Search results

Through Social Capital, this paper seeks to introduce interactions as antecedents of Knowledge Management.

The paper briefly reviews the Social Capital and Knowledge Management literature. It investigates how SC impacts the KM cycle in the literature and then presents statistical evidence using a case study.

Social Capital can be considered as an appropriate infrastructure for Knowledge Management implementation.

Social Capital refers to corporate culture; hence it differs in different contexts. This might result in a slight limitation to generalizing the findings to other organization types.

Actors' position in a network, trust, norms, teamwork enhance interactions, and encourage cooperation and resource sharing. As a consequence, exchange of knowledge is facilitated and knowledge is shared, created, applied and saved.

The paper provides insights to different dimensions of Social Capital and the way it enhances the Knowledge Management cycle. It further clarifies the relationship by statistical evidence via introducing a case study.

Query-based summarization approaches might not be able to provide summaries compatible with the user’s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user. This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms. This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user’s information need. This approach allows the user to interactively specify the composition of a multi-document summary.

This approach exploits the user’s opinion in two stages. The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents. It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method. Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered.

The proposed approach receives feedback from the user in terms of favorable keywords and sentences. The feedback eventually improves the summary as the end. To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire. The quality of the final summary has been also evaluated from the user’s perspective and information redundancy. It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction.

The interactive summarization approach goes beyond the initial user’s query, while it includes the user’s preferred keywords/keyphrases and sentences through a systematic interaction. With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need. Such interaction allows the summarization system to achieve a comprehensive understanding of the user’s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.

The study aimed to provide insights on antecedent and outcome of green HRM at the organisational level and the outcome of green HRM at the individual level. It also sought to examine the mechanism through which green HRM would lead to employees’ positive outcome.

A quantitative study design using a two-study approach was employed to collect and analyse the data. For study 1, 206 hotels from Malaysia were included in analysis at the organisational level, while in study 2 at the individual level, 508 employees from different sectors provided insights through an online questionnaire. For both studies, partial least squares (PLS–SEM) was used to assess the research model.

All the proposed hypotheses were supported. Specifically, at the organisational level, organisational environmental culture is positively related to green HRM, and green HRM management positively associates with organisation's environmental performance. At the individual level, green HRM positively influences employees' job satisfaction, and meaningfulness through work is a strong mediator in this relationship.

This study is significant as it contributes to both theory and practice by providing fresh insights on green HRM and its antecedent and outcomes at two levels (organisational and individual) and across two economies (emerging and developed). It also sheds some light on the outcome of green HRM at the employee level which is an area that is still under-researched. By focusing on meaningfulness through work as an important factor, the study contributes to better understanding of green HRM and employees’ positive outcomes.

The primary goal of this review is to identify emerging themes in the cold supply chain (CSC) and their future research directions, methodologies, and theories.

The review looks at CSC related articles from Scopus database published in the years 2000–2020. Thereafter, bibliometric and co-citation analyses have been conducted to identify emerging themes, methodologies, and theoretical perspectives related to CSC management.

This study revealed a clear research gap in CSC literature with emerging themes relevant to diverse aspects. Primarily, the most prominent authors, methodologies, and theories were identified from bibliometric analysis. Next, we generated clusters to identify the insights of each cluster using co-citation analysis. Consequently, the significance of clusters concerning the number of articles, theoretical frameworks, methodologies, and themes was recognized. Finally, a few future research questions regarding emerging themes have been identified.

The importance of co-citation and bibliometric analyses in studying the evolution of research over a definite time is emphasized in this work. As per emerging themes, implementing digital technologies has increased the efficiency of traditional CSC and transformed it into digital CSC.

As per the authors' knowledge, this work is the first in literature to explore the significance of identifying emerging areas and future research directions in managing CSC through literature review based on bibliometric and co-citation analysis.

The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical.

The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE.

In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model.

It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

Established data revealed a relationship between obesity and increasing the risk of mortality and morbidity of chronic diseases. There are conflicting data regarding the association between adherence of dietary approach to stop hypertension (DASH) and obesity. Therefore, this study aims to investigate this relationship among a large sample of Iranian adults.

This cross-sectional study was performed by 10,693 individuals; 6750 individuals related to Yazd Health Study living in the urban area and 3943 individuals related to Shahedieh cohort study living in the suburb area. Dietary intake was evaluated by using a validated food frequency questionnaire. In all participants, anthropometric indices including body mass index were measured. The DASH score was considered using gender-specific quintiles of DASH items. To evaluate the relationship of DASH diet and obesity, multivariate logistic regression analysis was used.

By adjusting confounders, participants in highest quintiles of DASH diet were compared to the lowest have lower odds of obesity in suburb area (odds ratio [OR]: 0.78; 95% confidence interval [CI]: 0.63, 0.96), in urban (OR: 0.71; 95% CI: 0.52, 0.99) and in whole population of both studies (OR: 0.75; 95% CI: 0.63, 0.90). Besides, more compliance of women to this diet in urban (OR: 0.64; 95% CI: 0.48, 0.85) and population of both studies (OR: 0.77; 95% CI: 0.62, 0.96) were associated with reduced odds of central obesity.

Considering this study limitations, the following can be mentioned: in this cross-sectional study, the causal relationship between DASH diet and obesity could not be assessed. Consequently, further prospective studies are required in this area. Second, although a valid food frequency questionnaire was used, but there was a measurement error and an error in the classification of people participating in the study. Moreover, we cannot reject the possibility of residual confounding bias because unknown or unmeasured confounders may exist that affected our results. Finally, our participants with odds of obesity might have been advised to reduce their fat intake, which led them to alter their dietary habits. However, such possibility cannot be resolved in a cross-sectional study.

DASH dietary pattern could decrease odds of obesity in both urban and suburb area and central obesity in urban area only. Further prospective studies are needed for causal conclusion.

The purpose of this study is to develop a conceptual framework for defining the notion of “physical integration” regarding the Iranian bazaar as the main component in the structure of traditional Iranian cities. Applying this conceptual framework to the historical bazaars in the cities of Kerman and Shiraz, this study seeks to pave the way for restoring the physical integrity of such historical districts.

The research was conducted in two phases. First, there was a review of the theoretical background of physical integration followed by the analysis of the corresponding qualitative contents and the validation of the proposed conceptual framework as confirmed by 15 local experts. The second phase tested the validated framework in two case studies based on maps, historical documents and field observations.

The findings show that the physical integration of historical bazaars can be undertaken in morphological, visual-aesthetic and functional aspects. The proposed conceptual framework is capable of dealing with the different aspects of physical integration in historical districts on a meso-scale.

The theoretical implications of this study concerning the physical integration of traditional bazaars address urban design, urban planning and multi-disciplinary historical geography. The study also has practical implications for the integration of bazaars in historical urban regeneration projects via design guidelines.

This study emphasizes the importance of physical integration as a multi-dimensional concept, facilitating it to deal with the physical quality and the characteristics of historical districts, particularly bazaars. It also highlights the role of the Iranian bazaar as a unifying structure in the historical districts.

The purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap.

In this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented.

The computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison.

There are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future.

In this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.