In the past few years, online impulse purchase has garnered attention from researchers in various fields, especially noted in the relatively new field of social commerce (S-commerce). This interactive envronment is a full of impulse buying stimulators. However, no previous studies have been conducted to evaluate the status of the research about online impulse buying behavior (IBB) in S-commerce. Hence, the authors reviewed studies published between 2005 and 2019, to gain an insight into IBB. The authors used an input-moderator-mediator-output model for identifying and classifying factors that influence consumer’s online IBB in S-commerce. The authors adopted a review protocol that involved two stages (i.e., automatic and manual), and identified 68 studies that addressed online IBB, of which 24 studies focused on IBB in S-commerce. The systematic review results indicated that survey-based studies (83%) and experiment methods (17%) were prevalent in online IBB in S-commerce. The authors also noted that a majority of existing studies adopted stimulus-organism-response. In this study, the factors that influence online IBB in S-commerce were classified, and a causal-chain framework for online impulse buying was developed. Finally, the authors made recommendations for future research in this field.

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

The implementation of usability in mobile augmented reality (MAR) learning applications has been utilized in a myriad of standards, methodologies, and techniques. The usage and combination of techniques within research approaches are important in determining the quality of usability data collection. The purpose of this study is to identify, study, and analyze existing usability metrics, methods, techniques, and areas in MAR learning. This study adapts systematic literature review techniques by utilizing research questions and Boolean search strings to identify prospective studies from six established databases that are related to the research context area. Seventy-two articles, consisting of 45 journals, 25 conference proceedings, and two book chapters, were selected through a systematic process. All articles underwent a rigorous selection protocol to ensure content quality according to formulated research questions. Post-synthesis and analysis, the output of this article discusses significant factors in usability-based MAR learning applications. This paper presents five identified gaps in the domain of study, modes of contributions, issues within usability metrics, technique approaches, and hybrid technique combinations. This paper concludes five recommendations based on identified gaps concealing potential of usability-based MAR learning research domains, varieties of unexplored research types, validation of emerging usability metrics, potential of performance metrics, and untapped correlational areas to be discovered.

Online Impulse Buying Behavior (IBB) significantly increases and contributes to the total S-commerce revenue. Hence, understanding such behavior is critical for an online business to strengthen competitiveness, enhance revenue, and ensure business sustainability. The majority of the prior research used urge-to-buy (UBI) to examine online IBB instead of the actual IBB. Hence, the studies that differentiate between UBI and IBB are limited. Moreover, these studies have concentrated on the impact of consumer characteristics, website, and marketing factors, but they neglected the role of social factors. This study integrated Social Influence and Uses and Gratifications Theories to explore the social factors affecting online IBB. The findings indicate that social influences, entertainment, purposive value, and maintaining interpersonal connectivity influence IBB significantly. UBI also mediates the relationships between social influences and IBB. Impulsiveness moderates the indirect relationships between compliance, internalization, and IBB through UBI. This study has practical implications for social commerce designers, marketers, and managers.

Many organisations fail to implement their strategies due to the inability of stakeholders in addressing the problems associated with strategy implementation. This paper aims to support their ability and involvement in solving these problems as well as measure the success of strategy implementation through the development of a knowledge management metrics model. This model comprised a set of 33 indicators, grouped into two metrics to measure the success of strategy implementation. The importance of the proposed indicators was theoretically discussed and justified through interview and survey conducted in the Malaysian higher education sector. In addition, a quantitative statistical analysis was performed to confirm this model. The overall results from the empirical evaluation were positive, thus indicating the appropriateness of this model. The crucial indicators were discussed so that managers can devote more attention to these elements. Furthermore, the key actors that influence the success of strategy implementation were also identified. For Malaysian Higher Education Institutions (HEIs), the model can aid them to measure their strategy implementation and increase their education quality. It can guide the top and middle managers in making decisions to support the achievement of strategy implementation success.