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Digital platforms increase their function and scope by leveraging boundary resources and complementary add-on products from third-party developers to interact with external entities and producers. Application Programming Interfaces (APIs) are essential boundary resources developers use to connect applications, systems and platforms. This notwithstanding, previous API studies tend to focus more on the technical dimensions, with little on the social and cultural contexts underpinning API innovations. This study relies on the new (neo) institutional theory (focusing on regulative, normative and cultural-cognitive pillars) as an analytical lens to understand the institutional forces that affect API integration among digital firms.

The study adopts a qualitative case study methodology and relies on phone calls and a semi-structured in-depth interview approach of a Ghanaian digital music platform to uncover the institutional forces affecting API integration.

The findings reveal that regulative institutions such as excessive tax regimes mostly constrained API development and integration initiatives. However, other regulative institutions like the government digitalization agenda enabled API integration. Normative institutions, such as the growing use of e-payment options, enabled API integration in digital music platforms. Cultural-cognitive institutions like employee ego constrained the API integration process in music digital platforms.

This study primarily contributes to deepening understanding of the relevant literature by exploring the institutional forces that affect API integration among digital firms in a developing economy. The study also uncovered a new form of an institution known as motivational institution as an enabler for API development and integration in digital music platforms.

The purpose of this study is to investigate a practical strategy for integrating application programming interfaces (APIs) and standard interchange protocols (SIPs) within library and information services. This study will seek to determine how such an integration strategy can improve access to resources, enhance the user experience, optimize library operations and improve the overall efficiency of library services.

A qualitative approach to research will be used in this study. This study will be based on the review of relevant literature sources, case studies and real examples. The data analyzes to determine the practical application of SIP and API integration and identify the major methods, approaches and processes used by libraries to successfully implement integration projects.

This study explores that library and information services may achieve numerous benefits from API and SIP integration. The cases describe how libraries have managed to improve access, user experience, operational efficiency and general performance. Libraries have integrated APIs and SIP to create seamless search experiences, establish communication networks in real-time, and develop automated workflows and customer services. API and SIP integration will transform libraries in future.

The originality of this study is the focus of the API and SIP integration. While other authors have discussed the concept of integration from a theoretical standpoint, this study presents practical recommendations and implementation advice for librarians and researchers. This study uses real cases and examples to illustrate how libraries today have managed to improve their operations with the help of APIs and SIP integration.

This review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?

There are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.

Collecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.

The study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.

The outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.

Internship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.

While surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.

First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.

The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.

This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

This study aims to adjust Thesis-type properties on Schema.org using metadata models and standards (MS) (Bibframe, electronic thesis and dissertations [ETD]-MS, Common European Research Information Format [CERIF] and Dublin Core [DC]) to enrich the Thesis-type properties for better description and processing on the Web.

This study is applied, descriptive analysis in nature and is based on content analysis in terms of method. The research population consisted of elements and attributes of the metadata model and standards (Bibframe, ETD-MS, CERIF and DC) and Thesis-type properties in the Schema.org. The data collection tool was a researcher-made checklist, and the data collection method was structured observation.

The results show that the 65 Thesis-type properties and the two levels of Thing and CreativeWork as its parents on Schema.org that corresponds to the elements and attributes of related models and standards. In addition, 12 properties are special to the Thesis type for better comprehensive description and processing, and 27 properties are added to the CreativeWork type.

Enrichment and expansion of Thesis-type properties on Schema.org is one of the practical applications of the present study, which have enabled more comprehensive description and processing and increased access points and visibility for ETDs in the environment Web and digital libraries.

This study has offered some new Thesis type properties and CreativeWork levels on Schema.org. To the best of the authors’ knowledge, this is the first time this issue is investigated.

This paper aims to understand the spatiotemporal influence of metro rail connectivity on housing prices in surrounding areas. The study assesses the average annual price shift for apartments around metro stations in Delhi during the previous decade, specifically from 2010 to 2019. The authors examine the spatiotemporal extents to which housing prices are determined by the prominence of metro stations and spatial development around metro stations.

The authors perform the cross-tabulation analysis to calculate chi-square values to test the hypotheses concerning the responsiveness of the housing market in Delhi to the number of locational variables in the areas connected with the mass public transportation system.

The empirical findings verify the existence of a housing market overvaluation in Delhi around metro stations until 2013, which was eventually re-adjusted after 2014. The key findings of the study suggest the role of location variables concerning metro rails in the shooting up of the housing prices in the city. In addition, the research establishes the association of annual housing price shifts to the metro rails in the short-term, mid-term and long-term in conjunction with the distance from the metro station.

In the market, the prices are often overvalued by real estate agents due to better connectivity to the metro stations. The overvaluation eventually causes massive downfalls in housing markets and rollouts as a risk for the investors. However, the effect of mass transportation on housing prices is mixed in nature, limited to a certain extent only and not as influential as frequently portrayed by the market forces. This effect loses colour with time.

Financial technology (fintech) has emerged as a major player in the global financial system, providing a range of services such as payments, digital currencies, money transfers, loans, crowdsourcing, and insurance. Fintech startups in Arab countries have also gained traction due to economic openness and globalization. However, concerns remain about the safety, durability, and security of traditional financial services, especially with the increasing use of artificial intelligence (AI) and digitization. The Central Bank of Bahrain and other regulatory authorities need to balance risk avoidance with the global trend toward innovation in fintech, as well as ensure that these technologies are not used for fraud, money laundering, piracy, or terrorist financing. The Bahraini government and supervisory authorities must strike a balance between preserving the integrity and robustness of the financial and banking sector and developing innovation. This can be achieved by adjusting the rhythm of comparison, strengthening and enhancing the safety of banks, achieving financial stability, and ensuring compliance with laws and legislation. It is important to address gaps in regulatory rules, information security, and the business environment, and launch financial awareness at the community level before embracing the potential of fintech and its unseen future development at the level of cryptocurrencies and others. The current work examines the impact of Fintech on the Future of banking in Bahrain and the opportunities and challenges.

This paper aims to explore the use and impact of social media, specifically Twitter (now X), in political mobilization in Pakistan. It focuses on the events followed by the no-confidence motion against Imran Khan as Pakistan’s prime minister in April 2022 and the protest campaign that ensued, facilitated through the strategic use of the Urdu hashtag #امپورٹڈ_حکومت_نامنظور (translated as “imported-government unacceptable”) on Twitter, both within and outside Pakistan.

Using Web scraping, data from Twitter was extracted and analyzed between 2022 and 2023. By probing into user account profiles and interactions with this hashtag, this paper investigates the claims surrounding the hashtag’s popularity, by identifying suspicious accounts and their contributions in the trending of the hashtag.

Findings suggest that the claim of the hashtag's unprecedented success was overhyped, further suggesting that the popularity and impact of the social media campaign were exaggerated. Despite high engagement rates, the study indicates a discrepancy between perceived influence and actual impact on public sentiment and political mobilization.

This paper contributes to the literature on social media’s role in political mobilization and agenda-setting in the Pakistani context. More generally, understanding hashtag dynamics and their impact on shaping public opinion, may be beneficial to academics and practitioners in better understanding the role of digital platforms in the politics.

In this study, the authors seek to introduce ways that show that in the age of artificial intelligence (AI), selective dissemination of information (SDI) performance can be greatly enhanced by leveraging AI technologies and algorithms.

AI holds significant potential for the SDI. In the age of AI, SDI can be greatly enhanced by leveraging AI technologies and algorithms. The authors discuss SDI technique used to filter and distribute relevant information to stakeholders based on the pertinent modern literature.

The following conceptual indicators of AI can be utilized for obtaining a better performance measure of SDI: intelligent recommendation systems, natural language processing, automated content classification, contextual understanding, intelligent alert systems, real-time information updates, intelligent alert systems, real-time information updates, adaptive learning, content summarization and synthesis.

The authors propose the general framework in which AI can greatly enhance the performance of SDI but also emphasize that there are challenges to consider. These include ensuring data privacy, avoiding algorithmic biases, ensuring transparency and accountability of AI systems and addressing concerns related to information overload.

The history of online trading began in the 1960s with the emergence of electronic communication networks, which allowed the electronic execution of trades outside traditional exchanges. The internet revolution led to the development of online brokerage platforms such as E*Trade and Schwab, enabling non-institutional investors to participate in the digital trading revolution. These platforms have evolved to serve the retail investor market, eventually adapting to mobile-first and commission-free models, significantly lowering the barriers to entry for financial markets. Platforms like Robinhood and other fintech firms have rapidly gained market share by offering services and products previously unavailable, such as commission-free trades, mobile trading, and novel products such as fractional shares and cryptocurrency investing. This chapter provides an overview of the history of online trading. It also introduces several new developments in fintech and the online trading industry and discusses various controversies and future implications of new technologies.