Search results

In this paper, we propose and test a conceptual model of how the psychological constructs of immersion, cognitive load (CL) and perceived autonomy (PA) relate to the performance of users at cybersecurity games.

We test the proposed model through an experimental study of 208 participants who play a cybersecurity game and answer a survey. We use structural equation modeling to test our hypotheses and further compute Shapley values to assess the contribution of each construct to game outcomes.

Results reveal that immersion is an important explanator of CL, PA and outcome. Additionally, our statistical tests show how the inclusion of CL in the conceptual model brings out the relation between autonomy and game-outcome.

This study combines constructs stemming from different theories to propose a conceptual model that better explains game performance. The findings will help enhance learner engagement and motivation. In turn, this can potentially add value to designing cost-effective training methods that can address the growing range of cybersecurity issues in enterprises.

The purpose of this paper is to determine how different innovation patterns affect the financial performance of global technological firms.

The authors integrate the theories of innovation performance with those of Schumpeter’s innovation patterns, namely, creative destruction and creative accumulation. Data spread over 20 years is used to investigate the influence of innovation on the firm performance.

Panel regression results indicate that, as compared to creative-destruction innovation, creative-accumulation patterns have a better firm performance, have a moderating effect on innovation-performance relationships, and have a better propensity to deal with difficult economic periods.

There is a scarcity of research that considers the effects of Schumpeterian patterns on innovation performance, especially ones dealing with the technology sector. Future work could consider other innovation variables (besides innovation patterns), as well as whether the results hold in other sectors.

The findings indicate that in the tech-sector firms must continue to innovate.

From the research perspective, the work integrates two streams of literature into a comprehensive model, and provides a holistic test for it. For tech-sector managers, the research provides one point of motivation for carrying out innovation even during a troubled economy.

This study aims to investigate the decisions related to business aspects of cloud computing and discuss the research density, models/techniques used and identify opportunities for future work.

In this paper, 155 research articles shortlisted through a systematic review were analyzed and a classification framework was developed. Using this framework, the research density is discussed and a detailed review of four widely researched decision themes is provided.

It was found that current research on business aspects is spread across 23 decision themes. The distribution, however, is skewed with 50 per cent pertaining to just four themes, namely, pricing, markets, sourcing and adoption. Simulation appears to be the preferred modeling approach. Decision themes in consumer behavior, sustainability, auditing and culture offer opportunities for future research.

The classification framework organizes extant research on applied models and allows researchers to identify potential avenues for application, improvement and development of models to support business decisions. The review is limited to academic articles and does not include industry reports.

Practitioners can readily understand various perspectives relevant to a decision theme such as pricing or sourcing, seek and use associated models such as simulation, optimization and game theory to support their decision-making.

Most of the extant review paper deal with cloud computing technology. This study is the first systematic review on the models applied to business aspects of cloud computing. This study provides a classification framework and explicitly lists associated decision themes, models/techniques and opportunities.

While the adoption of health-analytics (HA) is expanding, not every healthcare organization understands the factors impacting its readiness for HA. An assessment of HA-readiness helps guide organizational strategy and the realization of business value. Past research on HA has not included a comprehensive set of readiness-factors and assessment methods. This study’s objective is to design artifacts to assess the HA-readiness of hospitals.

The information-systems (IS) theory and methodology entail the iterative Elaborated Action Design Research (EADR)method, combined with cross-sectional field studies involving 14 healthcare organizations and 27 participants. The researchers determine factors and leverage multi-criteria decision-making techniques to assess HA-readiness.

The artifacts emerging from this research include: (1) a map of readiness factors, (2) multi-criteria decision-making techniques that assess the readiness levels on the factors, the varying levels of factor-importance and the inter-factor relationships and (3) an instantiated system. The in-situ evaluation shows how these artifacts can provide insights and strategic direction to an organization through collective knowledge from stakeholders.

This study finds new factors influencing HA-readiness, validates the well-known and details their industry-specific nuances. The methods used in this research yield a well-rounded HA readiness-assessment (HARA) approach and offer practical insights to hospitals.