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Exploratory search activities are ubiquitous in various information systems. Much potentially useful or even serendipitous information is discovered during the exploratory search process. Given its irreplaceable role in information systems, exploratory search has attracted growing attention from the information system community. Since few studies have methodically reviewed current publications, researchers and practitioners are unable to take full advantage of existing achievements, which, in turn, limits their progress in this field. Through a literature review, this study aims to recapitulate important research topics of exploratory search in information systems, providing a research landscape of exploratory search.

Automatic and manual searches were performed on seven reputable databases to collect relevant literature published between January 2005 and July 2023. The literature pool contains 146 primary studies on exploratory search in information system research.

This study recapitulated five important topics of exploratory search, namely, conceptual frameworks, theoretical frameworks, influencing factors, design features and evaluation metrics. Moreover, this review revealed research gaps in current studies and proposed a knowledge framework and a research agenda for future studies.

This study has important implications for beginners to quickly get a snapshot of exploratory search studies, for researchers to re-align current research or discover new interesting issues, and for practitioners to design information systems that support exploratory search.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This study aims to assess the effect of job insecurity, customer incivility and work–life imbalance on Lebanese bank employee workplace well-being (EWW), while investigating the moderating role that positive and negative affect might have.

Quantitative data was collected from 202 respondents and analyzed using structural equation modeling system through IBM SPSS and AMOS.

Results revealed that each of the independent variables has a negative, statistically significant effect on Lebanese bank EWW. The positive affect and the negative one are shown to have a moderating effect that lessens and boosts, respectively, these negative effects.

The study adds to the literature on EWW while highlighting the high-power distance and collectivist society that the research took place in.

Limitations include the sample size that was hoped to be larger, in addition to the self-reporting issue and what it entails in the data collection process.

The study has many practical implications, including the validation of a questionnaire in a developing Arab country, hence providing a reliable tool for researchers. HR specialists should lean toward applicants with positive affect, ensuring that their workplace is occupied by members with enhanced resilience. Furthermore, employers should support their employees’ professional growth, thus, boosting their employability during turmoil and consequently making them less vulnerable in times of economic recession.

The study’s unique context, depicted in the harsh economic and financial crisis, makes the findings on EWW of a high value.

Drawing on organizational design theory and organizational learning theory, this paper aims to examine component technology (CT) and the interaction between CT and experiential learning (EL) effects on the degree of integration (DI) of cross-border technological acquisitions.

Using a sample of 267 firms consisting of 229 acquirer firms who started cross-border technological acquisitions from developed economies and 38 acquirer firms who initiated cross-border technological acquisitions from emerging economies over the period of 1993–2016, this study adopts a value chain framework to measure the acquirers’ acquisition integration degree for the investigation of the effects of CT and the interaction between CT and EL.

First, this paper finds CT in cross-border technological acquisitions exerting a positive influence on the acquirer firm’s likelihood of the DI implementation, in line with the organizational design theory. Second, in view of organizational learning theory, this study finds EL and the combined effect of CT and EL to have an inverse influence on the DI.

The results imply that the moderating role of EL significantly optimizes decision choices for an acquirer firm for integration implementation strategies in the form of DI, such as full integration (structural integration), partial integration and no integration (structural separation), which appears to be crucial for cross-border technological acquisitions.

This study contributed to international business strategies by shedding light on the importance of the DI for an acquirer firm that undertakes a cross-border technological acquisition with a CT target firm. This study explains why structural integration might be necessary in cross-border technological acquisitions regardless of the costs of disruption it imposes, as well as the contexts in which it becomes less important or unnecessary. The study disclosed that the increase in the likelihood of DI because of CT depends on the EL of the acquisition company in the host country environment and fluctuates with the prior acquisition knowledge and EL of the host country. Combining two cross-border technological acquisition’s literature streams, such as CT and EL, this study enlightens the importance of organizational learning theory and theory of organization design strategic direction making on acquisition integration implementation strategies.

Policymakers are developing government-level pandemic response strategies (GPRS) to assist architecture, engineering and construction (AEC) enterprises. However, the effectiveness of the GPRS has not been assessed. Therefore, this study aims to investigate the interrelationships between GPRS and AEC enterprises. To achieve that aim, the study objectives are to compare GPRS effectiveness between small-medium and large AEC enterprises, develop groupings to categorize interrelated GPRS and evaluate the effectiveness of the GPRS and interrelated constructs.

A systematic literature review and semi-structured interviews with 40 AEC industry professionals were carried out, generating 22 GPRS. Then, questionnaire survey data was collected among AEC professionals. In total, 114 valid survey answers were received and analyzed using the Kruskal–Wallis H test, normalized mean analysis, factor analysis and fuzzy synthetic evaluation.

Small-medium enterprises have four distinct critical GPRS: “form a special task force to provide support in maneuvering COVID-19,” “provide infrastructure investment budgets to local governments,” “develop employee assistance programs that fit all types of working groups” and “diversify existing supply chain.” Large enterprises have two distinct critical GPRS: “provide help in digitalizing existing construction projects” and “mandate COVID-19 as force majeure.” Eighteen GPRS can be categorized into the following five constructs: “market stability and financial aid,” “enterprise capability management,” “supply chain improvement,” “law and policy resources” and “information and workforce management.” The former two constructs are more effective than other GPRS constructs.

This is the first paper that evaluates the effectiveness of GPRS for AEC enterprises, providing new evidence to policymakers for well-informed decision-making in developing pandemic response strategies.

This paper aims to analyze the thematic content of research addressing the relation between board of directors (BoD) and business transformation (BT) to obtain better understanding of status and to derive future areas of study.

This paper reviews literature through a bibliometric analysis based on co-occurrence of articles published in Web of Science Core Collection ™ (WoS) between 1990 and 2022, identifying key concepts, setting network of relations and identifying the strategic importance of clusters of concepts. Findings and implications are discussed, future lines of research are presented and limitations are noted.

Thematic research on boards addressing transformation shifted from the analysis of individuals' traits to an organizational approach with majority of research centered on the role of boards under different theories and the consequences of strategic changes on firm's performance. Further research is around gender diversity, sustainability and the moderating role of ownership structure and business culture.

Some limitations are also noted. This analysis considered articles indexed by WoS for Q1+Q2 publications as source of literature, while including others such as Scopus would increase knowledge base. Also, to identify main streams of research, the authors considered keywords with cumulative occurrence spanning from 30% to 40% while increasing this percentage would add terms that might improve precision to the connections among keywords. Other techniques could have been used such as co-citation or bibliographic coupling, although the authors find these as better suited to investigate the basic structure behind the foundational knowledge of the topic while the authors’ intention was to understand the positioning of study fields regarding the degree of research progress.

This paper presents some practical implications for future researchers. Those who wish to leverage previous evidence to address new research questions might look into principal themes covering BoD dynamics and composition to exert CG, and the relation between strategic decisions and performance measured by different variables. Those who wish to position their research as new findings to shed light on dilemmas, might find opportunities in the fields of climate change-sustainability, R&D for growth and innovation under the perspective of intangible assets.

This paper, is the first to the best of the authors’ knowledge, to identify research clusters for the intersection of boards and transformation and to determine their stage of development.

本文旨在分析探討董事會與業務轉型之間的關係的學術研究的專題內容，以能對有關課題的研究狀況有更深入的了解，並擬從分析中取得未來可供研究的範疇。

本文透過科學計量分析法來進行文獻探討。方法乃基於在1990年至2022年期間在Web of Science Core Collection 刊載的學術論文的共現分析而進行; 透過這個研究方法，研究人員建立了聯繫的網絡，並確認了各個概念群組的策略重要性。在本文中，研究結果和研究結果帶來的啟示會被討論，未來的研究領域和方針也會得到說明，研究的局限也會被認定和記錄下來。

探討董事會而又涉及業務轉型的專題研究，由當初集中探討董事個人的特質、轉移到現在研究整體的組織理念和處事取向，而就後者來說，大部份的研究都集中於在不同的理論框架裡董事會所扮演的角色，以及因策略上的改變而為公司的業績帶來的影響。進一步的學術研究都是圍繞著性別多元化、可持續性、所有權結構所扮演的緩和角色和商業文化的研究。

盡我們所知，本文乃為首篇學術論文，去鑑定關於董事會與業務轉型之間的關聯的研究集群，也是首篇學術論文，去確定這些研究集群的發展階段。

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

The current environment is unhelpful to female entrepreneurs, and they need to overcome numerous barriers when starting their own businesses. In this study, the researchers investigated the significant barriers that Pakistani female entrepreneurs require to overcome in this respect.

In this study, the researchers used a quantitative study and they used a questionnaire to survey the respondents and collect cross-sectional data. The researchers targeted female students who were undertaking bachelor’s and master’s degree programs in different Pakistani public and private sector universities. Accordingly, the researchers based this study’s findings on the usable samples received from 498 Pakistani female students.

The researchers used a structural equation model (SEM) in this study and its findings highlight that aversion to risk (ATR) has an insignificant impact on entrepreneurial inclinations (EI). In addition, fear of failure (FoF), lack of resources (LoR), aversion to hard work and stress (ASH) and the lack of social networking (LSN) have negative and insignificant effects on EI. The ATR factor has an insignificant effect on entrepreneurial success (ES), whereas FoF, LoR, ASH and LSN are negative and insignificant predictors of Pakistani female students’ ES.

This study’s findings may help Pakistani women to overcome the barriers to ES. In this respect, the researchers recommend that the Pakistan Government and policymakers develop significant strategies to provide the conducive business environment and to financially support Pakistani women to start their own businesses. Furthermore, this study’s findings contribute greatly to the vast amount of current literature and help to overcome the entrepreneurial conditions and barriers that potential entrepreneurs from advanced and developing countries experience frequently.

This study’s findings provide empirical evidence of EI and ES in Pakistan.

This paper aims to trace the contours of Islamic political economy (IPE) for last four decades with the help of bibliometric analysis. This method does not focus on in-depth literature. However, it reviews more material content of the published papers in the field, generally including the number of publications, authors, title, H-Index and authors’ affiliation.

The authors use biblioshiny by R in conducting bibliometric analysis. Based on the results of analysis, the authors only found 39 relevant documents to the topic with the help of keyword of “Islamic political economy”. The authors analyse the data and visualize it into bibliometric images for the convenience of the readers.

There are 39 documents on IPE in the annual scientific production. The year 1980 had the lowest productivity at 3% while the year 2007 showed an increase in scientific productivity by 13%. The most significant increase in production occurred between 2014 and 2015 by 8%, while the most significant decline occurred between 2007 and 2008 by 10%. The most significant contributors are Akan, T., Choudhury, M.A. and Asutay, M. According to the Corresponding Author’s Country, the UK has eight articles on IPE. Humanomics is the most influential Journal, with six documents.

This research only examines documents sourced from Web of Science and Scopus under the title “Islamic political economy” and does not include articles from other sources. This research has implications for future researchers and suggests a shift in recent research on IPE towards exploring current realities and expanding beyond traditional economic and political aspects. The goal is to gain a comprehensive understanding of Islam’s role in shaping economic and political systems, promoting inclusive sustainable development and social justice, and exploring its relationship with broader political and economic systems.

IPE has become a trendy topic in the early days, the second half of the 20th century, during the revival of the Islamic mode of finance and development. However, with time, the discussion on this topic appeared less in scientific and academic publications; this issue needs an overview of how far this discipline has evolved. This work aims to identify future research trends in this area. Scholars should investigate articles by author, institution, country, databases, data sources with high-impact factors and objective metrics to get new perspectives.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.