Third-party Developers Research Articles

Optimalisasi Partisipasi Anggota Koperasi Mahasiswa Melalui Digitalisasi Layanan Serta Potensi Pengembangan Bisnisnya

The demand to transform conventional systems into ones that utilize digital platforms is inevitable. Digital transformation is crucial, especially in the customer service sector, to facilitate transactions for consumers. The opportunity to undertake digital transformation within cooperatives is wide open due to the rapid technological advancements today. The specific objective of this research is to understand how the digitalization of services conducted by student cooperatives contributes to enhancing member participation, and also to identify the potential for the development of student cooperative businesses. This research is conducted at the Student Cooperative of Economics Education in Kendari City, Southeast Sulawesi. Informants for this study consist of cooperative advisors, management members, cooperative members, and alumni who were members of the cooperative. Data collection for this research was obtained through three methods: observation of the research location, interviews with the informants, and literature review. Data reduction, data presentation, and drawing conclusions are the methods used to analyze the qualitative data obtained from interview results. The research findings reveal that the student cooperative management has taken initiatives to optimize member participation, such as developing the "KopmaKu" application, attempting collaboration with third-party cooperative application developers, and working towards implementing Quick Response Code Indonesian Standard (QRIS) as a payment system. Student cooperative business units have significant potential to provide benefits and opportunities for alumni. These cooperatives serve not only as places for purchasing essential items, but also as platforms for collaboration, empowerment, and connections between students and alumni. The student cooperative business units can serve as a strong bridge between students and alumni. Alumni can collaborate with students in various aspects, including the provision of products or services, training, and even funding.

An economic test for an unlawful agreement to adopt a third-party’s pricing algorithm

SUMMARY Artificial intelligence has helped fuel a growing market in the supply of pricing algorithms by software developers. While there is an efficiency rationale for outsourcing pricing, anticompetitive concerns have been expressed when competitors in a market adopt the same pricing algorithm. These concerns have resulted in private litigation claiming a third-party company (who developed the pricing algorithm) and firms (who adopted it) had an unlawful agreement. This study develops an empirical test for determining whether firms’ adoption decisions are coordinated. If adoption decisions are coordinated then adopters’ average price is increasing in the number of adopting firms, while if adoption decisions are independent then adopters’ average price does not depend on the number of adopting firms. This test could provide economic evidence to support a claim of an unlawful agreement between a third-party developer and adopting firms.

The optimal knowledge-sharing strategy for digital platform owners and third-party developers

The optimal knowledge-sharing strategy for digital platform owners and third-party developers

End-Users Know Best: Identifying Undesired Behavior of Alexa Skills Through User Review Analysis

The Amazon Alexa marketplace has grown rapidly in recent years due to third-party developers creating large amounts of content and publishing directly to a skills store. Despite the growth of the Amazon Alexa skills store, there have been several reported security and usability concerns, which may not be identified during the vetting phase. However, user reviews can offer valuable insights into the security & privacy, quality, and usability of the skills. To better understand the effects of these problematic skills on end-users, we introduce ReviewTracker, a tool capable of discerning and classifying semantically negative user reviews to identify likely malicious, policy violating, or malfunctioning behavior on Alexa skills. ReviewTracker employs a pre-trained FastText classifier to identify different undesired skill behaviors. We collected over 700,000 user reviews spanning 6 years with more than 200,000 negative sentiment reviews. ReviewTracker was able to identify 17,820 reviews reporting violations related to Alexa policy requirements across 2,813 skills, and 131,855 reviews highlighting different types of user frustrations associated with 9,294 skills. In addition, we developed a dynamic skill testing framework using ChatGPT to conduct two distinct types of tests on Alexa skills: one using a software-based simulation for interaction to explore the actual behaviors of skills and another through actual voice commands to understand the potential factors causing discrepancies between intended skill functionalities and user experiences. Based on the number of the undesired skill behavior reviews, we tested the top identified problematic skills and detected more than 228 skills violating at least one policy requirement. Our results demonstrate that user reviews could serve as a valuable means to identify undesired skill behaviors.

A Multilayer Architecture towards the Development and Distribution of Multimodal Interface Applications on the Edge.

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly.

Third-party application developers and the liminal space experience during digital entrepreneurship development

PurposeThe study aims to embrace the lingering call for more empirical studies that can theorize the role of digital platforms in digital entrepreneurship. Hence, this study seeks to reveal the liminal space entrepreneurial experience of third-party application developers, by investigating how the platform boundary resources promote third-party entrepreneurial actions, as they transition through the disoriented, uncertain and ambiguous processes of digital entrepreneurship development.Design/methodology/approachTo conduct this investigation, an expert interview qualitative method was used. This approach is a well-established technique in the field of social sciences, which allowed a detailed exploration of the theory of liminality. Liminality refers to the transitional phase that individuals or groups experience when moving from one social or cultural context to another. The expert interview method is appropriate for this study because it involves engaging with knowledgeable individuals who have extensive experience and expertise in the subject area being investigated. Through in-depth and unstructured interviews, the experts were able to provide valuable insights and perspectives about the phenomenon investigated.FindingsThe research findings demonstrate that digital platform boundary resources play a significant role in the behaviour of third-party developers’ who engage in the development of digital entrepreneurship in today’s market. The study highlights three ways that show how these resources (software development kit (SDK), API, integrated development environment (IDE), libraries, frameworks) enable third-party developers to create new applications that are used to pursue entrepreneurship in a digital platform, leading to increased user engagement and revenue generation.Originality/valueThe research addresses the critical roles of digital platform boundary resources in digital entrepreneurship development processes. Also, using liminality theory, the research explicated the core experiences of third-party developers as they navigated the challenges and ambiguities experienced in the pursuit of entrepreneurship. Thus, contributing to the existing body of knowledge in literature and practice.

Tracing the evolutionary leaps and boundaries of digital platforms: a case study of Facebook

ABSTRACT Digital platforms exert a significant influence on the creation and utilisation of products and services across the globe. Past research has shed light on digital platform establishment, strategies for growing platforms’ scale and scope, as well as the role of boundary resources in stimulating third-party development. However, we lack insight into the interaction of growth in scale and scope as traditional digital solutions are transformed into emergent platforms, and how such platforms over time evolve beyond the platform metaphor. .In this paper, we seek to understand the emergence, evolution, and boundaries of digital platforms over time through a longitudinal case study of Facebook. For this, we utilise Facebook’s publicly available documentation of its evolution as digital trace data. We discover that from 2004 to 2019, Facebook underwent four distinct evolutionary stages: interaction, integration, interconnection, and implantation. These stages were facilitated by two novel roles for digital platform boundary resources that we term as distributing and centring. Based on these findings, we theorize the role of digital artefacts’ material qualities in their evolution from applications, via platforms, to information infrastructures. The paper contributes a model explicating this traversal as well as the subsequent unbundling to a new entity repeating the same traversal.

Measuring privacy policy compliance in the Alexa ecosystem: In-depth analysis

Virtual Personal Assistant (VPA) services such as Amazon Alexa are quickly and seamlessly integrating into people’s daily lives. The Alexa ecosystem allows third-party developers to build new skills and publish them to the skill market. These skills can be either standalone skills, operating independently without external linking, or skills with account linking, necessitating the user connect to external services to access the skill’s functionality. Skill developers are required to provide privacy policies to disclose their skills’ data practices. The popularity of these skills raises privacy concerns in data handling practices. Privacy policy documents play an important role in addressing users’ privacy concerns and informing them about the data practices. These documents are complex for users to comprehend, and skill developers may intentionally or unintentionally fail to comply. Previous investigations have predominantly focused on scrutinizing the privacy policies of standalone skills, overlooking those associated with companion services and developers with multiple skills. This study aims to bridge this gap by examining the privacy policies of both the skills and their companion services, along with multiple skills published by the same developer, to explore potential differences in data practices. We conduct the first study on the Alexa ecosystem for skills with account linking, where we compare the policy of the skills and their companion services. We automatically extract the data types from both privacy policies for comparison using a machine learning technique. Mismatches between skill and companion service privacy practices were unveiled, with 975 instances of data type collection mismatches between skills and their companion services, along with 692 instances of data type sharing mismatches. We uncover differences in privacy practices among developers; 13 developers publish skills in different categories and three of them employ different privacy policies on their published skills. Among 35 developers who publish skills in the same category ten provide different privacy policies.

Let’s join forces: boundary resources as enablers of value co-creation in e-commerce ecosystems

AbstractCollaboration and value co-creation are important drivers of the continuous growth of e-commerce, which is expected to reach US $6.4 trillion in 2024 despite current global crises. Only a few transaction platforms currently dominate e-commerce (eg., Amazon, Walmart), but other participants are likely to join these platforms’ ecosystems. Third-party developers can provide extensions to these ecosystems to enhance the platforms’ functionality, but third-party developers’ role in e-commerce ecosystems’ success and generativity remains underexamined in academia. The present study scrutinizes the efficacy of boundary resources in attracting and managing third-party developers in e-commerce ecosystems. This investigation is predicated upon qualitative data gathered through interviews with 14 domain experts. The insights derived from these interviews have culminated in the formulation of seven design principles. These design principles are envisaged to serve as a guiding framework for owners of innovation and transaction platforms within the e-commerce sphere, facilitating the strategic deployment of boundary resources. It is anticipated that collaboration, value creation, and the overall generative capacity as well as the success of e-commerce ecosystems shall be considerably enhanced.

The Effect of Digital Platform Strategies on Firm Value in the Banking Industry

ABSTRACT After digital platforms have become successful in the information technology (IT) industry, incumbents from traditional industries increasingly implement digital platform strategies. However, there is mixed evidence on whether these incumbents benefit from digital platform strategies. To provide systematic insights, we focus on the banking industry. With the emergence of open banking, banks have begun implementing digital platforms to unlock the innovative power of third-party developers. We conducted an event study based on the announcement of digital platform strategies in a global sample of 165 banks. We show that, on average, investors react positively to the announcements. Contrary to our expectations, this effect is more substantial for banks from emerging markets than those from developed markets. Prior artificial intelligence (AI) orientation only partly contributes to investors’ favorable perception of a digital platform strategy. These results point to the complex interplay of AI orientation and digital platform strategies, yielding questions for future research.

A distributed collaborative platform for multi-stakeholder multi-level management of renovation projects

To meet European energy-saving and decarbonisation goals, the annual renovation rate of buildings requires to be at least twice its current level; an aspiration to which the use of innovative and automated solutions can contribute. This paper presents such a solution, the RINNO Retrofitting Manager (RRM) which is part of a large, ambitious research and development project (RINNO) that aims to provide an augmented intelligence-enabled framework for deep, energy-focused retrofitting of buildings. The RRM uses web-service technologies to rationalise the retrofitting process and optimise the delivery of renovation works, while making data readily accessible through an integrated set of role-based user interfaces. The RRM is designed and developed as an open distributed system, that is extensible and portable, by implementing a collaborative research and development approach. The RRM platform implements a multi-level, multi-stakeholder planning approach. It addresses the dearth, insufficiency, and isolation of existing renovation tools by enhancing collaboration, interoperability, and data security, and avoiding information loss and misunderstanding. Employing the Unified Theory of Acceptance and Use of Technology (UTAUT) model, tests conducted with users from independent construction organisations confirmed the RRM's satisfactory performance, ease of deployment, and overall suitability for the management of renovation projects. While this research provides a free collaborative platform for managing renovation projects that can be used by all building retrofit stakeholders in Europe, it also introduces a set of web-services that can be easily reused by third-party developers and integrated into their software tools.

ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ РОЗУМНОГО ГОДИННИКА ДЛЯ САМОСТІЙНИХ ЗАНЯТЬ СПОРТОМ ТА ФІТНЕСОМ

According to available statistics, the most popular smart watch among users is the Apple Watch. Today, there are more than 100 million unique users of this device, 75% of whom use it for sports. With a wide array of different sensors to track a user's physical parameters, neither Apple nor third-party developers have yet developed software to systematize all the collected data to improve an athlete's physical pa-rameters and achieve personal athletic goals. The purpose of the research is to find the possibility of improving the physical parameters of a novice athlete by means of a comprehensive analysis of his activity data collected by a smart watch and creating more personalized recommendations during training based on this data. There are many fitness apps available for the Apple Watch, each with its own unique features and features. Unfortunately, none of the analyzed applications pro-vide sufficient information regarding the correct execution of training and the collec-tion of indicators, which is a disadvantage for users who want to do sports without risks to their health. One of the main challenges of software development for smartwatches is the lack of extensive monetization opportunities. This led to the lack of interest of large companies and teams in this type of software. Small screen size and limited process-ing power compared to desktop or mobile devices are also issues. This means that developers must optimize both the user interface and the algorithms of their software product to increase the speed of the application. Another challenge is the diversity of the smartwatch market, where different devices run on different operating systems and have different hardware specifications. You have to consider the specifics and limitations of each device and platform and make sure that your apps are optimized for each of them, or focus on building your software products for only a limited num-ber of devices. Due to the lack of tools for developing applications for several platforms at once, the path of developing an application for only one platform - for WatchOS - was chosen. Also, one of the goals is to create an application that is completely autono-mous from a smartphone. For this, a simple and minimalistic interface and a simple algorithm for analyzing training data have been developed. The proposed application is focused on running. Statistics will be collected dur-ing the first few training sessions. After starting the activity, the application will start displaying all the standard information, such as pace, heart rate, activity time, dis-tance traveled. This data comes from the sensors and modules of the smart watch. When basic training statistics are collected, the app will begin to display tips on how to continue training.

Optimal integration decision of industrial internet platform for third-party components in two-sided networks

PurposeWith the development of digitalization and interconnection, there is a growing need for enterprise customers to ensure the compatibility of the third-party components they are using in the manufacturing process, thus raising the integration requirements for the Industrial Internet platform and its third-party developers. Therefore, our study investigates the optimal integration decision of the Industrial Internet platform while considering its access price, the integration cost, and the net utility derived by enterprise customers from the third-party components.Design/methodology/approachWe model a two-sided Industrial Internet platform that connects customers on the demand side to the developers on the supply side. We then explore the integration decision of the Industrial Internet platform and its important factors by solving the optimal profit function.FindingsFirst, despite the high integration cost of third-party developers, the platform still chooses to integrate when enterprise customers derive high utility from the third-party components. Second, due to the compatibility effect, charging the enterprise customers a higher price may reduce the platform profits when these customers derive low utility from the third-party components. Third, the platform profits will increase along with the integration cost of third-party developers when it is low in the case where enterprise customers derive low utility from third-party components.Originality/valueOur findings offer insightful takeaways for the Industrial Internet platform when making integration decisions.

Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence

Critical scholars contend that ‘There is no AI without Big Tech’. This study delves into the substantial role played by major technology conglomerates, including Amazon, Microsoft, and Google (Alphabet), in the ‘industrialisation of artificial intelligence’. This concept encapsulates the shift of AI technologies from the research and development stage to practical, real-world applications across diverse industry sectors, resulting in new dependencies and associated investments. We employ the term ‘Big AI’ to encapsulate the structural convergence of AI and Big Tech, characterised by the profound interdependence of AI with the infrastructure, resources, and investments of these major technology companies. Using a ‘technographic’ approach, our study scrutinises the infrastructural support and investments of Big Tech in the AI sector, focussing on corporate partnerships, acquisitions, and financial investments. Additionally, we conduct a detailed examination of the complete spectrum of cloud platform products and services offered by Amazon, Microsoft, and Google. We demonstrate that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack. Significantly, these tech giants have seamlessly integrated all three components of the stack into their cloud offerings. Furthermore, they have developed industry-focussed solutions and marketplaces aimed at attracting third-party developers and businesses, fostering the growth of a broader AI ecosystem. This analysis underscores the intricate interdependence between AI and cloud infrastructure, emphasising the industry-specific aspects of cloud AI.

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

This article presents the design, implementation, and evaluation of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLSys</b> , a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps. FLSys is designed to work on smart phones with mobile sensing data. It balances model performance with resource consumption, tolerates communication failures, and achieves scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. FLSys adopts a modular design and is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) model. HAR sensing data was collected in the wild from 100+ college students during a 4-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed data. A sentiment analysis model is also used to demonstrate that FLSys effectively supports concurrent models. This article reports our experience and lessons learned from conducting extensive experiments using simulations, Android/Linux emulations, and Android phones that demonstrate FLSys achieves good model utility and practical system performance.

BIG AI: THE CLOUD AS MARKETPLACE AND INFRASTRUCTURE

Cloud infrastructure platforms underpin most of today’s internet, functioning as the operating system of the internet and representing the most important source of revenue for Big Tech companies. While most of the current hype around (“generative”) AI is focused on specific successful products and initiatives like OpenAI (ChatGPT, DALL·E 2) and Stability AI (Stable Diffusion), they would not have been possible without the significant infrastructural support and investments from Big Tech companies. This paper critically examines what we call Big AI, or those types and deployments of AI that simply would not be feasible or even possible without the infrastructural support, partnerships, or investments provided by Big Tech companies. To account for this, we articulate the key components of AI and how they are connected. By focusing on Big Tech’s own products and service offerings, third-party applications, and models, we gain a more comprehensive understanding of what Big AI is, or looks like today, and what it may become in the years to come—for which the infrastructure is being made right now. Further, we make a distinction between the cloud platform products and service offerings from Big Tech (i.e. the cloud as an infrastructure for AI) and Big Tech as the host or provided of marketplaces for diverse (AI-based) products and services from third-party businesses and developers (i.e. the cloud as a marketplace for AI). Overall, this research provides the basis for a better understanding of the critical political economy of (Big) AI.

An institutional perspective on application programming interface development and integration

PurposeDigital 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThis 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.

Feasibility and Implementation of an out-of-Pocket Cost Calculator for Pre-Treatment Financial Counselling in Cancer Patients

Introduction Public healthcare in Singapore is funded by a combination of subsidies, insurance, which includes national basic coverage (MediShield, MSL) and optional private coverage, pensions (MediSave, MSV) and out of pocket (OOP) costs. In Sep 2022, spiraling cancer costs led to a shift in national policy with the introduction of a Cancer Drug List (CDL) of cost-effective treatments. OOP costs are now determined by multiple factors such as subsidy status, financial resources, insurance claim limits, residency status, drug subsidy tier, therapeutic indication, dose and cost. To simplify this complex manual process, we developed ChemoCalc, a digital calculator for individualized OOP estimates. We hypothesize that it will increase OOP cost estimate accuracy, improve efficiency and have high uptake. Methods The study was performed in a Singapore tertiary cancer center. ChemoCalc was developed with a multi-disciplinary team comprising physicians, pharmacists, operations administrators, patient service associates and medical social workers. Data sources were consolidated and hierarchical logic determined for calculating regimen and patient-specific OOP cost. Data sources included the CDL, hospital formulary, chemotherapy protocols, chemotherapy facility fees and various national subsidy schemes. These were encoded into an algorithm by a third-party developer. The user interface and experience (UI/UX) was envisioned by the team with iterations based on feasibility testing. FCs were trained on using ChemoCalc and data was collected pre and post-ChemoCalc implementation. Feasibility outcomes were demand, acceptability, practicality and integration (the extent to which ChemoCalc could be deployed in a secure hospital IT environment). These were assessed through employee focus groups (FG). Reporting officers were not present during the FG. Implementation outcomes were adoption (percentage of eligible users using ChemoCalc for FC), engagement (percentage of returning users), efficiency (median time taken to perform FC) and accuracy (percentage of bills which are within ±10% of the FC estimate). For accuracy, 10 bills were randomly selected each month and compared against the pre-treatment FC. Results Prior to the development of ChemoCalc, the demand for automated FC was identified through employee engagement. ChemoCalc (Figure 1) was developed and iterated from Aug - Sep 2022. Feasibility assessment identified that simplicity underscored a good UI/UX. To reduce UI clutter and improve ease-of-use, an algorithm was encoded to ensure that only variables relevant to a particular regimen need to be entered (Figure 2). All data fields were accessible via the hospital electronic medical record. The final version of ChemoCalc was deemed acceptable and practical by FG. It was integrated into the hospital environment seamlessly and accessible from all hospital computers and personal mobile devices. Data collection for implementation outcomes took place from 1 Oct 2022 to 31 Mar 2023. ChemoCalc was used by 72 different members of staff from all job groups. On average, the prices of 2,484 regimens (mean 11 regimens per patient) were generated each month, with a total of 17,385 price estimates generated. 91% of price estimates were for subsidized patients and 9% for private patients. For staff performing FC, the adoption and engagement rate was 100%. ChemoCalc improved efficiency for the FC process with an accuracy of 95% (baseline 35%, 270% increase). Time spent computing OOP reduced from a median of 8 min (range 4.5 - 13) to 44 seconds (range 15 - 20) for simple regimens, and 23 min (range 15.5 - 44) to 1 min (range 0.3 - 1.3) for complex regimens respectively. Due to the convenience and simplicity, there was 100% uptake for pre-treatment FC, as staff have been able to process more FC requests and spend more time addressing patients' financial concerns. It has also allowed patients to compare costs of different treatment regimens. Conclusion The implementation of ChemoCalc was successful with 100% adoption rate and significantly improved efficiency and accuracy. ChemoCalc's algorithm can easily be adjusted to accommodate country- or institution-specific reimbursements for implementation elsewhere. Further studies are required to investigate if pre-treatment FC addresses financial toxicity or influences healthcare costs.

Analyzing the Impact of Open Data Ecosystems and Standardized Interfaces on Product Development and Innovation

The financial services industry is undergoing a seismic shift, driven by the convergence of technological advancements, regulatory mandates, and changing consumer expectations. Central to this transformation is the emergence of Open Banking, a strategic initiative reshaping traditional banking paradigms by advocating for collaboration, interoperability, and customer-centricity. Open Banking, fundamentally facilitated through standardized Application Programming Interfaces (APIs), enables secure and seamless sharing of customer-permissioned financial data and services between traditional financial institutions and external third-party developers. Concurrently, the API economy has emerged as a critical enabler of this collaborative model, offering a standardized interface that underpins the integration of diverse systems, allowing for the efficient exchange of data and functionality. This scholarly article delves into the intertwined realms of Open Banking and the API economy, aiming to elucidate their collective role in propelling collaborative financial services innovation. Open Banking serves as a pivotal force, affording financial institutions a comprehensive view of their customers' financial landscapes, thereby fostering the creation of tailored, responsive, and innovative financial solutions. Complementing this, the API economy acts as the technical scaffolding that enables seamless data sharing and integration, engendering a more connected, agile, and innovative financial ecosystem. By tracing the evolution of Open Banking from its origins to its present-day global presence, this research highlights its transformative potential, emphasizing its capacity to spur competition, drive innovation, and enhance customer experiences. Moreover, the significance of the API economy is analyzed, shedding light on its pivotal role in establishing open, standardized interfaces, thereby promoting a culture of collaboration and innovation. In conclusion, this article envisions a future where the symbiotic relationship between Open Banking and the API economy fuels an era of collaborative financial services innovation, ultimately leading to a more inclusive, efficient, and customer-centric financial landscape

Impact of third-party online payment systems on trade, export, import, and internet retailing

&lt;p style="text-align: justify;"&gt;&lt;span style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;This study estimates the impact of third-party online payment systems on trade, export, import, and Internet retailing in China. Also, the study examines the financial development mechanism via which third-party payment affects trade, export, import, and Internet retailing in China. By employing a sample of 31 provinces in China covering 2011 to 2018, the findings indicate that, third-party payment drives China&amp;rsquo;s trade (0.43%), export (0.42%), import (0.47%), and Internet retailing (0.24%). The study also finds that third-party payment complements financial development to positively affect trade, export, import, and Internet retailing in China. The study recommends policymakers promote third-party payment development in China to boost trade, export, import, and Internet retailing.&lt;/span&gt;&lt;/p&gt;