OPENNESS TO EXPERIENCE AND ONLINE SHOPPING INTENTION: A META-ANALYSIS

Previous published research has shown a correlation between openness to experience and online shopping intention. However, there is no research that reveals how true r and size effect are from the correlation of the two variables. This meta-analysis aims to measure the correlation between openness to experience and online shopping intention by considering the effect size. The total sample of this research is 14747 people from 17 studies who are considered eligible. The findings of this meta-analysis show that openness to experience has a significant positive correlation with online shopping intention at a weak level with 95% CI (0.09; 0.33). Similar results were also found in the group of internet users (95% CI [0.10; 0.36]), and the group of college students (95% CI [-0.240; 0.320]). The heterogeneity test showed good results and there was no publication bias

Openness to experience - a moderator between social commerce success factors and customer satisfaction relationship: facebook brand page platform / Ariff Md. Ab. Malik, Hanitahaiza Hairuddin and Nurfaznim Shuib.

Nowadays, the role of social media in marketing strategies is undeniable. Facebook brand page is one of the platforms used by the marketers to promote their products. The purpose of this study is to investigate whether the Openness to Experience personality moderates the relationship between Information System Success (ISS) factors and customer satisfaction using a sample of 354 customers from three different Facebook brand pages. The result found that the Openness to Experience personality effect the relationship between ISS factors and customer satisfaction. Meanwhile, the Information Quality is the most important factor that influences the customer satisfaction towards social media applications

The Five Factor Model of Personality and HR Employees’ Perceptions of AI Adoption

The use of artificial intelligence (AI) tools to support Human Resources (HR) functions has recently gained influence and sparked controversy in both academic and applied settings. While studies on human-technology interaction have mainly focused on the response of humans to digital technologies in various contexts (e.g., instant messaging and social media), there remains a lack of empirical research on HR professionals’ individual perceptions of AI tools. This paper will utilize McCrae & Costa’s Big-Five Factor Model of Personality (1989) to develop five theoretical propositions about HR workers’ dispositional willingness to accept AI technology. It is proposed that while agreeableness, openness to experience, extraversion, and conscientiousness are positively related to AI acceptance among HR professionals, neuroticism is negatively related to acceptance of AI technology. I also describe directions for future research, along with considerations for HR departments that are interested in incorporating AI-based tools in their operations

Context Data Categories and Privacy Model for Mobile Data Collection Apps

Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user's personality. As filling out personality questionnaires is tedious, we propose the prediction of the user's personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user's different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user's personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications.Comment: Accepted for publication at the 15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2018

Smart Cities as Focal Entities for Strategic Communication - Considering the Public's Concerns Regarding the Use of Information and Communication Technology

This paper addresses the people’s knowledge, acceptance and attitude towards the concept of the Smart City. Therefore, inhabitants of Leipzig (Germany) and Tallinn (Estonia) were surveyed online and asked to evaluate 10 technologies that can be used in a Smart City and to rate the Smart City concept itself. First, results show significant differences in the level of knowledge and acceptance towards smart technologies between citizens of Leipzig and Tallinn. In addition, the data provides information on the extent to which citizens are willing to live in a Smart City and how they perceive its advantages. Second, the data provides information about perceived opportunities and risks towards the Smart City and thus gives information about which aspects should be addressed in future strategic communication in order to increase the people’s trust and acceptance

Curse or Blessing? Combining Personality Traits and Technology Acceptance to Investigate the Intention to Use of Digital Contact Tracing in Germany

In order to trace the transmission of COVID-19, digital contact tracing (DCT) provides an enormous value for the public health. However, the acceptance of the German contact tracing app, the Corona-Warn-App (CWA), falls short of the expected coverage in the general public. Accordingly, this study focuses on investigating the influencing factors on the CWA’s acceptance to demystify the missing puzzle and to face future pandemics. To assess this objective comprehensively, we investigate personality traits (guiding perception and behavior), subjective norm (expressing social influence), and trust in technology on acceptance variables. Our empirical results emphasize that besides the personality traits conscientiousness and agreeableness, perceived usefulness, subjective norm, and trust in technology play a vital role for engagement with the CWA. Our research offers starting points for the use of mobile health solutions, particularly in early epidemic stages

Understanding How Personality Affects the Acceptance of Technology: A Literature Review

The aim of this literature review is to summarize the current state of research on the influence of the extended Big Five personality traits on the acceptance of technology and to uncover inconsistencies and gaps in knowledge. It focuses on the question of how the characteristics openness to experience, extraversion, agreeableness, conscientiousness, neuroticism and willingness to take risks affect people's acceptance of new technologies. Within the framework of the literature review, a total of 378 topic-relevant results were analyzed and ultimately a sample of 22 studies selected to reflect the current state of research. Upon review, most of these studies provide significant results for each of the six personality traits. Furthermore, it was found that most researchers use the Technology Acceptance Model (TAM) to measure technology acceptance and that the samples consisted mainly of students. In view of the increasing use of intelligent technologies in almost all areas of life, it is particularly important to continuously investigate the factors influencing technology acceptance - and to do so in a representative way for all social classes

Modelling User Behaviour in Market Attribution: finding novel data features using machine learning

This paper presents an exploration of market attribution methods and the integration of user behaviour. Attribution is the measurement of interaction between marketing touchpoints and channels along the customer journey, improving customer insights and driving smarter business decisions. Improving the accuracy of attribution requires a deeper understanding of user behaviour, not just marketing channel credit assignment. Evidence has been provided regarding the problems in the standardized approach to behavioural modelling and alternatives have been presented. The study explores data provided by a British based jewellery company with an investigation into pre-existing data features that can aid with the analysis of user behaviour. The study contains over 10 million rows collected over 2 years and presents the initial findings made in the first 15 months of a PhD study