Antecedents and Outcomes of the Flow Experience: An Empirical Study in the Context of Online Gaming

This study uses flow theory and the technology acceptance model (TAM) to provide new insight into the impact of enjoyment, one important dimension of flow, on user satisfaction, user beliefs, and behavioral intention to use. In addition, based on the propositions that knowledge results in an increased ability for activity and that flow is an emotional state of activity, this paper adopts a process view of knowledge to examine the role of knowledge in predicting enjoyment. The foregoing concepts are represented in a nomological network of enjoyment. Associated hypotheses are tested by using questionnaire responses of 253 online game players

THEORIZING A TWO-SIDED ADOPTION MODEL FOR MOBILE MARKETING PLATFORMS

From a two-sided market perspective, this paper is aimed at proposing a conceptual model for analyzing user adoption behaviors towards mobile marketing platforms. Both the consumer side and the merchant side of the platforms are modeled based on extending classical theories with newly introduced factors reflecting cross network effects, and the two sides are integrated in the overall model which reveals the dynamic interaction between the evolution processes of the two user groups through the platform. An experimental investigation and a survey study are conducted to test the consumer side and the merchant side of the model, respectively, both using the structural equation modeling (SEM) method for statistic analysis. Results from the empirical tests demonstrate that the two-sided perspective is promising for interpreting the adoption and evolution mechanisms of mobile marketing platforms. The proposed model extends the current research theme of information systems adoption to a more comprehensive viewpoint of two-sided markets, while contributes to the literature of two-sided market theories by introducing behavioral considerations

IT Based Knowledge Sharing and Organizational Trust: The Development and Initial Test of a Comprehensive Model

Knowledge has been recognized as an important asset for organizations to gain competitive advantage. Increasingly capable Information and Communication Technologies (ICT) and Information Systems (IS) have been developed and employed by organizations to facilitate Knowledge Management (KM). Beside outcomes, organizations are concerned with how to motivate employees to share their knowledge in order to obtain valuable inputs (i.e. knowledge), facilitate KM processes and get the greatest benefits from the investments. This paper aims to: (1) develop a comprehensive research model for studying the behavior of using KM systems to share knowledge in a socio-technical context, and (2) study the effect of Organizational Trust (OT) within this KM context. Literature review and survey were conducted to provide supportive results

Impact of Informational Social Support and Familiarity on Social Commerce Intention

Due to the increased popularity of social networking sites, a new platform called social commerce has emerged. Social commerce facilitates online interactions and user contributions to assist them in conducting commercial transactions. In this paper, we explore and identify factors that affect the intention to adopt social commerce. This study develops a comprehensive social commerce framework that has five key variables: Reviews and recommendations on social networking sites, customer ratings on social networking sites, trust on social networking sites, brand familiarity, and social commerce platform familiarity. Data were obtained from a survey of 310 consumers and were analyzed using Partial Least Squares PLS. The results indicate that reviews and recommendations on social networking sites, customer ratings on social networking sites, trust on social networking sites, and brand familiarity have a positive and direct influence on social commerce intention, while social commerce platform familiarity is not significant. This study contributes to consumer behavior theory by applying predictors of intention to social commerce for traditional e-commerce sites. The results also help e-commerce practitioners to improve their use of social tools

Impact of Informational Social Support and Familiarity on Social Commerce Intention

Due to the increased popularity of social networking sites, a new platform called social commerce has emerged. Social commerce facilitates online interactions and user contributions to assist them in conducting commercial transactions. In this paper, we explore and identify factors that affect the intention to adopt social commerce. This study develops a comprehensive social commerce framework that has five key variables: Reviews and recommendations on social networking sites, customer ratings on social networking sites, trust on social networking sites, brand familiarity, and social commerce platform familiarity. Data were obtained from a survey of 310 consumers and were analyzed using Partial Least Squares PLS. The results indicate that reviews and recommendations on social networking sites, customer ratings on social networking sites, trust on social networking sites, and brand familiarity have a positive and direct influence on social commerce intention, while social commerce platform familiarity is not significant. This study contributes to consumer behavior theory by applying predictors of intention to social commerce for traditional e-commerce sites. The results also help e-commerce practitioners to improve their use of social tools

Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques

Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19,175 observations, covering an entire livestreaming subsector. By comparing the forecasting accuracy of eight machine learning models, we identify a random forest model that provides the best prediction of gross merchandise volume (GMV). Furthermore, we utilize explainable artificial intelligence to open the black-box of machine learning model, discovering four new facts: 1) variables representing the popularity of livestreaming events are crucial features in predicting GMV. And voice attributes are more important than appearance; 2) popularity is a major determinant of sales for female hosts, while vocal aesthetics is more decisive for their male counterparts; 3) merits and drawbacks of the voice are not equally valued in the livestreaming market; 4) based on changes of comments, page views and likes, sales growth can be divided into three stages. Finally, we innovatively propose a 3D-SHAP diagram that demonstrates the relationship between predicting feature importance, target variable, and its predictors. This diagram identifies bottlenecks for both beginner and top livestreamers, providing insights into ways to optimize their sales performance.Comment: 25 pages, 10 figure

Curricular orientations to real-world contexts in mathematics

A common claim about mathematics education is that it should equip students to use mathematics in the ‘real world’. In this paper, we examine how relationships between mathematics education and the real world are materialised in the curriculum across a sample of eleven jurisdictions. In particular, we address the orientation of the curriculum towards application of mathematics, the ways that real-world contexts are positioned within the curriculum content, the ways in which different groups of students are expected to engage with real-world contexts, and the extent to which high-stakes assessments include real-world problem solving. The analysis reveals variation across jurisdictions and some lack of coherence between official orientations towards use of mathematics in the real world and the ways that this is materialised in the organisation of the content for students

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

An Intensive Spectrum for Intention Mining Analysis

There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining

Web Acceptance and Usage Model: A Comparison between Goal-directed and Experiential Web Users

In this paper we analyse the Web acceptance and usage between goal-directed users and experiential users, incorporating intrinsic motives to improve the particular and explanatory TAM value –traditionally related to extrinsic motives-. A field study was conducted to validate measures used to operationalize model variables and to test the hypothesised network of relationships. The data analysis method used was Partial Least Squares (PLS).The empirical results provided strong support for the hypotheses, highlighting the roles of flow, ease of use and usefulness in determining the actual use of the Web among experiential and goal-directed users. In contrast with previous research that suggests that flow would be more likely to occur during experiential activities than goal-directed activities, we found clear evidence of flow for goal-directed activities. In particular the study findings indicate that flow might play a powerfulrole in determining the attitude towards usage,intention to useand, in turn,actual Web use among experiential and goal-directed users