Tingru Cui

Information technology and open innovation

Advances in information technology (IT) have enabled firms to increasingly rely on open innovation. Although researchers and practitioners are interested in this phenomenon, there is a lack of theoretically driven research on how IT impacts organizational open innovation performance. Drawing on the strategic IT alignment perspective and related literature, we proposed a model to explain the performance of organizational open innovation; i.e., the alignment between IT strategies and the openness of open innovation strategies results in different outcomes for open innovation. Through the analysis of data from 225 firms in China, we found that the alignment between IT flexibility and breadth enhances innovation radicalness and innovation volume, whereas the alignment between IT integration and depth positively affects innovation volume only. Innovation volume and radicalness were found to enhance organizational performance in terms of sales growth. Our study contributes to the literature on open innovation and strategic alignment. Its findings also have important managerial implications for practitioners.

MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Mobile learning in massive open online course (MOOC) evidently differs from its traditional ways as it relies more on collaborations and becomes more fragmented. We present a cloud-based virtual learning environment (VLE) which can organize learners into a better teamwork context and customize micro learning resources in order to meet personal demands in real time. Particularly, a smart micro learning environment was built by a newly designed Software as a Service (SaaS), namely Micro Learning as a Service (MLaaS). It aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To personalize the micro learning, a dynamic learner model is constructed with regards to the internal and external factors that can affect learning experience and outcomes. Educational data mining (EDM) techniques are employed as the main method to understand learners’ behaviors and recognize learning resource features. A solution of learning path optimization is also proposed towards assembling a complete MOOC learning experience.

Micro Learning Adaptation in MOOC: A Software as a Service and a Personalized Learner Model

Micro learning is gradually becoming a common learning mode in massive open online course learning (MOOC). We illustrate a research strategy to formalize and customize micro learning resources in order to meet personal demands at the real time. This smart micro learning environment can be organized by a Software as a Service (SaaS) we newly designed, in which educational data mining technique is mainly employed to understand learners learning behaviors and recognize learning resource features in order to identify potential micro learning solutions. A learner model with regards to internal and external factors is also proposed for personalization in micro MOOC learning context.

Demystifying continuous participation in game applications at social networking sites: a social playfulness design perspective

Purpose Drawn from the social playfulness literature and the elaboration likelihood model, this study proposes and tests a research model to examine users’ continuous participation in SNS game applications. Design/methodology/approach A field survey with 133 subjects was conducted to test the research model. Findings Two identified design features, symbolic physicality and inherent sociability, are found to influence users’ perceived curiosity and perceived enjoyment toward playing SNS game applications. Perceived enjoyment is significantly associated with perceived curiosity and predicts users’ continuous participation of SNS game applications. We also observed a gender difference of social playfulness design on perceived curiosity. Research limitations/implications Use intention was used as a proxy for actual use behavior, since objective data on continuance behavior was not available. Additionally, the contributions of this study may be constrained by one single sample. Practical implications The findi...

Gamification artifacts and crowdsourcing participation: Examining the mediating role of intrinsic motivations

Abstract Participation of individual workers (i.e., solvers) is critical to the viability and success of crowdsourcing platforms. Past literature indicates that gamification artifacts, by intriguing solvers’ intrinsic motivations, can encourage solvers’ participation in crowdsourcing. Nevertheless, little research has systemically theorized how intrinsic motivations mediate the relationship between gamification artifacts and crowdsourcing participation. Based on the motivational affordance perspective and related literature, this study theorizes gamification artifacts i.e., point rewarding and feedback giving, and identifies four intrinsic motivations (i.e., self-presentation, self-efficacy, social bonds, and playfulness) in the context of crowdsourcing. It then hypothesizes the mediating effects of the four intrinsic motivations on the relationships between the two gamification artifacts and crowdsourcing participation. It tests the model using survey data from 295 solvers in a large crowdsourcing platform. Results show that self-presentation, self-efficacy and playfulness positively mediates the impacts of two gamification artifacts on solvers’ participation. Theoretical contributions and practical implications are discussed.

Ontological Learner Profile Identification for Cold Start Problem in Micro Learning Resources Delivery

Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.

Exploring ideation and implementation openness in open innovation projects: IT-enabled absorptive capacity perspective

Abstract Advancements in information technology (IT) have made organizational boundaries so porous, thereby resulting in a trend toward leveraging external knowledge for innovation. However, firms experience significant obstacles in drawing external knowledge. This study aims to disentangle the role of IT-enabled absorptive capacity in new product development performance. The research model was tested with survey data of open innovation projects from 152 firms. Our findings indicate that IT-enabled absorptive capability improves open innovation project performance in terms of new product innovativeness and product speed to market, but we do not detect a significant direct influence of openness on performance. Our findings also show that the interaction of IT-enabled absorptive capacity and ideation openness significantly amplifies new product innovativeness while the interaction of IT-enabled absorptive capacity and implementation openness accelerates the product speed to market. This study theoretically contributes by building an IT-enabled absorptive capacity theory in the open innovation context and uncovering the effects of two dimensions of openness. In practice, it offers managers strategies to successfully conduct open innovation projects in deploying effective ITs and leveraging various types of openness during the two phases.

Misalignment between business and IT strategic objectives in Saudi Arabia public sector organisations

Business-IT strategy misalignment is increasingly an important area of concern and interest in organisations around the world including Saudi Arabia (SA). Indeed, the SA government has launched the National Digital Transformation Strategy for 2030 to support all public-sector organisations to improve efficiency and performance. This research aimed to identify and analyse the factors that contribute to business/IT strategy misalignment in Saudi public-sector organisations. This research focused emerged from the need to better understand the business and IT models incorporated in the organisations Saudi Arabia to achieve high performance, quality of service (QoS) and return of investment (ROI). Using a qualitative study design that included semi-structured interviews with eight executive and managerial staff from five public-sector organisations in Saudi Arabia, this study found human, operational and IT system factors all have the potential to contribute to business-IT strategy misalignment. It also found the approaches to misalignment avoidance in Saudi public-sector organisations sometimes lack structure and consistency.

A Heuristic Approach for New-Item Cold Start Problem in Recommendation of Micro Open Education Resources

The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies.

Organizing online computation for adaptive micro open education resource recommendation

Our previous work, Micro Learning as a Service (MLaaS), aimed to deliver adaptive micro open education resources (OERs). However, relying solely on the offline computation, the recommendation lacks rationality and timeliness. It is also difficult to make the first recommendation to a new learner. In this paper we introduce the organization of the online computation of the MLaaS. It targets at solving the cold start problem due to the shortage of learner information and real-time updates of the learner-micro OER profile.