Search results
1 – 8 of 8Behzad Gholampour, Alireza Noruzi, Alireza Elahi, David Barranco Gil and Sajad Gholampour
The purpose of this study is to draw a scientific map of the Grand Tours cycling as part of the growing research field in this field at the global level. This study also…
Abstract
Purpose
The purpose of this study is to draw a scientific map of the Grand Tours cycling as part of the growing research field in this field at the global level. This study also identifies the components of scientific production in this field along with their collaboration patterns.
Design/methodology/approach
With the aim of achieving a comprehensive and deep understanding of the studies related to the Grand Tour, this research aims to address the existing gaps and provide a comprehensive summary of these scholarly works. To achieve this goal, the authors used a systematic and scientometric combination method, analyzing studies from the past half century (1970–2022).
Findings
The research findings reveal that scientific studies related to cycling events exhibit a geographical concentration within the continent of Europe, surpassing other continents. Notably, Spain, the USA, the UK and Italy emerge as pioneers in this field of inquiry. The main themes identified in these studies encompass cycling, the Tour de France, performance, professional roa cycling, heart rate, endurance, doping and power output.
Practical implications
This research, along with other systematic studies, contributes to the existing literature in this field by providing both quantitative and qualitative data. Additionally, the study serves as a foundation for identifying active and influential countries, institutions and authors in this domain. Such insights are highly effective in establishing scientific focal points in this field.
Originality/value
This study, in conjunction with the introduction of key figures in mega cycling event research, sheds light on the thematic areas explored within these studies. Notably, it is the sole study that has illuminated hidden facets of this field using scientometric and systematic methods.
Details
Keywords
Behnam (Abdolreza) Oboudi, Alireza Elahi, Hossein Akbari Yazdi and Do Young Pyun
In recent years, neurophysiological tools have been vastly applied in sport marketing research. Eye tracking, a pervasive sensor technology, has received a growing interest to…
Abstract
Purpose
In recent years, neurophysiological tools have been vastly applied in sport marketing research. Eye tracking, a pervasive sensor technology, has received a growing interest to examine the effects of advertising through sport on viewer attention. While there is a plethora of evidence in advertising that supports the positive effects of various advertising types and locations on viewer attention in various sport contexts, little is known about the role of a prosocial overlay ad on viewer attention when watching televised football matches. Therefore, this research aims to examine the differences in viewers' attention (i.e. fixation and duration) with regard to game attractiveness and colors of the prosocial message during televised football matches.
Design/methodology/approach
To identify the research gap, the authors first reviewed the relevant sport marketing and neuroscience research on advertising effectiveness. The authors selected a prosocial message displayed. Adopting an experimental research design and using eye tracking, this study examined the impacts of game attractiveness and colors of message on viewer attention to the prosocial message displayed on an overlay advertisement during a football match.
Findings
The authors found that the colors of prosocial messages and game attractiveness had significant effects on viewer attention to the prosocial message.
Originality/value
In this study, the authors sought to add advertisement color, as well as game attractiveness, to the extant knowledge in marketing literature as effective advertising factors in capturing viewers' attention. These variables can offer marketers new insights in designing effective advertisements for the context of televised sports events in a specialized field.
Details
Keywords
Vahid Delavari, Elahi Shaban, Marijn Janssen and Alireza Hassanzadeh
A large number of systematic reviews (SRs) studies have been performed in the cloud computing field, demonstrating miscellaneous outcomes and utilizing different approaches…
Abstract
Purpose
A large number of systematic reviews (SRs) studies have been performed in the cloud computing field, demonstrating miscellaneous outcomes and utilizing different approaches. Accordingly, a meta-review of cloud SRs is needed to appraise the results of such studies and create an integrated understanding. The paper aims to discuss these issues.
Design/methodology/approach
A tertiary study was conducted using a systematic method to analyze SRs including two stages: searching and screening the SRs and thematic synthesis of results. As a qualitative data management tool, Nvivo software was used to support the research process, for data coding and synthesis.
Findings
First, by searching electronic sources between the year of 2011–2016, out of a total of 142 identified articles, 94 articles were included according to pre-determined criteria, of which 76 articles were approved after qualitative evaluation. In the second stage, identifying the research themes, a map of the concepts and issues related to each theme was drawn up. The analysis shows that the quality of articles has improved but can be further enhanced using methodological guidelines as well as supporting tools. The research has focused more on the technical aspect, although there is an equal demand for synthesizing of cloud governance concepts.
Originality/value
This is the first tertiary study which presents the main research themes and concepts of cloud SRs in form of thematic maps by using the thematic synthesis and SR methods. This paper also provides some recommendations to improve reviews after evaluating the quality of papers. This study can support reviewers for future SRs in the field and also helps practitioners and managers to have a better understanding of different aspects of cloud computing.
Details
Keywords
Emad Kazemzadeh, Mohammad Taher Ahmadi Shadmehri, Taghi Ebrahimi Salari, Narges Salehnia and Alireza Pooya
The USA is one of the largest oil producers in the world. For this purpose, the authors model and predict the US conventional and unconventional oil production during the period…
Abstract
Purpose
The USA is one of the largest oil producers in the world. For this purpose, the authors model and predict the US conventional and unconventional oil production during the period 2000–2030.
Design/methodology/approach
In this research, the system dynamics (SD) model has been used. In this model, economic, technical, geopolitical, learning-by-doing and environmental (social costs of carbon) issues are considered.
Findings
The results of the simulation, after successfully passing the validation test, show that the US unconventional oil production rate under the optimistic scenario (high oil prices) in 2030 is about 12.62 million barrels/day (mb/day), under the medium oil price scenario is about 11.4 mb/day and under the pessimistic scenario (low oil price) is about 10.18 mb/day. The results of US conventional oil production forecasting under these three scenarios (high, medium and low oil prices) show oil production of 4.62, 4.26 and 3.91 mb/day, respectively.
Originality/value
The contribution of this study is important in several respects: First, by modeling SD that technical, economic, proven reserves and technology factors are considered, this paper models US conventional and unconventional oil production separately. In this modeling, nonlinear relationships and feedback loops are presented to better understand the relationships between variables. Second, given the importance of environmental issues, the modeling of social costs of CO2 emissions per barrel of oil is also presented and considered as a part of oil production costs. Third, conventional and unconventional US oil production by 2030 is forecast separately, the results of this study could help policymakers to develop unconventional oil and plan for energy self-sufficiency.
Details
Keywords
Asieh Bakhtiar, Seyed Sepehr Ghazinoory, Alireza Aslani and Vahid Mafi
The purpose of this paper is to present and evaluate the performance of innovation systems by considering two indicators of efficiency and effectiveness. The scope of the…
Abstract
Purpose
The purpose of this paper is to present and evaluate the performance of innovation systems by considering two indicators of efficiency and effectiveness. The scope of the evaluation is globally and due to the situation of each country, the suggested strategies are proposed to maintain the status quo or move toward the desired situation for countries.
Design/methodology/approach
The approach is to compare and benchmark the countries in terms of the efficiency and effectiveness of their innovation system. The Global Innovation Index report’s input-to-output ratio and the global competitiveness report are used for the assessment.
Findings
The findings indicate that countries such as China, Switzerland and the USA have an efficient and effective innovation system. However, the innovation systems in countries such as Brazil and Zimbabwe are not only inefficient but also ineffective. The findings also indicate that the innovation systems of countries such as Iran, Armenia and Egypt are efficient but ineffective. Finally, the authors can name Australia, Qatar and Russia as countries with effective but inefficient innovation systems.
Originality/value
Assessment of national innovation system using efficiency and effectiveness performances is done for the first time at the global stage.
Details
Keywords
Alireza Shokri, Seyed Mohammad Hossein Toliyat, Shanfeng Hu and Dimitra Skoumpopoulou
This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint…
Abstract
Purpose
This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC).
Design/methodology/approach
A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution.
Findings
Several major organisational, infrastructure and cultural obstacles were revealed, and an optimum scenario for the integration of spare part inventory management with PdM was recommended.
Practical implications
The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and addressing shortage of spare parts.
Originality/value
This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.
Details
Keywords
Bahareh Shafipour-Omrani, Alireza Rashidi Komijan, Seyed Jafar Sadjadi, Kaveh Khalili-Damghani and Vahidreza Ghezavati
One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day…
Abstract
Purpose
One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.
Design/methodology/approach
One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.
Findings
The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.
Originality/value
The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.
Details
Keywords
Samira Khodabandehlou, S. Alireza Hashemi Golpayegani and Mahmoud Zivari Rahman
Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity…
Abstract
Purpose
Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.
Design/methodology/approach
The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.
Findings
The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.
Research limitations/implications
The research data were limited to only one e-clothing store.
Practical implications
In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.
Originality/value
In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.
Details