Thought Process of a New Graduate Which Leads to Behavioral Intention to Apply for a Job Vacancy

2018 ◽  
pp. 247-273
Author(s):  
Shan Anjana Jayasinghe ◽  
Galagedarage Dinesh Samarasinghe ◽  
Theekshana Suraweea
Keyword(s):  
Behavioral Intention ◽  
Perceived Value ◽  
Thought Process ◽  
Value Proposition ◽  
New Graduates ◽  
Use Of Technology ◽  
New Graduate ◽  
Job Vacancy ◽  
And Gender ◽  
Acceptance Model

Due to inadequacy, there is a call for more research on the thought process of job seekers. This chapter argues that employers have to communicate their company's job-seeker value proposition to new graduates to create job-seeker perceived value in their minds. Job-seeker perceived value will lead to behavioral intention. Further, the chapter proposes that a company's job-seeker value proposition has a direct positive relationship with behavioral intention and the relationship is moderated positively by voluntariness and gender. The technology acceptance model and the unified theory of acceptance and use of technology (UTAUT) were used to develop arguments related to each relationship proposed in the conceptual framework. Marketing management literature was used to label both constructs: company's job-seeker value proposition and job-seeker perceived value; and the same is employed to complement the arguments borrowed from management information system. Theoretical contribution, practical contribution, limitations, and opportunities for future research are also discussed in the chapter.

Computer Skills, Technostress, and Gender in Higher Education

2011 ◽  
pp. 122-128
Author(s):  
Sonya S. Gaither Shepherd
Keyword(s):  
Higher Education ◽  
Technology Acceptance ◽  
Computer Software ◽  
Social Consequences ◽  
Use Of Technology ◽  
The People ◽  
Software And Hardware ◽  
And Gender ◽  
Acceptance Model ◽  
The Way

The creation of computer software and hardware, telecommunications, databases, and the Internet has affected society as a whole, and particularly higher education by giving people new productivity options and changing the way they work (Hulbert, 1998). In the so-called “information age” the increasing use of technology has become the driving force in the way people work, learn, and play (Drake, 2000). As this force evolves, the people using technology change also (Nelson, 1990). Adapting to technology is not simple. Some people tend to embrace change while others resist change (Wolski & Jackson, 1999). Before making a decision on whether to embrace technology or not, people may look at the practical and social consequences of accepting change. Therefore, the technology acceptance model, the accepting or resisting of technology is considered to be a form of reasoned behavior (Wolski & Jackson, 1999).

scholarly journals Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study (Preprint)

2020 ◽  
Author(s):  
Yanxiang Yang ◽  
Joerg Koenigstorfer
Keyword(s):  
Physical Activity ◽  
Behavioral Intention ◽  
Unified Theory ◽  
Physically Active ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
And Gender ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

BACKGROUND Smartphone fitness apps are considered promising tools for promoting physical activity and health. However, it is unclear which user-perceived factors and app features encourage users to download apps with the intention of being physically active. OBJECTIVE Building on the second version of the Unified Theory of Acceptance and Use of Technology, this study aims to examine the association of the seven determinants of the second version of the Unified Theory of Acceptance and Use of Technology with the app usage intentions of the individuals and their behavioral intentions of being physically active as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on these intentions. METHODS Data from 839 US residents who reported having used at least one smartphone fitness app were collected via a web-based survey. A confirmatory factor analysis was performed, and path modeling was used to test the hypotheses and explore the influence of moderators on structural relationships. RESULTS The determinants explain 76% of the variance in the behavioral intention to use fitness apps. Habit (<i>β</i>=.42; <i>P</i>&lt;.001), performance expectancy (<i>β</i>=.36; <i>P</i>&lt;.001), facilitating conditions (<i>β</i>=.15; <i>P</i>&lt;.001), price value (<i>β</i>=.13; <i>P</i>&lt;.001), and effort expectancy (<i>β</i>=.09; <i>P</i>=.04) were positively related to behavioral intention to use fitness apps, whereas social influence and hedonic motivation were nonsignificant predictors. Behavioral intentions to use fitness apps were positively related to intentions of being physically active (<i>β</i>=.12; <i>P</i>&lt;.001; <i>R<sup>2</sup></i>=0.02). Education-related app features moderated the association between performance expectancy and habit and app usage intentions; motivation-related features moderated the association of performance expectancy, facilitating conditions, and habit with usage intentions; and gamification-related features moderated the association between hedonic motivation and usage intentions. Age moderated the association between effort expectancy and usage intentions, and gender moderated the association between performance expectancy and habit and usage intentions. User experience was a nonsignificant moderator. Follow-up tests were used to describe the nature of significant interaction effects. CONCLUSIONS This study identifies the drivers of the use of fitness apps. Smartphone app features should be designed to increase the likelihood of app usage, and hence physical activity, by supporting users in achieving their goals and facilitating habit formation. Target group–specific preferences for education-, motivation-, and gamification-related app features, as well as age and gender differences, should be considered. Performance expectancy had a high predictive power for intended usage for male (vs female) users who appreciated motivation-related features. Thus, apps targeting these user groups should focus on goal achievement–related features (eg, goal setting and monitoring). Future research could examine the mechanisms of these moderation effects and their long-term influence on physical activity.

scholarly journals Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

10.2196/26063 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26063
Author(s):  
Yanxiang Yang ◽  
Joerg Koenigstorfer
Keyword(s):  
Physical Activity ◽  
Behavioral Intention ◽  
Unified Theory ◽  
Physically Active ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
And Gender ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

Background Smartphone fitness apps are considered promising tools for promoting physical activity and health. However, it is unclear which user-perceived factors and app features encourage users to download apps with the intention of being physically active. Objective Building on the second version of the Unified Theory of Acceptance and Use of Technology, this study aims to examine the association of the seven determinants of the second version of the Unified Theory of Acceptance and Use of Technology with the app usage intentions of the individuals and their behavioral intentions of being physically active as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on these intentions. Methods Data from 839 US residents who reported having used at least one smartphone fitness app were collected via a web-based survey. A confirmatory factor analysis was performed, and path modeling was used to test the hypotheses and explore the influence of moderators on structural relationships. Results The determinants explain 76% of the variance in the behavioral intention to use fitness apps. Habit (β=.42; P<.001), performance expectancy (β=.36; P<.001), facilitating conditions (β=.15; P<.001), price value (β=.13; P<.001), and effort expectancy (β=.09; P=.04) were positively related to behavioral intention to use fitness apps, whereas social influence and hedonic motivation were nonsignificant predictors. Behavioral intentions to use fitness apps were positively related to intentions of being physically active (β=.12; P<.001; R2=0.02). Education-related app features moderated the association between performance expectancy and habit and app usage intentions; motivation-related features moderated the association of performance expectancy, facilitating conditions, and habit with usage intentions; and gamification-related features moderated the association between hedonic motivation and usage intentions. Age moderated the association between effort expectancy and usage intentions, and gender moderated the association between performance expectancy and habit and usage intentions. User experience was a nonsignificant moderator. Follow-up tests were used to describe the nature of significant interaction effects. Conclusions This study identifies the drivers of the use of fitness apps. Smartphone app features should be designed to increase the likelihood of app usage, and hence physical activity, by supporting users in achieving their goals and facilitating habit formation. Target group–specific preferences for education-, motivation-, and gamification-related app features, as well as age and gender differences, should be considered. Performance expectancy had a high predictive power for intended usage for male (vs female) users who appreciated motivation-related features. Thus, apps targeting these user groups should focus on goal achievement–related features (eg, goal setting and monitoring). Future research could examine the mechanisms of these moderation effects and their long-term influence on physical activity.

scholarly journals Factors Affecting the Acceptance of Online Learning among the Urban Poor: A Case Study of Malaysia

2021 ◽  
Vol 13 (18) ◽  
pp. 10359
Author(s):  
Nornadia Azhar ◽  
Wan Fatimah Wan Ahmad ◽  
Rohiza Ahmad ◽  
Zainab Abu Bakar
Keyword(s):  
Online Learning ◽  
Urban Poor ◽  
Online Survey ◽  
Perceived Value ◽  
Factors Affecting ◽  
Knowledge And Skills ◽  
Use Of Technology ◽  
Klang Valley ◽  
Acceptance Model ◽  
Facilitating Conditions

Education is an avenue of gaining knowledge and skills that will, in turn, support economic growth. Therefore, as online learning platforms are abundantly available in Malaysia, the urban poor in the Klang Valley have a means of improving their knowledge and skills to generate more income. However, online learning requires technical readiness. As such, this study examined the factors affecting the acceptance of online learning among the urban poor in Malaysia. An acceptance model was developed by integrating the constructs of the unified theory of acceptance and use of technology (UTAUT) with factors that influence the perceived value of online learning among the urban poor. To assess the reliability of these factors, 293 respondents participated in an online survey. SmartPLS 3.0 was then used to analyse the collected data. We found that performance expectancy (PE), facilitating conditions (FC), and perceived value (PV) positively influenced the behavioural intention to use (BI) online learning. Therefore, the findings of this study provide an understanding of the value and vital factors that impact the acceptance of online learning among the urban poor in the Klang Valley.

Physicians' Non-Use of Technology

2010 ◽  
Vol 2 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Jon Blue
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Behavioral Intention ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Perceived Ease Of Use ◽  
Use Of Technology ◽  
Acceptance Model ◽  
Behavioral Intention To Use

Technology Acceptance Theories have been widely applied and quite successful at explaining the behavioral intention to use technology in many organizations. One of the most significant variables in the Technology Acceptance Model is perceived ease of use. The Technology Acceptance Model purports that perceived ease of use contributes to the behavioral intention to use technology. Additionally, the model purports that perceived ease of use is an antecedent of perceived usefulness. In the adoption and use of technology across multiple industries, previous studies show that Technology Acceptance Theories predictions have been incorrect and not consistently supported. Qualitatively studying physicians, it is shown that medical doctors focus on factors not thoroughly investigated or explained in the literature. This study specifically investigates physicians’ behavioral intention to use a Personal Digital Assistant in their work environment and examines why some tested constructs, such as perceived ease of use and perceived usefulness, are not relevant in a health care environment. Plausible solutions to this non-predictability issue, such as technology substitution, are presented.

An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age

2016 ◽  
Vol 28 (8) ◽  
pp. 1535-1553 ◽  
Author(s):  
Jungsun (Sunny) Kim
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Behavioral Intention ◽  
Ease Of Use ◽  
Equation Modeling ◽  
Age And Gender ◽  
Content Type ◽  
And Gender ◽  
Tablet Apps ◽  
Acceptance Model

Purpose This paper aims to examine whether a customer’s perceptions of hotel tablet apps serve as determinants of customers’ behavioral intention in terms of the app’s ease of use, usefulness, credibility and subjective norm. It also explored age and gender as moderators of the relationships between these determinants and customers’ behavioral intention as well as customers’ likelihood of using specific app functions across age and gender. Design/methodology/approach A research model, grounded in the technology acceptance model (TAM), used data collected from 751 hotel customers in the USA. The model was tested using confirmatory factor analysis and structural equation modeling. Findings The results showed that three of the four proposed determinants positively influenced customers’ behavioral intention toward hotel tablet apps. Neither gender nor age played significant moderating roles in the relationships between the four determinants and the behavioral intention. The study also revealed age- and gender-related differences in preferences for specific hotel tablet app functions. Practical implications This study helps operators successfully plan for investing in and implementing hotel apps. It assists operators in developing effective marketing strategies by understanding factors influencing customers’ app adoption and between group differences in their preferences on app functions. Originality/value This is the first tablet app adoption study that extends TAM to the hotel industry. Thus, it extends the literature on technology adoption by exploring both existing and new variables and testing them in a new context.

Computer Skills, Technostress, and Gender in Higher Education

2011 ◽  
pp. 3011-3019
Author(s):  
Sonya S. Gaither Shepherd
Keyword(s):  
Higher Education ◽  
Technology Acceptance ◽  
Computer Software ◽  
Social Consequences ◽  
Use Of Technology ◽  
The People ◽  
Software And Hardware ◽  
And Gender ◽  
Acceptance Model ◽  
The Way

The creation of computer software and hardware, telecommunications, databases, and the Internet has affected society as a whole, and particularly higher education by giving people new productivity options and changing the way they work (Hulbert, 1998). In the so-called “information age” the increasing use of technology has become the driving force in the way people work, learn, and play (Drake, 2000). As this force evolves, the people using technology change also (Nelson, 1990). Adapting to technology is not simple. Some people tend to embrace change while others resist change (Wolski & Jackson, 1999). Before making a decision on whether to embrace technology or not, people may look at the practical and social consequences of accepting change. Therefore, the technology acceptance model, the accepting or resisting of technology is considered to be a form of reasoned behavior (Wolski & Jackson, 1999).

scholarly journals User acceptance model of computer-based assessment: moderating effect and intention-behavior effect

2019 ◽  
Vol 35 (1) ◽  
Author(s):  
Jian-Wei Lin ◽  
Yung-Cheng Lai
Keyword(s):  
Behavioral Intention ◽  
Self Regulation ◽  
User Acceptance ◽  
Moderating Effects ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
E Learning ◽  
Computer Based ◽  
Acceptance Model ◽  
Use Behavior

Computer-based assessment (CBA) is an important area of e-learning research. Most studies of CBA technology add new constructs to existing user acceptance models and rarely consider the moderating effects and intention-behavior effects. Notably, within an e-learning environment, the self-regulation (SR) levels (i.e. high or low) substantially influence individual learning behavior and performance while intention-behavior effects are not guaranteed. This study investigates the moderating effects of SR levels on the relationships between the factors (including the intention-behavior relationship) of CBA acceptance model, which was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The main findings are that, firstly, both the perceived performance expectancy and social influence significantly affect CBA behavioral intention in all students regardless of their SR level. Secondly, compared to effort expectancy in high-SR students, effort expectancy in low-SR students has a significantly larger effect on CBA behavioral intention. Finally, the behavioral intention significantly predicts CBA use behavior for the high-SR students but not for the low-SR students.

A Study on Factors Affecting on Customer Intention to Use the Express Bus

2018 ◽  
Vol 34 (5E) ◽  
Author(s):  
Phuong Nguyen Van ◽  
Hieu Trung Nguyen ◽  
Toan Bao Le
Keyword(s):  
Local Government ◽  
Service Quality ◽  
Behavioral Intention ◽  
Structural Equation ◽  
Perceived Value ◽  
Equation Model ◽  
Corporate Image ◽  
Organizational Image ◽  
Factors Affecting ◽  
Managerial Implications

The improvement of transit service quality is an essential role in developing urban and interstate transportation. The local government, as well as bus service companies, should understand the behavioral intention of passengers to meet their expectation and requirements. This paper aims to highlight such behavioral decision and investigate dominant factors that influence the customers’ decision to use the express bus. The study explores the case of express bus companies in Tay Ninh province, Vietnam. By using the structural equation model approach to analyze the data collected from 295 passengers, who have experienced in using the express bus, the results reveal that service quality has a significantly positive relationship with both perceived value and corporate image. Specifically, customer satisfaction and organizational image also positively influence behavioral intention. Meanwhile, service quality indirectly affects on behavioral intention throughout perceived value and corporate image. Based on the findings, we provided some insightful managerial implications and recommendations to managers of bus carriers, and valuable practical suggestions to policymakers in the local government of Tay Ninh province to improve the service quality to encourage more citizens to use the express bus.

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