Use and Impact of Online Travel Reviews for Planning Free and Easy Holidays

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Psychological Empowerment ◽  
Dual Process ◽  
Dual Process Theory ◽  
Intention To Use ◽  
Research Findings ◽  
Normative Social Influence ◽  
Online Travel Reviews ◽  
Online Travel

Online travel reviews (OTRs) are used by travelers to plan and book their holiday particular by the free and easy traveler. Hence, it is essential for hoteliers to understand the factors that affect and empower travelers when using OTRs to plan their holidays. This study has adopted three theories: dual-process theory, psychological empowerment (PE) and TPB. These theories were used to test additional dimensions of the informational and normative social influence, its links with perceived empowerment, and their impact on the intention to use OTRs. Using SmartPLS to analyze the data collected from 392 Malaysian, the results show that PE is significantly affected by informational and normative social influence, from travelers’ perception of PE positively affects their attitude toward using OTRs, and attitude significantly influences intention to use OTRs when plans for travel. Furthermore, the research findings contribute to the literature on travelers’ behavioral intention; this study also has direct implications for online travel providers.

scholarly journals Investigating the Determinants of Students’ Intention to Use Business Simulation Games

2019 ◽  
Vol 58 (2) ◽  
pp. 433-458 ◽  
Author(s):  
Yu-Yin Wang ◽  
Yi-Shun Wang ◽  
Shi-En Jian
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Partial Least Square ◽  
Intention To Use ◽  
Business Simulation ◽  
Simulation Games ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

Business simulation games (BSGs) are educational tools that help students develop business management knowledge and skills. However, to date, relatively little research has investigated the factors that influence students’ BSG usage intention. Grounded on the extended unified theory of acceptance and use of technology, this study helped to fill this gap by exploring intention to use BSGs. Specifically, this study investigated the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and price value on behavioral intention to use BSGs. Data collected from 141 useful respondents were tested against the research model using partial least square approach. The results of this study indicated that behavioral intention to use BSGs was influenced by facilitating conditions, hedonic motivation, and price value. Unexpectedly, performance expectancy, effort expectancy, and social influence were not predictive of students’ behavioral intention to use BSGs. These findings enhanced our understanding of students’ BSG usage behavior and provided several important theoretical and practical implications for the application of BSG in the context of business and management education.

scholarly journals Factors influencing trust and behavioral intention to use Airbnb service innovation in three ASEAN countries

2020 ◽  
Vol 14 (2) ◽  
pp. 175-188
Author(s):  
Evelyn Lim Chua ◽  
Jason Lim Chiu ◽  
Candy Lim Chiu
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Quantitative Methods ◽  
Business Models ◽  
Service Providers ◽  
Sharing Economy ◽  
The Philippines ◽  
Intention To Use ◽  
Content Type ◽  
Behavioral Intention To Use

Purpose The sharing economy is described as a community marketplace, particularly home sharing such as Airbnb, which is more prevalent. Airbnb changed the way renters and tourists find places to stay when they are traveling. The company introduced innovations in business models and technologies. So, Airbnb requires specific factors that will influence consumers’ trust because consumers intuitively seek out trusting factors to make judgments on innovative service providers. Thus, the purpose of this study is to understand the factors that influence travelers’ trust to use Airbnb within the three ASEAN nations. Design/methodology/approach The data were collected from both qualitative and quantitative methods. The questionnaire was the main data-gathering instrument used in this study and supplemented by informal interviews. A self-administered questionnaire was provided to 130 Airbnb users from the Philippines, Indonesia and Singapore using Hayes’ Process Macro as the statistical tool. Findings The correlation test was carried out to determine the strength and relationships among the independent, mediating and dependent variables. All independent variables are positively correlated with the mediating variable. The results reveal that ease of use, convenience, information social influence, normative social influence and security have a significant impact on trust and behavioral intention to use Airbnb. Originality/value This study contributes to the field of sharing economy, particularly home sharing, by examining different factors that influence trust and behavioral intention. This study focused on the case of Southeast Asian consumers, so this study is useful for marketing practitioners to enhance their marketing strategies in catering to this segment of the market.

scholarly journals Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey

10.2196/15023 ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. e15023 ◽  
Author(s):  
Yiyu Zhang ◽  
Chaoyuan Liu ◽  
Shuoming Luo ◽  
Yuting Xie ◽  
Fang Liu ◽  
...  
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Diabetes Management ◽  
Intention To Use ◽  
Unified Theory ◽  
Research Model ◽  
Web Based ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Facilitating Conditions

Background Diabetes poses heavy social and economic burdens worldwide. Diabetes management apps show great potential for diabetes self-management. However, the adoption of diabetes management apps by diabetes patients is poor. The factors influencing patients’ intention to use these apps are unclear. Understanding the patients’ behavioral intention is necessary to support the development and promotion of diabetes app use. Objective This study aimed to identify the determinants of patients’ intention to use diabetes management apps based on an integrated theoretical model. Methods The hypotheses of our research model were developed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). From April 20 to May 20, 2019, adult patients with diabetes across China, who were familiar with diabetes management apps, were surveyed using the Web-based survey tool Sojump. Structural equation modeling was used to analyze the data. Results A total of 746 participants who met the inclusion criteria completed the survey. The fitness indices suggested that the collected data fit well with the research model. The model explained 62.6% of the variance in performance expectancy and 57.1% of the variance in behavioral intention. Performance expectancy and social influence had the strongest total effects on behavioral intention (β=0.482; P=.001). Performance expectancy (β=0.482; P=.001), social influence (β=0.223; P=.003), facilitating conditions (β=0.17; P=.006), perceived disease threat (β=0.073; P=.005), and perceived privacy risk (β=–0.073; P=.012) had direct effects on behavioral intention. Additionally, social influence, effort expectancy, and facilitating conditions had indirect effects on behavioral intention that were mediated by performance expectancy. Social influence had the highest indirect effects among the three constructs (β=0.259; P=.001). Conclusions Performance expectancy and social influence are the most important determinants of the intention to use diabetes management apps. Health care technology companies should improve the usefulness of apps and carry out research to provide clinical evidence for the apps’ effectiveness, which will benefit the promotion of these apps. Facilitating conditions and perceived privacy risk also have an impact on behavioral intention. Therefore, it is necessary to improve facilitating conditions and provide solid privacy protection. Our study supports the use of UTAUT in explaining patients’ intention to use diabetes management apps. Context-related determinants should also be taken into consideration.

Smartphone Alcohol Use Disorder Recovery Apps: an Survey of Behavioral Intention to Use using UTAUT (Preprint)

2021 ◽  
Author(s):  
Rijuta Menon ◽  
Julien Meyer ◽  
Pria Nippak ◽  
Housne Begum
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Alcohol Use Disorder ◽  
Survey Study ◽  
P Value ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

BACKGROUND Alcohol Use Disorder (AUD) carries a huge health and economic cost to society. Effective interventions exist but numerous challenges limit their adoption, especially in a pandemic context. AUD recovery apps (AUDRA) have emerged as a potential complement to in-person interventions. They are easy to access and show promising results in terms of efficacy. However, they rely on individual adoption decision and remain underused. OBJECTIVE The aim of this survey study is to explore the beliefs that determine the intention to use AUDRA. METHODS We conducted a cross-sectional survey study of people suffering from AUD. We used the Unified Theory of Acceptance and Use of Technology, which predicts use and behavioral intention to use based on performance expectancy, effort expectancy, social influence and facilitating conditions. Participants were recruited directly from two sources: first, respondents at addiction treatment facilities in Ontario, Canada were contacted in person and filled a paper form; second, members from AUD recovery support groups on social media were contacted and invited to fill an online sruvey. The survey was conducted between October 2019 and June 2020. RESULTS The final sample was comprised of 159 participants (124 online and 35 paper based) self-identifying somewhat or very much with AUD. Most participants (85.5%) were aware of AUDRA and those participants scored higher on performance expectancy, effort expectancy and social influence. Overall, the model explains 35.4% of the variance in behavioral intention to use AUDRA and 11.1% of the variance in use. Social influence (p-value 0.314), especially for women (p-value 0.227) and effort expectancy (p value 0.247) were key antecedents of behavioral intention. Facilitating conditions was not significant overall but was moderated by age (p value 0.231) suggesting that it matters for older participants. Performance expectancy did not predict behavioral intention, which is unlike many other technologies but confirms other findings with mhealth. Open-ended questions suggest that privacy concerns may play a significant role for AUDRA. CONCLUSIONS This study suggests that unlike many other technologies, the adoption of AUDRA is not mainly determined by utilitarian factors such as performance expectancy. Rather, effort expectancy and social influence play a key role in determining the intention to use AUDRA.

Individual Differences in Implicit Learning

2015 ◽  
pp. 61-85
Author(s):  
Daisuke Nakamura
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Implicit Learning ◽  
Evolutionary Theory ◽  
Dual Process ◽  
Dual Process Theory ◽  
Process Theory ◽  
Future Research ◽  
Individual Difference Variables ◽  
Research Findings

This chapter reviews research on whether individual differences in psychometric intelligence, working memory, and other less investigated variables, such as emotion and personality, affect implicit learning, with particular focus on Reber's evolutionary theory and Kaufman's dual-process theory for implicit learning. The review shows that while the null effects of psychometric intelligence on implicit learning seems robust as both theories claim, those of working memory were unclear due to methodological insufficiency. For the effects of emotion and personality, further investigation is needed as studies in this direction have just begun to proliferate. The chapter concludes that the research findings on the effects of these individual difference variables on implicit learning are still inconclusive, except for psychometric intelligence, and provides suggestions for future research.

scholarly journals Analysis of Factors Affecting Behavioral Intention to Use E-Learning Uses the Unified Theory of Acceptance and Use of Technology Approach

2020 ◽  
Author(s):  
Ramllah . ◽  
Ahmad Nurkhin
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Learning Intention ◽  
Effort Expectancy ◽  
E Learning ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use ◽  
Descriptive Statistical Analysis

The purpose of this study isto analyze the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived creadibility, and anxiety on e-learning behavioral intention to use who are moderated by experience and voluntariness of use.The study population was 215 students who used e-learning in the Accounting Department of SMK N 1 Karanganyar. The sample selection using Slovin method with an error rate of 5% and sampling area technique obtained by respondents as many as 140 students. The technique of collecting data using a questionnaire. Data analysis techniques used descriptive statistical analysis and SEM-PLS. Data analysis tool using WarpPLS 5.0.The results of the descriptive statistical analysis show that the behavioral intention to use e-learning, performance expectancy, effort expectancy, social influence, facilitating conditions, perceived creativity, anxiety, experience and voluntariness of use are in the sufficient category. Hypothesis test results show the influence of performance expectancy on e-learning behavioral intention to use, effort expectancy does not affect the behavioral e-learning intention to use, social influence has an effect on behavioral e-learning intention to use, facilitating conditions have no effect on behavioral intention to Using e-learning, perceived creativity does not affect e-learning behavior, anxiety influences the behavioral intention to use e-learning, voluntary moderating negative social influences the behavioral e-learning intention to use, experience moderates the effect of effort expectancy on The behavior of e-learning intention to use, experience does not moderate the influence of social influence on the behavioral e-learning intention to use, experience does not moderate the effect of facilitating conditions on e-learning behavioral intention to use e-learning the conclusion of this study states that of the ten hypotheses proposed there are five types of hypotheses accepted. Keywords: E-learning, Behavioral Intention, UTAUT.

Understanding and Predicting Behavioral Intention to Adopt Mobile Banking

2019 ◽  
Vol 27 (3) ◽  
pp. 182-202 ◽  
Author(s):  
Ilyoo Barry Hong
Keyword(s):  
Social Influence ◽  
Influence Factors ◽  
Intention To Use ◽  
Mobile Banking ◽  
Personal Factors ◽  
Banking Services ◽  
Intention To Adopt ◽  
Research Findings ◽  
Survey Responses ◽  
Number Of Customers

Although mobile banking provides cost-saving opportunities as well as convenient banking experience for customers, today's banks still face challenges when deploying the technology because a good number of customers are reluctant to use mobile banking for personal reasons. This article is an empirical investigation of the determinants of the intention to use mobile banking services. The determinants are grouped into two categories including personal factors and social influence factors. The authors conducted an empirical analysis using 751 survey responses collected from present users of mobile banking services. The results of the analysis reveal that all the personal factors have positive relationships with the intention to use mobile banking services. On the other hand, it was found that of the social influence factors, perceived herding behavior has a significantly positive relationship with the intention to use mobile banking services, whereas subjective norm is not significantly related to the intention. The authors provide practical as well as academic implications of the research findings.

scholarly journals THE ANALYSIS ON FACTORS INFLUENCING THE USE OF MOBILE PAYMENT SYSTEM AMONG GENERATION Z IN BEKASI CITY

2021 ◽  
Vol 3 (9) ◽  
pp. 334-348
Author(s):  
Zxavian Zebadia Simorangkir ◽  
Kurnia Fajar Afgani
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Online Survey ◽  
Mobile Payment ◽  
Intention To Use ◽  
Mobile Payments ◽  
Generation Z ◽  
Perceived Security ◽  
Behavioral Intention To Use ◽  
The Impact

The topic of the factors that influence the usage of Mobile Payments is not to be overlooked, given the rise in the use of Mobile Payments in recent years. The goal of this study is to identify the factors that have a major impact on users' use of mobile payment instruments. The study was carried out with the help of an online survey questionnaire, which was distributed to 414 people. The impact of performance expectancy, effort expectancy, social influence, trust, and perceived security on the behavioral intention of mobile payment usage was investigated in this study. This study used a quantitative approach, with Generation Z as the unit of analysis, and the respondents from Bekasi as the target population. Two of the criteria have a significant relationship with the behavioral intention to use the mobile payment instrument, according to the research. Based on the result of this study, it can be seen that perceived security has the most influence on the behavioral intention to use mobile payment, followed by social influence.

scholarly journals Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey (Preprint)

2019 ◽  
Author(s):  
Yiyu Zhang ◽  
Chaoyuan Liu ◽  
Shuoming Luo ◽  
Yuting Xie ◽  
Fang Liu ◽  
...  
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Diabetes Management ◽  
Intention To Use ◽  
Unified Theory ◽  
Research Model ◽  
Web Based ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Facilitating Conditions

BACKGROUND Diabetes poses heavy social and economic burdens worldwide. Diabetes management apps show great potential for diabetes self-management. However, the adoption of diabetes management apps by diabetes patients is poor. The factors influencing patients’ intention to use these apps are unclear. Understanding the patients’ behavioral intention is necessary to support the development and promotion of diabetes app use. OBJECTIVE This study aimed to identify the determinants of patients’ intention to use diabetes management apps based on an integrated theoretical model. METHODS The hypotheses of our research model were developed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). From April 20 to May 20, 2019, adult patients with diabetes across China, who were familiar with diabetes management apps, were surveyed using the Web-based survey tool Sojump. Structural equation modeling was used to analyze the data. RESULTS A total of 746 participants who met the inclusion criteria completed the survey. The fitness indices suggested that the collected data fit well with the research model. The model explained 62.6% of the variance in performance expectancy and 57.1% of the variance in behavioral intention. Performance expectancy and social influence had the strongest total effects on behavioral intention (β=0.482; P=.001). Performance expectancy (β=0.482; P=.001), social influence (β=0.223; P=.003), facilitating conditions (β=0.17; P=.006), perceived disease threat (β=0.073; P=.005), and perceived privacy risk (β=–0.073; P=.012) had direct effects on behavioral intention. Additionally, social influence, effort expectancy, and facilitating conditions had indirect effects on behavioral intention that were mediated by performance expectancy. Social influence had the highest indirect effects among the three constructs (β=0.259; P=.001). CONCLUSIONS Performance expectancy and social influence are the most important determinants of the intention to use diabetes management apps. Health care technology companies should improve the usefulness of apps and carry out research to provide clinical evidence for the apps’ effectiveness, which will benefit the promotion of these apps. Facilitating conditions and perceived privacy risk also have an impact on behavioral intention. Therefore, it is necessary to improve facilitating conditions and provide solid privacy protection. Our study supports the use of UTAUT in explaining patients’ intention to use diabetes management apps. Context-related determinants should also be taken into consideration.

scholarly journals An Empirical Investigation into Examination of Factors Influencing University Students’ Behavior towards Elearning Acceptance Using SEM Approach

2020 ◽  
Vol 14 (02) ◽  
pp. 19 ◽  
Author(s):  
Barween Al Kurdi ◽  
Muhammad Alshurideh ◽  
Said A. Salloum ◽  
Zaid Mohammad Obeidat ◽  
Rami Mohammad Al-dweeri
Keyword(s):  
University Students ◽  
Social Influence ◽  
Behavioral Intention ◽  
Computer Anxiety ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Perceived Ease Of Use ◽  
E Learning ◽  
Behavioral Intention To Use

<p class="0abstract">There are several reasons why most of the universities implement E-learning. The extent of E-learning programs is being offered by the higher educational institutes in the UAE are evidently expanding. However, very few studies have been carried out to validate the process of how E-learning is being accepted and employed by university students. The study involved a sample of 365 university students. To describe the acceptance process, the Structural Equation Modeling (SEM) method was used. On the basis of the technology acceptance model (TAM), the standard structural model that involved E-learning Computer Self-Efficacy, Social Influence, Enjoyment, System Interactivity, Computer Anxiety, Technical support, Perceived Usefulness, Perceived Ease of Use, Attitude, and Behavioral Intention to Use e-learning, was developed. The findings showed that TAM served as a suitable theoretical tool to comprehend the acceptance of e-learning by users. The most critical construct to explain the causal process employed in the model was E-learning Computer Self-Efficacy, Social Influence, Enjoyment, System Interactivity, Computer Anxiety, Technical support, Perceived Usefulness, Perceived Ease of Use, Attitude, followed by Behavioral Intention to Use e-learning. Practical implications are offered by the outcomes for decision makers, professionals and developers in how effective E-learning systems can be implemented properly.</p>

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