Hyerim Cho, Minh T.N. Pham, Katherine N. Leonard and Alex C. Urban
With ready access to search engines and social media platforms, the way people find image information has evolved and diversified in the past two decades. The purpose of this…
Abstract
Purpose
With ready access to search engines and social media platforms, the way people find image information has evolved and diversified in the past two decades. The purpose of this paper is to provide an overview of the literature on image information needs and behaviors.
Design/methodology/approach
Following an eight-step procedure for conducting systematic literature reviews, the paper presents an analysis of peer-reviewed work on image information needs and behaviors, with publications ranging from the years 1997 to 2019.
Findings
Application of the inclusion criteria led to 69 peer-reviewed works. These works were synthesized according to the following categories: research methods, users targeted, image types, identified needs, search behaviors and search obstacles. The reviewed studies show that people seek and use images for multiple reasons, including entertainment, illustration, aesthetic appreciation, knowledge construction, engagement, inspiration and social interactions. The reviewed studies also report that common strategies for image searches include keyword searches with short queries, browsing, specialization and reformulation. Observed trends suggest common deployment of query analysis, survey questionnaires and undergraduate participant pools to research image information needs and behavior.
Originality/value
At this point, after more than two decades of image information needs research, a holistic systematic review of the literature was long overdue. The way users find image information has evolved and diversified due to technological developments in image retrieval. By synthesizing this burgeoning field into specific foci, this systematic literature review provides a foundation for future empirical investigation. With this foundation set, the paper then pinpoints key research gaps to investigate, particularly the influence of user expertise, a need for more diverse population samples, a dearth of qualitative data, new search features and information and visual literacies instruction.
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Pham Thi Bich Ngoc, Pham Thi Hoa Tien, Pham Dinh Long and Huynh Quoc Vu
The paper aims to investigate the difference in total factor productivity (TFP) among those firms with and without outsourcing in a developing country like Vietnam. Also, it…
Abstract
Purpose
The paper aims to investigate the difference in total factor productivity (TFP) among those firms with and without outsourcing in a developing country like Vietnam. Also, it explores the effect of outsourcing activities on total factor productivity with a specified concentration on the Vietnamese small and medium-sized enterprises (SMEs).
Design/methodology/approach
The panel data set of SMEs used in this study was originated from biannual surveys conducted under the collaboration between educational organizations and government agencies: Stockholm School of Economics (SSE), Department of Economics – the University of Copenhagen, the Institution of Labor Studies and Social Affairs (ILSSA) in the Ministry of Labor, Invalids and Social Affairs (MOLISA). In this study, the model is developed based on the production function in accordance with the model of Girma and Görg (2004). The firms’ TFP is the difference between the actual and the predicted output as with the approach by Levinsohn and Petrin (2003).
Findings
This study finds out that firms with outsourcing have higher total factor productivity than those without outsourcing activities. In addition, the more firms spend on outsourcing, the higher total factor productivity they can gain. Outsourcing to SMEs in a developing country can significantly increase its TFP by means of either maintaining core competencies or searching external resources in conducting some internal activities.
Originality/value
Although outsourcing has been widely applied by large firms, the research studying its impact on productivity at firm level is limited. Especially, this study can shed light on the impact for the case of SMEs in a developing economy.
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This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system…
Abstract
Purpose
This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations.
Design/methodology/approach
The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept.
Findings
Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique.
Originality/value
Authors confirm that this manuscript is original.
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Ho Pham Huy Anh and Cao Van Kien
The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power…
Abstract
Purpose
The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation.
Design/methodology/approach
Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results.
Findings
Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement.
Originality/value
This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.
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Nguyen Ngoc Son, Cao Van Kien and Ho Pham Huy Anh
This paper aims to propose an advanced tracking control of the uncertain nonlinear dynamic system using a novel hybrid fuzzy linear quadratic regulator…
Abstract
Purpose
This paper aims to propose an advanced tracking control of the uncertain nonlinear dynamic system using a novel hybrid fuzzy linear quadratic regulator (LQR)-proportional-integral-derivative (PID) sliding mode control (SMC) optimized by differential evolution (DE) algorithm.
Design/methodology/approach
First, a swing-up and balancing control is presented for an experimental uncertain nonlinear Pendubot system perturbed with friction. The DE-based optimal SMC scheme is used to optimally swing up the Pendubot system to the top equilibrium position. Then the novel hybrid fuzzy-based on LQR fusion function and PID controller optimized by DE algorithm is innovatively applied for balancing and control the position of the first link of the Pendubot in the down-right position with tracking sinusoidal signal reference.
Findings
Experimental results demonstrate the robustness and effectiveness of the proposed approach in balancing control for an uncertain nonlinear Pendubot system perturbed with internal friction.
Originality/value
This manuscript is an original research paper and has never been submitted to any other journal.
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Thanh Pham Thien Nguyen, Nga Thu Trinh and Son Nghiem
This study aims to investigate the relationships between loan growth, loan losses and net income after the 2008 global financial crisis. This study further conducts a comparative…
Abstract
Purpose
This study aims to investigate the relationships between loan growth, loan losses and net income after the 2008 global financial crisis. This study further conducts a comparative analysis by considering the period of COVID-19.
Design/methodology/approach
This study uses panel data models such as one-step system GMM, random effects, fixed effects and OLS, with a data set of 131 Chinese commercial banks from 2009 to 2020.
Findings
The study finds no significant relationship between loan growth and future loan losses. However, after adjusting loan loss by net interest income (NII-adjusted loan loss), the study reveals that loan growth in the subsequent year decreases if NII-adjusted loan loss increases. The study also demonstrates the positive effect of loan growth on net income as newly expanded loans are funded at similar costs but offered at a lower rate compared with existing loans. During COVID-19, loan growth and net income were higher than in previous years.
Originality/value
The findings suggest that Chinese banks can increase lending to support the economy without sacrificing loan quality, emphasizing the importance of maintaining and enhancing credit policies and practices. Chinese banks should also continue to refine their pricing strategies for loans and deposits. The findings also imply that China's policy responses to the impact of COVID-19 could serve as lessons for future policy decisions.
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Ho Pham Huy Anh and Nguyen Tien Dat
The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control…
Abstract
Purpose
The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control (SMC). Through this integration, a novel structure of GRNN is designed to enable online and. This structure is then combined with SMC to develop a stable adaptive controller for a class of nonlinear multivariable uncertain dynamic systems.
Design/methodology/approach
In this study, a new hybrid (SMC-GRNN) control method is innovatively developed.
Findings
A novel structure of GRNN is designed that can be learned online and then be integrated with the SMC to develop a stable adaptive controller for a class of nonlinear uncertain systems. Furthermore, Lyapunov stability theory is utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system. Eventually, two different numerical benchmark tests are employed to demonstrate the performance of the proposed controller.
Originality/value
A novel structure of GRNN is originally designed that can be learned online and then be integrated with the sliding mode SMC control to develop a stable adaptive controller for a class of nonlinear uncertain systems. Moreover, Lyapunov stability theory is innovatively utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system.
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Vu Hong Son Pham and Duy Hieu Pham
This study aims to optimize the construction site layout planning (CSLP) problem, with a focus on prefabricated projects. It proposes the use of the oMOAHA algorithm, an enhanced…
Abstract
Purpose
This study aims to optimize the construction site layout planning (CSLP) problem, with a focus on prefabricated projects. It proposes the use of the oMOAHA algorithm, an enhanced version of the multi-objective artificial hummingbird algorithm (MOAHA), to address challenges related to search space exploration and local optimization in CSLP.
Design/methodology/approach
The study integrates three techniques – opposition-based learning (OBL), quasi-opposition and quasi-reflection – into the initialization phase of the MOAHA algorithm, creating the oMOAHA variant. This model is applied to all three types of CSLP problems – pre-determined location, grid system and continuous space – to evaluate its effectiveness. Six objective functions (three related to cost, two to safety and one to tower crane efficiency) and four site-related constraints are considered through three case studies taken from previous research and one real project involving prefabricated steel structures.
Findings
The oMOAHA algorithm demonstrates superior performance compared to previous models, consistently outperforming traditional approaches in CSLP optimization for prefabricated projects. In the real case study, the proposed model exceeded the actual project plan by 28–43%, indicating its potential to significantly improve both solution quality and project outcomes.
Originality/value
This study is the first to apply an optimization model to all three types of CSLP problems – pre-determined location, grid system and continuous space – within a unified framework. The integration of advanced techniques into the MOAHA algorithm and the model’s successful application in a real prefabricated project underscore its high applicability and effectiveness in modern construction management.
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Tan Vo-Thanh, Huan Minh Nguyen, Triet Minh Nguyen, Danh Cong Pham and Hung Phuc Nguyen
This study aims to examine the relationships among fear of COVID-19, job stress, job commitment and intention to quit. It also investigates the boundary conditions of the impact…
Abstract
Purpose
This study aims to examine the relationships among fear of COVID-19, job stress, job commitment and intention to quit. It also investigates the boundary conditions of the impact of fear of COVID-19 on job stress and intention to quit, a research gap that has not been addressed yet.
Design/methodology/approach
This research focused on full-time frontline hotel employees who have been working in four- and five-star hotels in Ho Chi Minh City, Vietnam. A pilot test was performed before collecting formal data. The survey was conducted face-to-face on site so that any potential confusion could be clarified right away. 487 valid responses were analyzed using SPSS 28 and SmartPLS 4.
Findings
The majority of hypotheses were supported, with the results suggested that supervisor support contributes to reducing the tendency of hotel employees to quit their job and their job stress. Besides, government support is necessary to make staff feel secure during the pandemic.
Practical implications
This study contributes to pointing out central priorities in making decisions by hotel managers when facing crises. Managers need to focus more on measures to help their employees feel secure and should be available for guidance and feedback when nedeed. Additionally, supportive policies from the government, especially financial support, can provide hotel employees with resources they need to maintain a basic level of living in the face of a severe drop in their income. The study provides the hotel industry not only in Vietnam but also in countries with comparable settings with strategies to cope with unexpected events.
Originality/value
Research on intention to quit a job has mainly focused on a complex interplay of internal factors. However, the influence of fear of COVID-19 on intention to quit a job has not been studied in the context of Vietnamese hotel industry yet. During the COVID-19 pandemic, a number of hotels in Vietnam had to close due to a lack of visitors, which had a negative impact on human resources. Accordingly, fear, stress, commitment and intention to quit a job are the issues faced by staff.
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Thi Anh Ngoc Pham, Ho Huu Loc, Dung Duc Tran and Nguyen Hong Quan
The purpose of this paper is to investigate the input- and output-specific technical inefficiency of Vietnamese prawn-rice rotational crops (PRRC) and to identify the impacts of…
Abstract
Purpose
The purpose of this paper is to investigate the input- and output-specific technical inefficiency of Vietnamese prawn-rice rotational crops (PRRC) and to identify the impacts of the socio-economic characteristics of farmers and farms on these technical inefficiencies.
Design/methodology/approach
This study first used a Russell-type (input-output) directional distance function to estimate the input- and output-specific technical inefficiency. Second, it applied a bootstrap truncated regression to analyze the factors influencing these technical inefficiencies. Data were gathered through a survey among 94 farmers, from Ben Tre and Kien Giang provinces, the two popular PRRC areas in the Mekong Delta.
Findings
Results show that Vietnamese PRRC farmers could reduce the water surface area by 3%, the use of seedlings by 15%, labor by 16%, fertilizers by 26%, and the use of others by 24%, while simultaneously increasing the revenue of farming system by 57% relative to the variable returns to scale (VRS) frontier. Farmers with more years of experience are generally better in managing the use of seedlings and in improvement of revenue. Farmers in Kien Giang province are more efficient in achieving revenue of the PRRC farming system than farmers in Ben Tre province.
Research limitations/implications
Outcomes of this study are useful to identify strategies in minimizing the use of inputs while simultaneously maximizing PRRC production.
Originality/value
This paper relates to the comparison of two mostly different ecological zones, being the dominant production areas of PRRC, in which, Kien Giang represents the western part, while Ben Tre is in the eastern part of the Vietnam’s Mekong Delta. The findings not only expand the current understanding but also suggest various meaningful research questions regarding the development of Vietnamese PRRC under the impacts of climate change. The study also contributes to the literature on examining the input- and output-specific technical inefficiencies and influencing factors.