Search results

This study investigated the moderating effect of organizational knowledge management performance on the sharing and use of information encountered by serendipity within the organization.

The authors surveyed 274 medical librarians from the top 100 medical schools.

Individual information encountering predicted information encountering at work, which, in turn, predicted organizational sharing of encountered information. When the propensity to encounter information was high, then organizational knowledge management performance moderated the effect between organizational encountering and organizational sharing of information. Encountered information at work was only present when high organizational knowledge management performance was in place.

This finding helps information behavior researchers discover the transfer of behaviors from everyday life to organizational environments.

It shows the need for greater support for information encounterers at work and the role of knowledge management, which may enhance their contribution to the organizational objectives.

Information encountering involves finding information by chance. Studies on information encountering have not focused on work settings and if the individual propensity to encounter information translates to organizational settings. Also, the relationship between information encountering and organizational knowledge management has not been studied so far.

This study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation.

The author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians.

The author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI.

The sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference.

The results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns.

This study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.

Among the proposed radio access strategies for improving system execution in 5G networks, the non-orthogonal multiple access (NOMA) scheme is the prominent one.

Among the most fundamental NOMA methods, power-domain NOMA is the one where at the transmitter, superposition coding is used, and at the receiver, successive interference cancellation (SIC) is used. The importance of power allocation (PA) in achieving appreciable SIC and high system throughput cannot be overstated.

This research focuses on an outage probability analysis of NOMA downlink system under various channel conditions like Rayleigh, Rician and Nakagami-m fading channel. The system design's objectives, techniques and constraints for NOMA-based 5G networks' PA strategies are comprehensively studied.

From the results of this study, it is found that the outage probability performance of downlink ordered NOMA under Rayleigh, Rician and Nakagami-m fading channel was good.

Outage probability analysis of downlink ordered NOMA under various channel conditions like Rayleigh, Rician and Nakagami-m fading channels were employed. Though the performance of Nakagami-m fading channel is lesser compared to Rayleigh channel, the performance for user 1 and user 2 are good.

Although sustainability is a popular topic in the past decade, there is a lack of research to identify the driving factors for developing countries. The purpose of this paper is to investigate the driving factors for achieving eco-innovation.

An in-depth case study is employed to address the objective outlined above. A Chinese company with more than 1,200 employees was selected to address the research question.

By fuzzy decision-making trial and evaluation laboratory and interpretive structural modelling analysis, the driving factors for eco-innovation are identified, and the priority of different factors has also been extracted.

This is among the first studies to carry similar analysis regarding eco-innovation. More specifically, this is perhaps the first study to take this approach and to analyse this topic in a developing country.

This paper aims to investigate the economic, political or sociocultural determinants of corruption in Tunisia.

To better understand the main determinants of corruption in Tunisia. This study uses The Bayesian Model Averaging (BMA) model, which allows us to include a large number of explanatory variables and for a shorter period.

The results show that economic freedom is the most important variable of corruption in Tunisia. In second place comes the subsidies granted by the government, which is one of the best shelters of corruption in Tunisia through their use for purposes different from those already allocated to them. Third, this paper finds the high unemployment rate, which, in turn, is getting worse even nowadays. The other three factors considered as causal but of lesser importance are public expenditures, the human development index (HDI) and education. Education, the HDI and the unemployment rate are all socio-economic factors that promote corruption.

The realization of this study will lead to triple net contributions. The first is to introduce explicitly and simultaneously political, social and economic determinants of corruption in developing countries. Second, unlike previous studies based on the simple and generalized regression model, the present research uses another novel and highly developed estimation method. More precisely, this study uses the BMA model, on the set of annual data for a period of 1998–2018. The third contribution of this research resides in the choice of the sample.