Knowledge transfer challenges and mitigation strategies in global software development—A systematic literature review and industrial validation

Extracellular vesicles as therapeutic agents in rheumatoid arthritis: a systematic review of current evidence

Rheumatoid arthritis (RA) is defined as a chronic autoimmune disease that severely influences a patient's quality of life. Extracellular vesicles (EVs) have gained much attention in recent years as one of the most potent therapeutic agents for the treatment of RA. A systematic review was performed with the purpose of assessing the current evidence relating to the therapeutic applications of EVs in RA. The systematic search was performed in the databases of PubMed, Scopus, and Web of Science, from inception times to September 2024. All studies investigating the use of EVs for the treatment of RA were included. The quality appraisal of selected articles and data extraction regarding EV characteristics, therapeutic applications, and associated outcomes were performed. Of the 1418 initially identified articles, 59 studies met inclusion criteria. Regarding their cellular origins, most EVs were derived from mesenchymal stem cells, followed by immune cells. The main therapeutic mechanisms included modulation of the immune response, reduction of inflammation, and repair of tissues. Recent trends are toward increasing interest in engineered EVs and combination therapies. Indeed, most studies reported positive outcomes with regard to lowered inflammation and improved joint function. On the other hand, standardization of the metrics of evaluation considerably varied between different studies. EVs are promising therapeutic agents in the treatment of RA by modulating immune responses. Standardization, delivery systems, and clinical translation are challenges yet to be overcome. Future studies will be directed to optimize EV engineering, targeted delivery systems, and large-scale clinical trials.

Problematic Social Media Use and Employee Outcomes: A Systematic Literature Review

A systematic literature review was conducted to summarize research that examined the association between problematic social media use (PSMU) and employee work-related and psychological outcomes. Following rigorous protocols, 42 peer-reviewed studies published from 2013 to 2022 were identified from the Web of Science, Elsevier, and PubMed databases, which were used to analyze and evaluate the current research boundary, explore the accumulated knowledge, and propose approaches to further enrich this research area. The findings of this review revealed that the current research mainly focuses on four research themes (a) focal areas of effects, (b) divergence of effects, (c) contextual specificity, and (d) investigated variables. However, the existing knowledge on this domain is still limited in understanding the conceptualization of PSMU, along with the narrow focus on methodological, geographical focus, and objective measures. This study contributes to theory, as it is one of the few reviews that link PSMU to employee outcomes, building an integrated framework to outline future research trends.

Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review

BACKGROUND AND OBJECTIVE

Pharmacokinetic studies encompass the examination of the absorption, distribution, metabolism, and excretion of bioactive compounds. The pharmacokinetics of drugs exert a substantial influence on their efficacy and safety. Consequently, the investigation of pharmacokinetics holds great importance. However, laboratory-based assessment necessitates the use of numerous animals, various materials, and significant time. To mitigate these challenges, alternative methods such as artificial intelligence have emerged as a promising approach. This systematic review aims to review existing studies, focusing on the application of artificial intelligence tools in predicting the pharmacokinetics of drugs.

METHODS

A pre-prepared search strategy based on related keywords was used to search different databases (PubMed, Scopus, Web of Science). The process involved combining articles, eliminating duplicates, and screening articles based on their titles, abstracts, and full text. Articles were selected based on inclusion and exclusion criteria. Then, the quality of the included articles was assessed using an appraisal tool.

RESULTS

Ultimately, 23 relevant articles were included in this study. The clearance parameter received the highest level of investigation, followed by the  area under the concentration-time curve (AUC) parameter, in pharmacokinetic studies. Among the various models employed in the articles, Random Forest and eXtreme Gradient Boosting (XGBoost) emerged as the most commonly utilized ones. Generalized Linear Models and Elastic Nets (GLMnet) and Random Forest models showed the most performance in predicting clearance.

CONCLUSION

Overall, artificial intelligence tools offer a robust, rapid, and precise means of predicting various pharmacokinetic parameters based on a dataset containing information of patients or drugs.

Water treatment and artificial intelligence techniques: a systematic literature review research

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review

BACKGROUND AND OBJECTIVE

Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient clinical deterioration, most are based on vital signs and have methodological shortcomings that are not able to provide accurate estimates of deterioration risk. The aim of this systematic review is to examine the effectiveness, challenges, and limitations of using machine learning (ML) techniques to predict patient clinical deterioration in hospital settings.

METHODS

A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines using EMBASE, MEDLINE Complete, CINAHL Complete, and IEEExplore databases. Citation searching was carried out for studies that met inclusion criteria. Two reviewers used the inclusion/exclusion criteria to independently screen studies and extract data. To address any discrepancies in the screening process, the two reviewers discussed their findings and a third reviewer was consulted as needed to reach a consensus. Studies focusing on use of ML in predicting patient clinical deterioration that were published from inception to July 2022 were included.

RESULTS

A total of 29 primary studies that evaluated ML models to predict patient clinical deterioration were identified. After reviewing these studies, we found that 15 types of ML techniques have been employed to predict patient clinical deterioration. While six studies used a single technique exclusively, several others utilised a combination of classical techniques, unsupervised and supervised learning, as well as other novel techniques. Depending on which ML model was applied and the type of input features, ML models predicted outcomes with an area under the curve from 0.55 to 0.99.

CONCLUSIONS

Numerous ML methods have been employed to automate the identification of patient deterioration. Despite these advancements, there is still a need for further investigation to examine the application and effectiveness of these methods in real-world situations.

Managing knowledge loss: a systematic literature review and future research directions

Purpose

Job market shifts, such as workforce mobility and aging societies, cause the exit of knowledgeable personnel from organizations. The ensuing knowledge loss (K-loss) has broad negative effects. This study analyzes the knowledge management literature on K-loss published from 2000 to 2021 and identifies fruitful directions for future research.

Design/methodology/approach

The authors conduct a systematic literature review of 74 peer-reviewed articles published between 2000 and 2021. These articles were retrieved from ProQuest Central, Science Direct, EBSCOhost and Emerald databases. The analysis utilizes Jesson et al.’s (2011) six principles: field mapping, comprehensive search, quality assessment, data extraction, synthesis and write-up.

Findings

Three sub-topics emerge from the systematic literature review: K-loss drivers, positive and negative impacts of K-loss and mitigation strategies. Over half of the literature addresses mitigation strategies and provides solutions for K-loss already in progress, rather than proposing preventive measures.

Research limitations/implications

This study has limitations related to the time span covered. Moreover, it focuses on articles published in refereed journals. Therefore, important contributions from conference papers, books and professional reports were excluded.

Originality/value

This research comprehensively synthesizes the K-loss literature and proposes future avenues of research to address under-investigated areas and potentially lead to theoretical and empirical advancements in the field. This study also provides suggestions for improving managerial practices.

Evaluating online health information quality using machine learning and deep learning: A systematic literature review

Background

Due to the large volume of online health information, while quality remains dubious, understanding the usage of artificial intelligence to evaluate health information and surpass human-level performance is crucial. However, the existing studies still need a comprehensive review highlighting the vital machine, and Deep learning techniques for the automatic health information evaluation process.

Objective

Therefore, this study outlines the most recent developments and the current state of the art regarding evaluating the quality of online health information on web pages and specifies the direction of future research.

Methods

In this article, a systematic literature is conducted according to the PRISMA statement in eight online databases PubMed, Science Direct, Scopus, ACM, Springer Link, Wiley Online Library, Emerald Insight, and Web of Science to identify all empirical studies that use machine and deep learning models for evaluating the online health information quality. Furthermore, the selected techniques are compared based on their characteristics, such as health quality criteria, quality measurement tools, algorithm type, and achieved performance.

Results

The included papers evaluate health information on web pages using over 100 quality criteria. The results show no universal quality dimensions used by health professionals and machine or deep learning practitioners while evaluating health information quality. In addition, the metrics used to assess the model performance are not the same as those used to evaluate human performance.

Conclusions

This systemic review offers a novel perspective in approaching the health information quality in web pages that can be used by machine and deep learning practitioners to tackle the problem more effectively.

What is needed to build a personalized recommender system for K-12 students' E-Learning? Recommendations for future systems and a conceptual framework

Online learning has significantly expanded along with the spread of the coronavirus disease (COVID-19). Personalization becomes an essential component of learning systems due to students' different learning styles and abilities. Recommending materials that meet the needs and are tailored to learners' styles and abilities is necessary to ensure a personalized learning system. The study conducted a systematic literature review (SLR) of papers on recommendation systems for e-learning in the K12 setting published between 2017 and 2021 and aims to identify the most important component of a personalized recommender system for school students' e-learning. Recommendations for later studies were proposed based on the identified components, namely a personalized conceptual framework for providing materials to school students. The proposed framework comprised four stages: student profiling, material collection, material filtering, and validation.

IoT Adoption and Application for Smart Healthcare: A Systematic Review

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

Work Systems in the Indian Information Technology (IT) Industry Delivering Artificial Intelligence (AI) Solutions and the Challenges of Work from Home

Our study is based on a workplace ethnography conducted between Jan-May 2020 in an AI research lab of an Indian service-based IT organization, whose operations shifted from co-located work to work from home (WFH) owing to the recent pandemic. The field notes of the ethnographer, working as a full-time intern in a running AI project within this lab, is the basis for the qualitative data for this study. We discuss the socio-technical aspects and the specific challenges of distributed team-working due to the WFH norms facing such emerging research units, which are rapidly diffusing across the IT industry in the offshoring context, particularly in India. We rely on work system theory as a map to bring out key findings from our ethnographic observations. The findings point to the importance of having workflows compatible with the specific work roles in such emerging work systems - particularly for the beginner roles in the AI space. Our study contributes to the IS literature by depicting the challenges of distributed teams in a relatively novel setting emerging in offshoring contexts like the Indian IT sector, and suggests implications for managers handling AI projects and tackling employee-focused Human Resource practices in such settings.

The Utilization of Augmented Reality Technology for Sustainable Skill Development for People with Special Needs: A Systematic Literature Review

New technologies such as Augmented Reality can be used to enhance the possibility of obtaining new experiences to assist people with special needs. However, in the literature, there are not enough studies conducted on the use of Augmented Reality as an assistive technology, especially for people with special needs. The purpose of this study is to highlight the use of Augmented Reality technology on people with special needs for skill development. This systematic literature review includes recent and high-quality articles from chosen prestige databases between the years 2010 and 2020. The selected studies which fitted the eligibility selection criteria have been analyzed and synthesized. The study findings reveal the importance of using AR technology to assist individuals with special needs in their skill development process, to help them become more independent. We hope this study will enlighten researchers and the developers of AR tools. It has been recommended that more studies be done on the sustainable use of AR as an assistive technology, particularly for children with special needs, to make their life easier.

Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research.

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.

A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Serendipity in human information behavior: a systematic review

Purpose

Serendipitous information discovery has become a unique and important approach to discovering and obtaining information, which has aroused a growing interest for serendipity in human information behavior. Despite numerous publications, few have systematically provided an overview of current state of serendipity research. Consequently, researchers and practitioners are less able to make effective use of existing achievements, which limits them from making advancements in this domain. Against this backdrop, we performed a systematic literature review to explore the world of serendipity and to recapitulate the current states of different research topics.

Design/methodology/approach

Guided by a prior designed review protocol, this paper conducted both automatic and manual search for available studies published from January 1990 to December 2020 on seven databases. A total of 207 serendipity studies closely related to human information behavior form the literature pool.

Findings

We provide an overview of distinct aspects of serendipity, that is research topics, potential benefits, related concepts, theoretical models, contextual factors and data collection methods. Based on these findings, this review reveals limitations and gaps in the current serendipity research and proposes an agenda for future research directions.

Originality/value

By analyzing current serendipity research, developing a knowledge framework and providing a research agenda, this review is of significance for researchers who want to find new research questions or re-align current work, for beginners who need to quickly understand serendipity, and for practitioners who seek to cultivate serendipity in information environments.

Entrepreneurship education: systematic literature review and future research directions

Purpose

The purpose of the study is to further understanding of entrepreneurship education, highlighting current trends and directions for further research.

Design/methodology/approach

This paper used systematic literature review of published articles to collect, evaluate, and interpret entrepreneurship education literature from selected databases between 2009 and 2019. The study reviewed 90 articles from the entrepreneurship education literature. There are several different topics that have been analyzed; with the most researched topic being analyzed was focusing on entrepreneurship education development.

Findings

Entrepreneurship education programs have become an increasingly important focus of attention in recent years. This paper deeply investigates the literature on entrepreneurship education to help entrepreneurship education decision makers to develop better solutions.

Research limitations/implications

It must be noted that this study has some limitations, which suggest avenues for further research. In reviewing the articles, the study used only four databases and only considered papers published between 2009 and 2019. Other studies may include more databases and a longer time frame.

Originality/value

Regarding the theories most used in the reviewed articles, TPB and social learning theory (SLT) were most used in relation to entrepreneurship education. This shows that researchers were focusing on the influence of entrepreneurship education in relation to the entrepreneurial intention, behavior and attitude of the individuals.

Cultural distance and inter-organizational knowledge transfer: a case study of a multinational company

Purpose

The purpose of this study is to explore a phenomenon in knowledge management that has been given scant attention: the influence of cultural distance on inter-organizational knowledge transfer in the context of multinational companies involving headquarters in the USA and a subsidiary in Sri Lanka.

Design/methodology/approach

Designed as a qualitative exploratory study, data was collected through in-depth interviews of 15 participants and documents review.

Findings

The findings of this study implied that the theoretically introduced cultural dimensions shall be relevant to analyze the phenomenon of this study. Consequently, through the findings of this study, it is argued that inter-organizational knowledge transfer in multinational companies is influenced by cultural distance.

Research limitations/implications

This study theoretically and empirically contributes to the debates on knowledge transfer in knowledge management research in general and, inter-organizational knowledge transfer in multinational companies between headquarters and subsidiaries with respect to the influence of cultural distance in particular, through the light of Trompenaars’ (1993) cultural dimensions theory.

Practical implications

The findings of this study could motivate the practitioners to take into account: the influence of cultural distance on inter-organizational knowledge transfer, if inter-organizational knowledge transfer happens in similar contexts: multinational companies with a headquarters in the USA (a western context) and a subsidiary in Sri Lanka (a non-western context) in the practical business world.

Originality/value

This study provides theoretical and empirical insights into the influence of cultural distance on inter-organizational knowledge transfer in multinational companies between headquarters and subsidiaries in the selected context while suggesting various avenues for further research toward the influence of cultural distance on such phenomenon in similar/dissimilar contexts.

Understanding Telerehabilitation Technology to Evaluate Stakeholders' Adoption of Telerehabilitation Services: A Systematic Literature Review and Directions for Further Research

OBJECTIVES

To examine the adoption of telerehabilitation services from the stakeholders' perspective and to investigate recent advances and future challenges.

DATA SOURCES

A systematic review of English articles indexed by PubMed, Thomson Institute of Scientific Information's Web of Science, and Elsevier's Scopus between 1998 and 2020.

STUDY SELECTION

The first author (N.N.) screened all titles and abstracts based on the eligibility criteria. Experimental and empirical articles such as randomized and nonrandomized controlled trials, pre-experimental studies, case studies, surveys, feasibility studies, qualitative descriptive studies, and cohort studies were all included in this review.

DATA EXTRACTION

The first, second, and fourth authors (N.N., W.I., B.N.) independently extracted data using data fields predefined by the third author (M.B.). The data extracted through this review included study objective, study design, purpose of telerehabilitation, telerehabilitation equipment, patient/sample, age, disease, data collection methods, theory/framework, and adoption themes.

DATA SYNTHESIS

A telerehabilitation adoption process model was proposed to highlight the significance of the readiness stage and to classify the primary studies. The articles were classified based on 6 adoption themes, namely users' perception, perspective, and experience; users' satisfaction; users' acceptance and adherence; TeleRehab usability; individual readiness; and users' motivation and awareness.

RESULTS

A total of 133 of 914 articles met the eligibility criteria. The majority of papers were randomized controlled trials (27%), followed by surveys (15%). Almost 49% of the papers examined the use of telerehabilitation technology in patients with nervous system problems, 23% examined physical disability disorders, 10% examined cardiovascular diseases, and 8% inspected pulmonary diseases.

CONCLUSION

Research on the adoption of telerehabilitation is still in its infancy and needs further attention from researchers working in health care, especially in resource-limited countries. Indeed, studies on the adoption of telerehabilitation are essential to minimize implementation failure, as these studies will help to inform health care personnel and clients about successful adoption strategies.

Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review

Race towards industry 4.0 increases the hyper competition and puts pressure on organizations to integrate the advanced technologies. Business intelligence system (BIS) is one of the top prioritized technologies that attracted the significant attention of policy-makers and industry experts due to its ability to provide more informed and intelligent knowledge for decision-making processes. It is evident by literature that organizations and industries are prone to integrate the BIS at large scale, but more than 70% BIS projects fail to give the expected results. Hence, it is pertinent to explore the significant determinants that influence the BIS adoption and acceptance in organizations. Although previous literature did not have any comprehensive review relevant to the individual, technological, organizational, and environmental determinants. Therefore, the current study tries to narrow this gap by a systematic literature review (SLR) of 84 studies that were published during the period of 2011–2020. A total of 93 determinants are identified based on content analysis by using text mining techniques of Yoshikoder and human coding skills. The identified determinants are ranked according to their frequency of use. A theoretical framework has been developed with potential determinants and theories. The study results will enrich the recent BIS literature and improve the understanding of practitioners’ decision-making processes to leverage maximum value from the adoption of BIS.

Requirements engineering issues causing software development outsourcing failure

Software development outsourcing is becoming more and more famous because of the advantages like cost abatement, process enhancement, and coping with the scarcity of needed resources. Studies confirm that unfortunately a large proportion of the software development outsourcing projects fails to realize anticipated benefits. Investigations into the failures of such projects divulge that in several cases software development outsourcing projects are failed because of the issues that are associated with requirements engineering process. The objective of this study is the identification and the ranking of the commonly occurring issues of the requirements engineering process in the case of software development outsourcing. For this purpose, contemporary literature has been assessed rigorously, issues faced by practitioners have been identified and three questionnaire surveys have been organized by involving experienced software development outsourcing practitioners. The Delphi technique, cut-off value method and 50% rule have also been employed. The study explores 150 issues (129 issues from literature and 21 from industry) of requirements engineering process for software development outsourcing, groups the 150 issues into 7 identified categories and then extricates 43 customarily or commonly arising issues from the 150 issues. Founded on 'frequency of occurrence' the 43 customarily arising issues have been ranked with respect to respective categories (category-wise ranking) and with respect to all the categories (overall ranking). Categories of the customarily arising issues have also been ranked. The issues' identification and ranking contribute to design proactive software project management plan for dealing with software development outsourcing failures and attaining conjectured benefits of the software development outsourcing.

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

Barriers of knowledge transfer and mitigating strategies in collaborative management system implementations

Purpose

As the dynamics of the external environment of the enterprise continue to increase, the support of information systems for organizational agility becomes increasingly important. Collaborative Management System (CMS) is a new type of information system that can cope with the dynamic changes of the organization. Effective knowledge transfer is the core of the system implementation. The purpose of this study is to explore the knowledge transfer barriers faced by CMS in its implementation process.

Design/methodology/approach

Through field interviews with a representative CMS provider, this paper summarizes the barriers of knowledge transfer during CMS implementation into three aspects.

Findings

Based on the innovative measures taken by the company and relevant literature, the corresponding mitigating strategies are proposed.

Originality/value

The findings enrich the implementation methodology of agile information systems by exploring the knowledge transfer problem from a novel context. The study also provides a reference for practical implementation to overcome the dilemma of knowledge transfer.