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This paper aims to demonstrate that Facebook likes affect outcomes in nonprofit settings. Specifically, Facebook likes influence affinity to nonprofits, which, in turn, affects fundraising outcomes.

The authors report three studies that establish that relationship. To examine social contagion, Study 1 – an auction field study – relies on selling artwork created by underprivileged youth. To isolate signaling, Study 2 manipulates the number of total Facebook likes on a page. To isolate commitment escalation, Study 3 manipulates whether a participant clicks a Facebook like.

The results show that Facebook likes increase willingness to contribute in nonprofit settings and that the process goes through affinity, as well as through Facebook impressions and bidding intensity. The total number of Facebook likes has a direct signaling effect and an indirect social contagion effect.

The effectiveness of the proposed mechanisms is limited to nonprofit settings and only applies to short-term effects.

Facebook likes serve as both a quality signal and a commitment mechanism. The magnitude of commitment escalation is larger, and the relationship is moderated by familiarity with the organization. Managers should target Facebook likes at those less familiar with the organization and should prioritize getting a potential donor to leave a like as a step leading to donation, in essence mapping a donor journey from prospective to active, where Facebook likes play an essential role in the journey. In a charity auction setting, the donor journey involves an additional step of bidder intensity.

The approach the authors study is shown effective in nonprofit settings but does not appear to extend to corporate social responsibility more broadly.

To the best of the authors’ knowledge, this study is the first investigation to map Facebook likes to a seller’s journey through signals and commitment, as well as the only investigation to map Facebook likes to charity auctions and show the effectiveness of this in the field.

This study aims to investigate how the social identity theory and emotional attachment theory influence the willingness of consumers to buy foreign cosmetic products. Specifically, this study examines the relationship between consumer ethnocentrism, foreign product judgment and willingness to buy foreign products. Furthermore, the interaction effect of consumer affinity and patriotism are tested in the model.

An online survey of 208 millennial Muslim women consumers was used to collect the data. The structural equation modeling test was used to assess the six hypotheses. Moreover, the two-step estimation approach was used to test the interaction moderation of consumer affinity and patriotism.

The results indicate that consumer ethnocentrism has a positive and significant relationship with foreign product judgment. Foreign product judgment was also found to have a positive and significant relationship with willingness to buy. In addition, this study concludes that affinity was found to moderate the relationship between consumer ethnocentrism and foreign product judgment and strengthen the positive and significant effect of foreign product judgment on the willingness to buy. Finally, patriotism did not moderate the relationship between consumer ethnocentrism and foreign product judgment. However, patriotism moderated the relationship between foreign product judgment and willingness to buy.

This study only focused on one category (i.e. low involvement product), and the authors recommend future studies to examine a high involvement product. Other individual orientation constructs, such as xenocentrism, need to be examined in future studies. Moreover, only intentional measures were investigated. Thus, further research could correlate intentional measures with product ownership. Finally, future research could examine how consumers behave differently across nations. Thus, the present model would require cross-cultural research.

Marketers focusing on global branding and international marketing can benefit from the findings of this paper by understanding the antecedents of consumers’ willingness to buy in the foreign cosmetic products setting. Additionally, foreign cosmetic marketers could focus on consumer affinity to strengthen the communication with and arouse the affinity of Muslim millennials women consumers in Indonesia. Finally, marketers can incorporate messages and signals of patriotism in their marketing communications to increase Muslim millennial women consumers’ love and pride.

The growing obsession with beauty among women has led to the immense growth of the cosmetics industry. This phenomenon has spawned an abundance of cosmetic products on the market. The advancement of information technology has further increased competition for cosmetic products as more products can be quickly brought to market. Muslim millennials consumers must be aware and careful about raw materials, impacts on long-term health, impacts on the national economy, environmental impacts and halal certification when using various kinds of cosmetics.

This study contributes to the literature on international marketing research by incorporating the interactive effect of consumer affinity and patriotism in the acceptance of foreign cosmetic products.

The purpose of this article is to (1) map the most important topics in the domain of sustainable business practices for entrepreneurial firms in emerging countries, (2) identify important terms in the various dimensions of sustainability in business and (3) identify the important practices and prioritize the practices.

This study has adopted a unique methodology that combines state-of-art scientometric analysis with the fuzzy nominal group technique (NGT) and fuzzy decision-making trial and evaluation laboratory (DEMATEL). Results obtained from the co-occurrence analysis in scientometrics were further mapped through NGT to obtain the list of the most important topics in the domain. The factors affecting sustainable business practices obtained through topic mapping were analyzed through fuzzy DEMATEL to obtain the cause-and-effect relation of variables.

The scale of firms, leadership, uncertainty, gender, country/location, education and tourism were found to be the factors affecting the sustainable business practices of entrepreneurial firms. The sustainable business practices for entrepreneurial firms were (1) innovation, (2) resilience, (3) policy, (4) business ethics and virtue ethics, (5) business model, (6) upcycling and value creation, (7) collaboration and (8) triple bottom line.

Policymakers in entrepreneurial firms, as well as other organizations, can implement the identified sustainable business practices to obtain optimum results and smooth functioning of the companies. The research framework obtained can be tested using exploratory methods.

Very few researchers have used the technique of scientometric analysis to identify the sustainable business practices of entrepreneurial firms, and to the best of the knowledge of the authors, no earlier researcher has attempted to use the technique of topic mapping, fuzzy NGT and fuzzy DEMATEL in combination.

In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

The current study aims to map the scientific output of grey literature (GL) through bibliometric approaches.

The source for data extraction is a comprehensive “indexing and abstracting” database, “Web of Science” (WOS). A lexical title search was applied to get the corpus of the study – a total of 4,599 articles were extracted for data analysis and visualisation. Further, the data were analysed by using the data analytical tools, R-studio and VOSViewer.

The findings showed that the “publications” have substantially grown up during the timeline. The most productive phase (2018–2021) resulted in 47% of articles. The prominent sources were PLOS One and NeuroImage. The highest number of papers were contributed by Haddaway and Kumar. The most relevant countries were the USA and UK.

The study is useful for researchers interested in the GL research domain. The study helps to understand the evolution of the GL to provide research support further in this area.

The present study provides a new orientation to the scholarly output of the GL. The study is rigorous and all-inclusive based on analytical operations like the research networks, collaboration and visualisation. To the best of the authors' knowledge, this manuscript is original, and no similar works have been found with the research objectives included here.

Several manuscripts are adopting knowledge-based dynamic capabilities (KBDCs) as their main theoretical lens. However, these manuscripts lack consistent conceptualization and systematization of the construct. Consequently, the purpose of this study is to advance the understanding of KBDCs by clarifying the dominant concepts at the junction of knowledge management and dynamic capabilities domains, identifying which emerging themes are gaining traction with KBDCs scholars, demonstrating how the central thesis around KBDCs has evolved and explaining how can KBDCs scholars move towards finding a mutually agreed conceptualization of the field to advance empirical assessment.

The Clarivate Analytics Web of Science Core Collection database was used to extract 225 manuscripts that lie at the confluence of two promising management domains, namely, knowledge management and dynamic capabilities. A scientometric analysis including co-citation analysis, bibliographic coupling, keyword co-occurrence network analysis and text mining was conducted and integrated with a systematic review of results to facilitate an unstructured ontological discovery in the field of KBDCs.

The co-citation analysis produced three clusters of research at the junction of knowledge management and dynamic capabilities, whereas the bibliographic coupling divulged five themes of research that are gaining traction with KBDCs scholars. The systematic literature review helped to clarify each clusters’ content. While scientific mapping analysis explained how the central thesis around KBDCs has evolved, text mining and keyword analysis established how KBDCs emerge from the combination of knowledge management process capabilities and dynamic capabilities.

Minimal attention has been paid to systematizing the literature on KBDCs. Accordingly, KBDCs view has been investigated through complementary scientometric methods involving machine-based algorithms to allow for a more robust, structured, comprehensive and unbiased mapping of this emerging field of research.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.