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This study aims to assess the effectiveness of online e-resource marketing tools used by university libraries to market e-resources. The specific objectives of this study were to uncover the e-resources that are showcased by university libraries, find out instructions posted by libraries to facilitate access and utilization of e-resources, examine marketing tools used, examine how current and relevant marketing messages and test the functionality of the marketing tools used.

An exploratory research design was used to assess the effectiveness of e-resources marketing tools. The study population included all public university libraries in Tanzania. The sample was purposively selected to include all publicly funded libraries as they work under similar management environments. Data collection involved the evaluation of online marketing tools using a matrix checklist.

It was revealed that most university libraries use websites for showcasing e-resources. The commonly used social media for marketing e-resources is Facebook followed by Instagram while Twitter and Ask a Librarian are the least used. The results further show that only a few universities provide instructions on how to access and use e-resources. The findings indicate that universities had active communication platforms for marketing e-resources. Furthermore, less than 50% of marketing messages are active and current. It is recommended that marketing e-resources should be integrated into the library’s plans and strategies and this should be informed by library policies.

Given that, this study involved Tanzanian public university libraries, it is possible that the findings do not represent the same situation in all academic university libraries in the nation.

This study assesses the effectiveness of online tools used to market e-resources in university libraries in Tanzania. It reveals the e-resources that are showcased online, the instructions provided on how to access the e-resources and the functionality of the marketing tools. Unlike the previous research, which primarily focused on evaluating the Web-based tools in university and college libraries, this study focused on public libraries in the country. The results will not only contribute to the theoretical and practical understanding of the functionality of the marketing tools but also provide actionable insights to decision-makers in libraries in marketing their e-resources.

Focused feedback, such as mentoring and coaching, is a crucial ingredient for generating the intellectual capital needed for successful venture creation and has become a structural resource offered to entrepreneurs in business incubator/accelerator programs. Yet so far, literature has remained silent on the way that entrepreneurs differ in their engagement with focused feedback in such programs. This study poses the question of how focused feedback engagement shapes cognitive development during value creation (i.e. business opportunity development), aimed at the construction of a taxonomy of such feedback engagement.

Focusing on cognitive learning outcomes, we carried out a qualitative analysis using NVivo to perform content analysis on the logbooks of 70 entrepreneurs engaged in business opportunity development in a highly regarded accelerator program.

Results show that engagement with focused feedback and its effects relate to the state of tangibility of the entrepreneur’s value offer and to the amount of prior entrepreneurial experience. We also develop a promising taxonomy to classify entrepreneurs on their learning needs and outcomes (e.g. procedural versus declarative knowledge).

This study brings together types of human learning (types of knowledge acquired) with types of focused feedback. This connection has been speculated to exist in entrepreneurial settings; this study provides strong initial evidence that argues for more explicit consideration in practice. Adding the intellectual capital perspective further enabled this study to better address implications for practice as well as motivate powerful new directions for research.

Are there smart ways to find heuristics? What are the common principles behind heuristics? We propose an integrative definition of heuristics, based on insights that apply to all heuristics, and put forward meta-heuristics for discovering heuristics.

We employ Herbert Simon’s metaphor that human behavior is shaped by the scissors of the mind and its environment. We present heuristics from different domains and multiple sources, including scholarly literature, practitioner-reports and ancient texts.

Heuristics are simple, actionable principles for behavior that can take different forms, including that of computational algorithms and qualitative rules-of-thumb, cast into proverbs or folk-wisdom. We introduce heuristics for tasks ranging from management to writing and warfare. We report 13 meta-heuristics for discovering new heuristics and identify four principles behind them and all other heuristics: Those principles concern the (1) plurality, (2) correspondence, (3) connectedness of heuristics and environments and (4) the interdisciplinary nature of the scissors’ blades with respect to research fields and methodology.

We take a fresh look at Simon’s scissors-metaphor and employ it to derive an integrative perspective that includes a study of meta-heuristics.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

This paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.

The research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).

The research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.

Each internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.

The application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

The need for mechanization in company operations during the pandemic has been further illustrated by the pressure to use recent technologies for sustainable business practices. Recent technologies like Internet of Things (IoT), artificial intelligence (AI), big data and cloud are being abandoned in favour of automating business activities during and after the pandemic, to build flexibility and sustainability. The objective of this paper is to give an outline of the literature on the bang of digital metamorphosis on organizational adaptability. The paper focuses on the future of business sustainability from dislocations by espousing recent technologies from different perspectives. As well as the anticipated disruptive developments, the benefits of technology on economics and business are also being felt, but still in their early stages. Similar ideas and methods must be implemented as quickly as is practical, and governments and enterprises must be ready and willing to do so. The transition to a commercial environment that emphasizes technology from alternative distribution channels will have a direct influence on organizational structures. Additionally, they could have training in or experience in positive sciences, which will aid in creating the corporate environment of the future sustainably. Absolutely the variety of technologies in business helps to accelerate business activities and attain the maximum goal before and after the pandemic. It still appears that a hypothetical model is required that could simplify the incorporation of using these technologies during a disaster with business processes. The findings may be applied to manage technology and speed up corporate resilience for a better economy.