain experience in using the technology and learn more about it, the effort expectancy construct becomes not significant for behavioral intention”. Effort expectancy as moderated by age, gender and experience is more salient for older women with relatively little experience, affirm Venkatesh et al. (2003). C. Social Influence (SI): Theoretical definition - in the development of UTAUT2 to suit the consumer context Venkatesh et al. (2012) defined SI as, “the extent to which consumers perceive that important others (eg. family and friends) believe they should use a particular technology”. “Social influence reflects the influence of the opinions of people deemed important to individuals on the individuals’ user decision to use a technology activity (Venkatesh et al. 2003), describe Lai, I. K. W. & Shi, G., (2015) & Lemire, Sicotte, et al., (2008); Rodrigues, Lopes, & Tavares, (2013). Kit, A.K.L., Ni, A.H., Badri, E. N. F. B. M. & Yee, T. K. (2014) use Leong, Hew, Tan and Ooi, 2013 simple definition as, “an individual’s perception that significant others believe the individual should adopt the information system”. Vinnik, V. (2017) asserts: This variable was introduced in the initial UTAUT model (Venkatesh et al., 2003) and originally comes from TRA model where it was presented as Subjective Norm. It was also represented as subjective norm in TAM2, TPB/DTPB and C-TAM-TPB, as image in IDT and as social factors in MPCU (Venkatesh et al. 2012). In the UTAUT and UTAUT2

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

model, authors explained and interpreted the construct called Social influence similarly to Subjective norm construct from earlier adoption models (e.g. Fishbein & Azjen 1975, Davis et al., 1989, Mathieson 1991, Ajezen, 1991, Taylor & Todd 1995). “The perceptions provided by people surrounding customers (family members and friends) is very important in affecting customers’ awareness and the intention toward mobile applications,” says Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020. “These perceptions,” adds Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020), “also may affect customers rather than their own feelings and beliefs, especially if those surrounding people are important for the last”. “The major feature of an MIM-SN (Mobile Instant Messaging - Social Networking) app is that it enables users to communicate with friends, colleagues, and others…Unlike other technologies, the users of an MIM-SN app cannot use it alone,” state Lai, I. K. W. & Shi, G, (2015). Lai, I. K. W. & Shi, G, (2015) continue to say, “in cases in which many of a user’s friends are habitually using such an app, the consequent increased social pressure could lead to a higher continuance intention on the part of that user”. “There are many advocates about the significance of social influence on intention to use which has been positive” as has been proved in previous theoretical research, says Praveena, K. (2016). Praveena, K. (2016) adds, “Moreover, both opinions and information from some members would be more important to the user and, therefore, have a stronger influence on their decisions. “Therefore, the key factors of social pressures or subjective norms still happen in this situation,” state Praveena, K. (2016). “Furthermore, Lu, Yao, & Yu mentioned that social influences may also assist to shape an individual’s perception of users' self-confidence or having a skill to use a system very well,” say Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020). Venkatesh et al. (2003) aver that, “the

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

influence of social influence on behavioral intention will be moderated by gender, age, experience, such that the effect will be stronger for older inexperienced women. D. Facilitating Conditions (FC): Theoretical definition - this “is the perception that technical infrastructure exists to support the use of technology (Venkatesh et al. 2003),” say (Kit, A.K.L., Ni, A.H., Badri, E. N. F. B. M. & Yee, T. K., 2014) It “reflects the extent to which users have the ability and resources necessary to use a system (Venkatesh et al. 2003),” (Lai, I. K. W. & Shi, G., 2015). Venkatesh et al. (2012) redefined it for a consumer context, “as “consumers” perceptions of the resources and support available to perform a behaviour,” state Praveena, K. (2016). The availability of necessary resources facilitates the use of technology (Praveena, K., 2016). Vinnik, V. (2017) points out: This construct was first formulated in the model of MPCU Utilization (Thompson et al., 1991) and later was used to formulate the facilitating conditions construct described in the UTAUT model (Venkatesh et al., 2003). In the formulation of this concept Venkatesh et al. (2003) also showed how facilitating conditions construct from model of MPCU Utilization (Thompson et al., 1991) is similar to Perceived Behavioral Control from TPB/ DTPB, C-TAM-TPB described by Ajzen (1991) and Taylor & Todd (1995) and to Compatibility from IDT by Moore & Benbasat (1991). In the UTAUT2 consumer context unlike in the original UTAUT facilitating conditions has a direct effect on both behavioural intention and behaviour. Whereas in the later the effect was only on actual usage behaviour (Praveena, K., 2016 & Vinnik, V., 2017). This was because in the organisational organizational context of UTAUT, authors assume many aspects of facilitating conditions such as training and support “will be freely available within the

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

organisations organizations but it will differ in the consumer context especially across technology providers”. “For example, users with different level of access to information, tutorials and different models of mobile phones and phone functionalities and availability of support may find it more easy or complicated to start using the mobile application,” posit Praveena, K. (2016) & Vinnik, V. (2017). Lai, I. K. W. & Shi, G., (2015) assert: Although MIM-SN apps are a relatively easy-to-use technology, users must still possess the basic skills necessary to install such an app on their mobile phones. Additionally, users must learn how to share photos and videos as well as how to use other interactive features. If users can access resources that facilitate their use of an MIM-SN app, such as online tutorials, then they will have a higher intention to adopt that MIM-SN app on a continuing basis. “The availability of necessary resources facilitates the use of technology.” posits Praveena, K. (2016). “The effect of facilitating conditions on behavioral intention is moderated by age, gender, and experience,” affirms Venkatesh et al. (2012), being more salient for older women with minimum experience. Venkatesh et al. (2012) adds, “moreover, gender, age, and experience have a joint impact on the link between facilitating conditions and intention”. Operational Definition - operationally, facilitating conditions is defined as the perceptions that people with mental disorders have the resources and support available to them to enable them use WhatsApp (Praveena, K., 2016). Using Indrawati & Gusti Ayu Made Mas Marhaeni, G. A. M. (2015) definition we may define it as, “the degree to which,” people with mental disorders “believe that factors, such as availability of devices, knowledge, guidelines, and staff or people from social groups exist to support their use,” of WhatsApp. “It is measured in terms of the availability of resources (devices and technology) and help/support,” that people with mental

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

disorders get to be able to use WhatsApp (Praveena, K., 2016). “Facilitating Conditions is measured using four items from Venaktesh et al. (2012),” (Praveena, K., 2016).} The gender, age and experience of people with mental disorders is measured as moderating variables to the effect of facilitating conditions on behavioural intention. E. Hedonic Motivation (HM): Theoretical definition - Praveena, K. (2016) state, “the word “hedonic” means relating to, or characterised characterized by pleasure,” and thus hedonic motivation as defined by Venkatesh et al. (2012) is “the fun or pleasure derived from using a technology”. “Brown and Venkatesh (2005) defined hedonic motivation as an enjoyment or happiness resultant from using a technology,” adds Praveena, K. (2016). According to Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020) various studies have argued that hedonic motivation has, “a positive impact on intention to adopt the technology”. Venkatesh et al. (2012) state, “age, gender, and experience moderate the effect of hedonic motivation on behavioral intention, such that the effect is stronger among younger men in early stages of experience with a technology”. F. Price Value (PV): Theoretical definition - it “is defined as ‘consumers’ cognitive trade-offs between the perceived benefits and cost of using various applications,” as per Venkatesh et al. (2012). This according to Indrawati, I & Ariyanti, M. (2015), “might incorporate the costs of device and data, and different kinds of service charges”. “In the consumer context,” unlike in the organisational organizational setting of UTAUT, “price is an important factor because the consumer usually bears the monetary cost of” purchasing the, “device and service whereas employees do not” say Indrawati, I & Ariyanti, M.,

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

2015 & Chang, A., 2012). Chang,A. (2012) adds, “the cost and pricing structure may have a significant impact on consumers’ technology use”. “Price Value is positive when the benefits of using a technology are perceived to be greater than the monetary cost and such price value has a positive impact on intention” states Indrawati, I & Ariyanti, M. (2015). According to Praveena, K. (2016) “when prices are low, there are chances that technology use increases”. However, the price factor is getting negligible with rise in technology and a competitive market (Praveena, K., 2016). “In the case of mobile applications and other online applications, the cost of internet use stands insignificant as many of the companies provide minimal and low rates to attract users,’ states Praveena, K. (2016). The effect of price value on behavioural intention is moderated by age and gender being more salient to older women (Venkatesh et al. (2012). G. Habit (HB): Theoretical definition - according Venkatesh et al. (2012), habit is an “automating behaviour behavior from initial learning to regular use of a technology”. Habit has also been defined as, “the extent to which people tend to perform behaviours behaviors automatically because of learning" (Limayem et al., 2007). “Habit or Habitual use,” state Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020), “mirrors the numerous consequences of previous practices and experience and the consistency of past conduct is viewed as one of the central causes of present behavior”. Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020) say, “habit is normally viewed in two ways,” according to Kim and Malhotra (2005), “as a prior behaviour behavior and as the extent to which an individual believes the behavior to be automatic”. “System usage will be driven by conscious intention when the linkage between stimuli and action is not fully

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

developed”, and that “once the information system use becomes routine, past use is a proxy for habit,” says Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020). “Ajzen and Fishbein (2000) reports that repeated performance of a behaviour behavior can result in formation of attitude and attitudes can trigger intentions,” posit Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020). Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020) also avers, “time is also a factor for the formation of a habit”. Multiple relevant studies confirm the relationship between habit and intention and use behavior (Hew, Lee, Ooi, & Wei 2015). Vinnik, V. (2017) affirms this by saying: Wong et al. (2014) in the research of mobile TV adoption reported that habit influences the continual use of informational systems, and that usage of new technology on a daily, routine basis leads to development of habit what in turn supports the adoption the new technology...The mechanism for such influence on mobile applications can be the following; since mobile applications are normally created for the usage on a daily basis, consumers will have to use the app for a certain period of time to try it and discover its benefits, which contributes to the development of the habit to use certain application. Once the habit is developed – it will be harder for the user to switch towards another mobile application, and it is likely that users may not even consider using other mobile apps. In such a way habit will contribute to the intention to adopt mobile application. Venkatesh et al. (2012) asserts that, “age, gender, and experience moderate the effect of habit on behavioural intention and usage behaviour behavior , such that the effect is stronger for older men, with high levels of experience with the technology. H. Behavioural Intention (BI):

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Theoretical definition - according to Indrawati, I & Ariyanti, M. (2015) & Praveena, K. (2016), “behavioural intention is defined as an indication of an individual's readiness to perform a given behaviour behavior .” It may also be, “defined as the degree to which a person formulates conscious plans to perform or not perform some specified future behaviour behavior ,” (Aarts et al, 1998). “According to Fishbein and Ajzen (2010), BI measures a person's relative strength of intention to perform a behaviour behavior ,” and “it is assumed to be an immediate antecedent of behaviour behavior (Ajzen, 1985),” state (Indrawati, I & Ariyanti, M., 2015 & Praveena, K., 2016). This assumption is corroborated by, “Sheppard, Hartwick and Warshaw (1988), in their meta-analysis,” and, “Taylor and Todd (1995)”. Researchers have widely made efforts to study BI since the study of actual behaviour behavior is difficult in many circumstances. For example, predictions of actual purchase behaviours behaviors are always difficult to measure and hence most of the studies are done with the intention to purchase. (Indrawati, I & Ariyanti, M., 2015 & Praveena, K., 2016) Yi, Jackson, Park & Probst (2006) give another definition of BI as per Islam et al. (2013) who, “defined behavioural intention as an individual’s intention to perform a given act which can predict corresponding behaviours when an individual acts voluntarily”. “Besides that, behavioural intention is the subjective probability of carrying out behaviour and also the cause of certain usage behaviour,” points out Islam et al. (2013), Yi, Jackson, Park & Probst (2006). “According to Ajzen (1991:181), intentions are assumed to capture the motivational factors that influence a behavior,” state Indrawati, I & Ariyanti, M. (2015) & Praveena, K. (2016). The moderating variable Venkatesh et al. (2012) address in this regard is experience, and he quotes Jasperson et al. (2005) stating, “with increasing experience, routine behavior becomes

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

automatic and is guided more by the associated cues”. Therefore, states Venkatesh et al. (2012), “the effect of behavioral intention on technology use will decrease as experience increases”. Operational Definition - operationally, behavioural intention is defined as indications of how hard people with mental disorders are willing to try, or how much of an effort they are planning to exert, in order to use WhatsApp in the future (Indrawati, I & Ariyanti, M., 2015 & Praveena, K., 2016). This is measured in terms of the intention to continue the use of WhatsApp in daily life and frequently. It is measured using three items from Venkatesh et al. (2012). I. Use Behavior (UB): Theoretical definition - Indrawati, I & Ariyanti, M. (2015) state that “according to Wu, Tao, and Yang (2012:95), use behavior measures frequency of actual use of technology by user,” and that usage behaviour or, “use behaviour is treated as actual usage,”. Praveena, K. (2016) highlight that, “Venkatesh et al. (2012) have measured use by the different types of uses of mobile internet”. Therefore Praveena K (2016) suggested that,” usage can be determined by the type of use as well as the actual time taken to use the system.i.e, the variety and frequency of use contributes to usage”. According to Venkatesh et al. (2012), facilitating conditions, habit and behavioural intention all have direct effects on use behaviour. Operational definition - operationally, usage describes the type of use of and the actual time taken to use WhatsApp by persons with mental disorders. The type of use is measured by the extent of use of the different varied features offered by WhatsApp. “Nine main features are used to measure the type of use and the actual time is measured by one item which asks for the time actively spent for using SNS,”* as per Praveena, K. (2016). Criticisms

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

UTAUT2 is not without criticism. For instance, “Choi found although hedonic motivation was included as the predictor of behavioral intention and use behaviour in UTAUT2, it failed to discuss factors that lead to enjoyment,” posit Tamilmani, K., Rana, N. & Dwivedi, Y. (2017). “Though this theoretical framework is certainly valuable to study technology adoption in consumer contexts, more systematic investigation is needed to improve the formulation of the UTAUT2 and define a more parsimonious and consistent theory.” opine Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017). One of the major shortcomings of UTAUT2 is, “its complex interactions among the various attributes and moderators resulting in relatively less parsimony hindering its usage as such,” aver Tamilmani, K., Rana, N. & Dwivedi, Y. (2017). Majority of the studies, “which utilized UTAUT2 didn’t include moderating variables during conceptual model development,” state Tamilmani, K., Rana, N. & Dwivedi, Y. (2017). The irony of the theory is that, “since it is comprehensive in nature, it not only hinders efforts in extending the existing theory but also hampers the further theoretical advancement,” state Tamilmani, K., Rana, N. & Dwivedi, Y. (2017). In addition to proving it has been, “a successful model for studying technology acceptance and use in a variety of contexts,” another of its limitations is that to date there is very scarce empirical evidence of its validity and applicability, “and the results obtained are contradictory (Arenas-Gaitan, Peral-Peral, & Ramon-Jer onimo, 2015; Baptista & Oliveira, 2015),” pose Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017). According to Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017), there is another variable beyond “price value”, “directly related to the “cost” to consumers that has been identified as a barrier for technology adoption (e.g., for e-commerce and Internet use) in previous research:

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

privacy loss”. “In this sense, Joinson, Reips, Buchanan, and Schofield (2010) found evidence that privacy concerns influence people's willingness to disclose personal information on a website, acting as a “cost” in terms of privacy,” state Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017). Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017) go on to say, “more relevantly, Krasnova, Spiekermann, Koroleva, and Hildebrand (2010) confirmed that perceived privacy risk has a negative effect on consumers' self-disclosure on SNS”. “Accordingly, users' privacy concerns act as a potential cost in terms of information disclosure, which can negatively influence the acceptance of technology,” posit Herrero, Rodríguez, & García de los Salmones (2009). Reasons for choosing UTAUT2 This theoretical model was chosen due to the following reasons: first, it is a novel theory specifically designed to explain and predict technology adoption in consumption contexts based on the original UTAUT incorporating both main relationships from it as well as new predictors and mechanisms relevant to this context; as is the case in individuals´ use of SNS to publish contents about their experiences with brands and products as per Mara del Mar Garcia- De los Salmone s, Gutiérrez , H. S M.. & Herrero-Crespo , A. (2017) & Vinnik, V. (2017) Second according to Vinnik, V. (2017): in addition to the constructs that represent the specific features of mobile applications, UTAUT2 model also includes the constructs that are widely confirmed to be relevant for information technology adoption but are not yet properly researched in the mobile app context. These variables are Price Value and Habit”.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

“Third, this model offers a global and integrative approach, as it incorporates the main explanatory variables of 8 previous theoretical models about technology acceptance and use,” say Mara del Mar Garcia-De los Salmone s, Gutiérrez , H. S. M.. & Herrero-Crespo , A. (2017) Fourth, “the model is perfect for extending it with constructs that represent influences from new social dimensions,” state Vinnik, V. (2017) Fifth, UTAUT2 has “the highest explained variance compared with other adoption models”; in comparison to UTAUT there is an improvement of 56% to 74% in behavioural intention and 40% to 52% in technology use aver Vinnik, V. (2017 ) “Finally, it has proven to be a successful model for studying technology acceptance and use in a variety of contexts, although the empirical evidence about its validity and applicability is very scarce to date and the results obtained are contradictory (Arenas-Gaitán et al. 2015; Baptista and Oliveira 2015). (Mara del Mar Garcia-De los Salmone s, Gutiérrez , H. S. M.. & Herrero- Crespo , A., 2017) “Based on these arguments we can see that UTAUT2 model is the most relevant model for this research as it is much more specific for the needs of our study than other models, as it allows us measure the model with most of the features that are described to influence the user intention to adopt Whatsapp”. (Vinnik, V., 2017) By understanding how and why people with mental disorders use technology, we can better aid curating technology that fits their condition to better aid in their recovery journey . UTAUT2 helps us better understand the ways in which people use technology for their benefit. If the same school of thought is applied to people with mental disorders, then the research can reveal how these individuals use technology and more so for their recovery journey. Thus aiding in creating technology centered at the recovery journey of these persons. UTAUT2 can therefore assist in comprehension of how social media such as WhatsApp

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

can aid in the recovery journey of people with mental disorders by understanding how and why they accept and use WhatsApp. The researcher will utilize UTAUT2 to determine if and how WhatsApp is used for the recovery journey of persons with mental disorders. The theory will be used to assess the degree to which WhatsApp aids people with mental disorders in thier recovery journey.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

References Aarts, H., Verplanken, B., & Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374 Aggelidis, V. P. & Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. Int J Med Inform. ; 78(2): 115–26. doi: 10.1016/j.ijmedinf.2008.06.006. Ahn, D., & Shin, D.-H. (2013). Is the social use of media for seeking connectedness or for avoiding social isolation? Mechanisms underlying media use and subjective well-being. Computers in Human Behavior, 29(6), 2453–2462. Ajzen, I. & Fishbein, M. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behaviour. in In Action Control (Kuhland, J., and Beckman, J. (Eds ) , ), From Cognition to Behaviour, pp.11- 39 ) 39) . Heidelberg: Springer Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes ; 50(2): 179–211. doi: 10.1016/0749-5978(91)90020-T. Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European review of social psychology, 11(1), 1-33 Ajzen, I., Perceived behavioral control, self‐efficacy, locus of control, and the theory

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

of planned behavior 1. Journal of applied social psychology, 2002. 32(4): p. 665-683. Alvarez-Jimenez, M., Bendall, S., Koval, P., Rice, S., Cagliarini, D., Valentine, L., et al. (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services. BMJ Open, 9 (2), e024104. American Psychiatric Association. (2021). What is mental illness? Retrieved April 14th, 2021 from https://www.psychiatry.org/patients-families/what-is-mental-illness Arenas-Gait an, J., Peral-Peral, B., & Ramon- Jer onimo Jer onimo , M. A. (2015). Elderly and Internet and Internet Banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20(1). Aschbrenner, K. A., Naslund, J. A., Gorin, A. A., Mueser, K. T., Scherer, E. A., Viron, M., et al. (2018a). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: protocol for a randomized controlled trial. Contemporary Clinical Trials, 74 , 97–106. Aschbrenner, K. A., Naslund, J. A., Grinley, T., Bienvenida, J. C. M., Bartels, S. J., & Brunette, M. (2018b). A survey of online and mobile technology use at peer support agencies.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Psychiatric Quarterly , 1–10. Bandyopadhyay, K., & Bandyopadhyay, S. (2010). User Acceptance of Information Technology Across Cultures. International Journal of Intercultural Information Management, 2(3), 218–231. Baptista, G. & Oliveira, T. (2015). Understanding mobile banking: The unified theory acceptance and use of technology combined with cultural moderators. Computers in Human Behaviour, 50, 418-430. Batterham, P. J., & Calear, A. J. (2017). Preferences for internet-based mental health interventions in an adult online sample: findings from ann an online community survey. JMIR Mental Health, 4 (2), e26. Beer, D. 2008. “Social Network(ing) sites…Revisiting the Story So Far: A Response to danah boyd & Nicole Ellison.” Journal of Computer-Mediated Communication 13: 516- 529. Berger, R. (1996). Efficient replenishment and EDI. ECR Europe. Berland, G. K., Elliott, M. N., Morales, L. S., Algazy, J. I., Kravitz, R. L., Broder, M.S., et al. (2001). Health Information on the Internet: Accessibility, Quality, and Readability in English and Spanish. JAMA; 285(20): 2612–21. doi: 10.1001/jama.285.20.2612. Biagianti, B., Quraishi, S. H., & Schlosser, D. A. (2018). Potential benefits of incorporating peer- to-peer interactions into digital interventions for psychotic disorders: a systematic review. Psychiatric Services, 69 (4), 377–388.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., & Confino, J. (2017b). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Intervention in Psychiatry, 11 (4), 290–295. Boyd D Ellison NB . NB. Social network sites: definition, history, and scholarship. J Comput Mediat Commun 2008 ;13:210 ; 13:210 –30 Brown, S. A. & Venkatesh, V. (2005). Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle. MIS Quarterly ; 29(3): 399–426. Brunette, M., Achtyes, E., Pratt, S., Stilwell, K., Opperman, M., Guarino, S., & Kay-Lambkin, F. (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention. Community Mental Health Journal , 1–6. Brusilovskiy, E. (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses. Computers in Human Behavior , 65 , 65 , 232-240. Retrieved September 18th, 2020 from Bucci, S., Schwannauer, M., & Berry, N. (2019). The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice, 92 (2), 277– 297. Chan, K. Y., Gong, M., Xu, Y. & Thong, J. (2008). Examining User Acceptance of SMS: an Empirical Study in China and Hong Kong. PACIS Proceedings ; 9(2): 396–401. Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. Journal The

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

WINNERS ; Vol. 13, No. 2; 106-114 Chang, M. Y., Han, D. H., Moon, I. J., Kim, S. T., Kim, D. Y., Lee, C. H., et al. (2010). Assessment of Allergic Rhinitis Websites in Korea. Clin Exp Otorhinolaryngol; 3(1): 32– 6. doi: 10.3342/ceo.2010.3.1.32. Chen, Y.-F. (2014). See you on Facebook: exploring influences on Facebook continuous usage. Behaviour & Information Technology, 33(11), 1208–1218. Childers, T. L., Carr, C. L., Peck, J. & Carson, S. (2001). “Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior,” Journal of Retailing (77:4), pp. 511-535. Choi, S.: The flipside of ubiquitous connectivity enabled by smartphone-based social networking service: Social presence and privacy concern. Computers in Human Behavior, 65, 325-- 333 (2016) Christian, M., Konrad, B., Rayna, S., Bernd, L., Ionut, A., BOris, T., Mark E., & Alexander, M. (2015). Smartphone usage in the 21st century: who is active on WhatsApp? BMC Notes , 331. Retrieved August 17th, 2021 from h ttps://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-015-1280- z Davis, F. D. & Venkatesh, V. (2004). Toward Preprototype User Acceptance Testing of New Information Systems: Implications for Software Project Management. IEEE Transactions on Engineering Management ; 51(1): 31–46. doi: 10.1109/TEM.2003.822468. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly ; 13(3): 319–340. doi: 10.2307/249008.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Deb, M. and E. Lomo-David, An empirical examination of customers’ adoption of m-banking in India. Marketing Intelligence & Planning, 2014. 32(4): p. 475-494. Dodds, W. B., Monroe, K. B. & Grewal, D. (1991). Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations. Journal of Marketing Research ; 28(3): 307– 319. doi: 10.2307/3172866. experiences Experiences on the internet affect people's health? Milbank Quarterly 90, 219–249. Eysenbach, G., Powell, J., Englesakis, M., Rizo, C., & Stern, A. (2004). Health related virtual communities and electronic support groups support groups : systematic review systematic of review the of effects the of effects online of online peer to peer interactions. BMJ, 328(7449), 1166. Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophrenia Bulletin, 42(2), 448–455. Fishbein, M., & Ajzen, I. (1975). Belief, attitudes, intention, and behavior. An introduction to theory and research. Massachussets Massachusetts : Addison-Wesley. Fishbein, M., & Ajzen, I. (2010). Belief, attitudes, intention, and behavior. An introduction to theory and research. Massachussets Massachusetts : Addison-Wesley. Fox, S. (2013). Health. Pew Internet. 2013-01

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Gharaibeh, N, Gharaibeh O., & Gharaibeh, M. (2020). Exploring intention to adopt mobile commerce: integrating UTAUT2 with social media.International media. International Journal of Scientific & Technology Research. Retrieved November 12th, 2021 from https://www.researchgate.net/profile/Malik- Gharaibeh/publication/340128776_Exploring_Intention_to_Adopt_Mobile_Commerce_I ntegrating_UTAUT2_with_Social_Media/links/5e7b612c92851caef4996920/Exploring- Intention-to-Adopt-Mobile-Commerce-Integrating-UTAUT2-with-Social-Media.pd f Gleeson, J., Lederman, R., Herrman, H., Koval, P., Eleftheriadis, D., Bendall, S., Cotton, S. M., & Alvarez-Jimenez, M. (2017). Moderated online social therapy for carers careers of young people recovering from first-episode psychosis: study protocol for a randomised randomized controlled trial. Trials, 18 (1), 27. Glick, G., Druss, B., Pina, J., Lally, C., & Conde, M. (2016). Use of mobile technology in a community mental health setting. Journal of Telemedicine and Telecare, 22(7), 430–435. González ME, Quesada G, Urrutia I, Gavidia JV. (2006). Conceptual Design of an E- health Strategy for the Spanish Health Care System . Int J Health Care Qual Assur Inc Inc. Leadersh Health Serv; 19(2):146–57. doi: 10.1108/09526860610651681. Gowen, K, Deschaine, M, Gruttadara, D, Markey, D (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community. Psychiatric Rehabilitation Journal 35, 245–250.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Haker H, Lauber C, Rossler W (2005). Internet forums: a self-help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica 112, 474–477 Herrero, A., Martín, H. S. & Garcia-De los Salmones, M. M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in human behaviour behavior . Retrieved November 13th, 2021 from http://pdf-s3.xuebalib.com:1262/xuebalib.com.33417.pd f Herrero, A., Rodriguez, L. & Garcia de los Salmones, M. M. (2009). The influence of perceived risk on internet shopping behaviour behavior ; A multidimensional perspective. Journal of Risk Research, 12(2), 259-277. Indrawati & Gusti Ayu Made Mas Marhaeni, G. A. M. (2015). Predicting instant messenger application adoption using a unified theory of acceptance and use of technology 2. Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 11-13 August, 2015 Istanbul, Turkey. Universiti University Utara Malaysia. Retrieved November 10th, 2021 from http://repo.uum.edu.my/15681/1/PID030.pd f Indrawati & Gusti Ayu Made Mas Marhaeni. (2015). Measurement for analyzing instant messenger application adoption using a unified theory of acceptance and use of technology 2. International Business Management 9 (4) 391-396. Indrawati, I & Ariyanti, M. (2015). A Proposed Model for Measuring Instant Messenger Application Adoption A a Comparative Study between Indonesia and Malaysia

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Customers. Retrieved November 8th, 2021 from https://www.dline.info/jet/fulltext/v6n2/v6n2_1.pd f Jadad, A. R, & Gagliardi, A. (1998). Rating Health Information on the Internet: Navigating to Knowledge or to Babel? JAMA; 279(8):611–4. doi: 10.1001/jama.279.8.611. Jenkins-Guarnieri, M. a., Wright, S. L., & Hudiburgh, L. M. (2012). The relationships among attachment style, personality traits, interpersonal competency, and Facebook use. Journal of Applied Developmental Psychology, 33(6), 294–301. Joinson, A. N., Reips, U.-D., Buchanan, T., & Schofield, B. P. (2010). Privacy, trust, and self- disclosure online. HumaneComputer Humane Computer Interaction, 25(1), 1e24. Kalckreuth, S., Trefflich, F., & Rummel-Kluge, C. (2014). Mental health related internet use among psychiatric patients: a cross-sectional analysis. BMC Psychiatry, 14, 368. Retrieved March 30th, 2021 from Keong, M. L., Ramayah, T., Kurnia, S., & Chiun, L. M. (2010). Explaining intention to use an enterprise resource planning ( ERP ( ) ERP) system : an extension of the UTAUT model. Business Strategy Series, 13(4), 173–180. Kijsanayoting, B., Pannarunothai, S. & Speedie, S. M. (2009). “Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model,” International Journal of Medical Informatics, 78, pp. 404-416. doi: 10.1016/j.ijmedinf.2008.12.005.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Kim, S. S. & Malhotra, N. K. (2005). A longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Post Adoption Phenomena. Management Science ; 51(5): 741–755. doi: 10.1287/mnsc.1040.0326. Kim, S. S., Malhotra, N. K. & Narasimhan, S. (2005). Research Note—Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison. Information Systems Research ; 16(4): 418–32. doi: 10.1287/isre.1050.0070. Kit, A.K.L., Ni, A.H., Badri, E. N. F. B. M. & Yee, T. K. (2014). UTAUT2 Influencing the behavioural intention to adopt mobile applications. Retrieved December 11th, 2021 from http://eprints.utar.edu.my/1277/1/AC-2014-1005389.pd f Kortemann, M., editor. (2005). Cultural Background and Technology Acceptance: Evaluation of ICT Projects that Bridge the Digital Divide. IWIPS. Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online social networks: Why we disclose. Journal of Information Technology, 25, 109e125. Kwanya, T., Stilwell, C., & Underwood, P. (2012). The application of Web 2.0 tools by libraries in Kenya: A reality check. (T. Kwaya, C. Stilwell, & P. Underwood, Producers) Retrieved October 12, 2020 from Kwon, S. J., Park, E., & Kim, K. J. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 51(4), 1–11.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Lai, I. K. W. & Shi, G. (2015). The impact of privacy concerns on the intention of continued use of an integrated mobile instant messaging and social network platform. Int. J. Mobile Communications; 13, 6, pp. 641-669. Retrieved December 4th, 2021 from https://www.doc88.com/p-1116367007822.html?r= 1 LaRose, R., Kim, J., & Peng, W. (2010). Social networking: Addictive, compulsive, problematic, or just another media habit. A Networked Self: Identity, Community, and Culture on Social Network Sites, 59–81. Lian, J. W. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35(1), 98-109. Light, B. (2014). Disconnecting with social networking sites. Palgrave Macmillan, New York NY. Retrieved April 02, 2021 from https://DOI 10.1057/9781137022479. Limayem, M., Hirt, S. G. & Cheung, C. M. K. (2007). “How Habit Limits the Predictive Power of Intentions: The Case of IS Con- tinuance,” MIS Quarterly 31(4), pp. 705-737. Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595– 603. Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Lu, J., J.E. Yao, and C.-S. Yu, Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 2005. 14(3): p. 245-268. Malaquias, R.F. and Y. Hwang, An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 2016. 54: p. 453-461. Manikonda, L., & Choudhury, M. D. (May 2017). Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media. Retrieved September 14, 2020 from https://dl.acm.org/doi/abs/10.1145/3025453.3025932 Mara del Mar Garcia-De los Salmone s, Gutiérrez , H. S. M .. . & Herrero-Crespo , A. (2017). Explaining the Adoption of Social Networks Sites to Share User-Generated Content: A Revision of the UTAUT2, Computers in Human Behavior; 71 , C , C . pp 209–217 DOI: 10.1016/j.chb.2017.02.00 7 Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. Mashhour, A.S. and Z. Saleh, Community Perception of the Security and Acceptance of Mobile Banking Services in Bahrain: An Empirical Study. International Journal of Advanced Computer Science and Applications, 2015. 6(9): p. 46-54. Mazman, S. G., & Usluel, Y. K. (2009). The usage of social networks in educational context.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

World Academy of Science, Engineering and Technology, 49(1), 404-408. Miller, BJ, Stewart, A, Schrimsher, J, Peeples, D, Buckley, PF (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use. Psychiatry Research 225, 458–463. Mohr, D.C., Burns, M. N., Schueller, S. M., Clarke, G. & Klinkman, M. (2013). Behavioural Behavioral Intervention Technologies: Evidence review and recommendation for future research in mental health. General Hospital Psychiatry , 35 (4), 332-338. Moore, K., & McElroy, J. C. (2012). The influence of personality on Facebook usage, wall postings, and regret. Computers in Human Behavior, 28(1), 267–274 Naslund J., Aschbrenner K. & Bartels, S. (2015). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences. Naslund, J. A., Aschbrenner, K. A., & Bartels, S. J. (2016). How people living with serious mental illness use smartphones, mobile apps, and social media. Psychiatric Rehabilitation Journal, 39 (4), 364–367. Naslund, J. A., Aschbrenner, K. A., Marsch, L. A. & Bartels, S. J. (2016). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 25, 113-122. DOI: https://doi.org/10.1017/S2045796015001067 Naslund, J. A., Aschbrenner, K. A., McHugo, G. J., Unützer, J., Marsch, L. A., & Bartels, S. J.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

(2019). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness. Early Intervention in Psychiatry, 13 (3), 405–413. Naslund, J. A., Grande, S. W, Aschbrenner, K. A, Elwyn, G. (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PloS ONE 9, e110171. Naslund, J.A., Bondre, A., Torous, J., & Aschbrenner, K.A. (2020). Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. Journal of Technology in Behavioral Science , 5 , 5 , 245–257. Retrieved September 14, 2020 from National Standards for Mental Health Services. (2010 ). 7 Definitions - Department of health. Retrieved April 7th , 2021 , 2021 from Nikou, S. and Bouwman, H. (2014), “Ubiquitous use of mobile social network services”, Telematics and Informatics, Vol. 31 No. 3, pp. 422-433. Or, C. K. & Karsh, B. T. (2009). A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc ; 16(4): 550–60. doi: 10.1197/jamia.M2888. Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3 (2), 173–182. Oye, N. D., N.a, L., & Rabin, Z. A. (2011). A Model of ICT Acceptance and Use for Teachers in Higher Education Institutions. International Journal of Computer Science &

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Communication Networks, 1(1), 22– 40. Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., et al. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392(10157), 1553–1598. Pavalanathan, U., & Choudhury, M.D. (May 2015). Identity Management and Mental Health Discourse in Social Media. Praveena, K. (2016).User Acceptance and Usage of Social Networking Sites: Validation of an Extension to UTAUT2 Model. School of Management Studies Cochin University of Science and Technology. Retrieved November 4th, 2021 from file:///home/chronos/u- 24b5d48c72df4caa58b0b159cace764b74a88de7/MyFiles/Downloads/Dyuthi%20T- 2349.pdf Ramin, R ., Zhila, K. , , Somaye, Z. A ., Roxana, S ., & Hossein, M . (2017). Development of the UTAUT2 model to measure the acceptance of medical laboratory portals by patients in Shiraz. Electron Physicia n, 9(2): 3862–3869. doi: 10.19082/3862 Retrieved September 14, 2020 from https://dl.acm.org/doi/abs/10.1145/2740908.2743049 Rice SM, Goodall J, Hetrick SE, Parker AG, Gilbertson T, Amminger GP, Davey CG, McGorry PD, Gleeson J, Alvarez-Jimenez M (2014). Online and social networking interventions for the treatment of depression in young people: a systematic review. Journal of Medical Internet Research 16, e206pp

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Sajithra, K. & Dr. Patil, Rajndra. (2013). Social media - History and components. J ournal of Business and Management , 7(1), 69-74. Salim, B. (2012). An Application of UTAUT Model for Acceptance of Social Media in Egypt : A Statistical Study. International Journal of Information Science, 2(6), 92–105. Schaper, L. K. & Pervan, G. P. (2007). ICT and OTs: A Model of Information and Communication Technology Acceptance and Utilisation by Occupational Therapists. Int J Med Inform; 76: S212–21. doi: 10.1016/j.ijmedinf.2006.05.028. Schrank B, Sibitz I, Unger A, Amering M (2010). How patients with schizophrenia use the internet: qualitative study. Journal of Medical Internet Research 12, e70 Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The Theory of Reasoned Action : A Meta-Analysis of Past Research with Recommendations for Modifications and Future Research. The Journal of Consumer Research, 15(3), 325–343. Silberg, W. M., Lundberg, G. D. & Musacchio, R. A. (1997). Assessing, controlling, and assuring the quality of medical information on the Internet: Caveant lector et viewor--Let the reader and viewer beware. JAMA ;277(15):1244–5. doi: 10.1001/jama.1997.03540390074039.] Slade, E. L., Williams, M. & Dwivedi & Dwivedi , Y. (2013). An extension of the UTAUT 2 in a healthcare context. UK Academy for Information Systems Conference Proceedings 2013. 55. Retrieved November 4th, 2021 from http://aisel.aisnet.org/ukais2013/55 Sledgianowski, D., & Kulviwat, S. (2009). Using Social Network Sites: The Effects of

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Playfulness, Critical Mass and Trust in a Hedonic Context. Journal of Computer Information Systems, 49(4), 74–83. Sood, S. P, Nwabueze, S. N., Mbarika, V. W. A., Prakash, N., Chatterjee, S., Ray, P., et al. editors. (2008). Proceedings of the 41st Annual. IEEE. Electronic medical records: a review comparing the challenges in developed and developing countries. Hawaii International Conference on System Sciences. Taiwo, A. A. & Downe, A. G. (2013). The Theory of User Acceptance and Use of Technology (UTAUT): A Meat-Analytic Review of Empirical Findings. Journal of Theoretical and Applied Information Technology. ; 49(1): 48–58. Tamilmani, K., Rana, N. & Dwivedi, Y. (2017). A Systematic Review of Citations of UTAUT2 Article and Its Usage Trends. 16th Conference on e-Business, e-Services and e-Society (I3E), Delhi, India. Pp.38-49. Retrieved November 13th, 2021 from https://hal.inria.fr/hal-01768514/document Taylor, S. & Todd, P. A. (1995). “Understanding Information Technology Usage: A Test of Competing Models,” Information Systems Research (6:2), pp. 144-176. Thong J. Y. L., Hong, S. J. & Tam, K. Y. (2006). “The Effects of Post-Adoption Beliefs on the Expectation–Confirmation Model for Information Technology Continuance,” International Journal of Human-Computer Studies 64(9), pp. 799-810. . Torous, J., Chan, S. R., Tan, S. Y.-M., Behrens, J., Mathew, I., Conrad, E. J., et al. (2014a). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Mental Health, 1(1), e5. Torous, J., Friedman, R., & Keshavan, M. (2014b). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth and uHealth, 2(1), e2. Trefflich, F., Kalckreuth, S., Mergl, R., & Rummel-Kluge, C. (2015). Psychiatric patients' internet use corresponds to the internet use of the general public. Psychiatry Research, 226 , 136–141. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. Management Information Systems Quarterly ; 27(3): 425–478. Venkatesh, V., Thong, J. Y. & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly ; 36(1): 157–78. Venkatesh, V., Thong, J. Y. L., Xu, X. (2016) Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association of Information Systems, 17(5), 328- 376 . 376.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Vinnik, V. (2017). User adoption of mobile applications: Extension of UTAUT2 model. Retrieved November 16th, 2021 from https://openaccess.nhh.no/nhh- xmlui/bitstream/handle/11250/2487036/masterthesis.PDF?sequence= 1 Wang, H.-I. & Yang, H.-L. (2005). “The Role of Personality Traits in UTAUT Model under Online Stocking,” Contemporary Management Research, 1, pp. 69-82. Wong, C., Wei-Han Tan, G., Loke, S., & Ooi, K. (2014). Mobile TV: a new form of entertainment ?. . Industrial Management & Data Systems, 114(7), 1050-1067. http://dx.doi.org/10.1108/imds-05-2014-014 6 Wu, C.-C., Huang, Y., & Hsu, C.-L. (2014). Benevolence trust: a key determinant of user continuance use of online social networks. Information Systems and E-Business Management, 12(2), 189–211. Wu, Y.L., Tao, Y., and Yang, P.C. (2008).The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users, Journal of Statistics & Management Systems, 11 (5), 919–949. Xiuyan, S. & Mikko, S. (2011). Consumer Acceptance and Use of Information Technology: Adding consumption theory to UTAUT2. Retrieved October 22, 2021 from https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1470&context=sprouts_al l) Zheng, K., Fear, K., Chaffee, B. W., Zimmerman, C. R., Karls, E. M., Gatwood, J. D., et al. (2011). Development and validation of a survey instrument for assessing prescribers’ perception of computerized drug–drug interaction alerts. J Am Med Inform Assoc ;

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

18(Supple 1): i51–61. doi: 10.1136/amiajnl-2010-000053. Zhou, T. (2011). Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207–218. Ziebland, S. & Wyke, S. (2012). Health and illness in a connected world: how might sharing

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Chapter 2 Revised Corrected Copy

Related Documents