Business Users Research Articles

Using Ecosystem Design to Help Projects Thrive—Cultivating Systemic Project Success

Emphasizing ethical considerations, community well‐being, and sustainability, designers are challenged to create product solutions that are technically effective while being socially responsible and inclusive.Substantial improvements in business efficiency and user experience can be achieved by employing ecosystem design. The challenge is creating solutions that are technically effective, socially responsible, and inclusive.

Development of Endek Innovation in Encouraging Digitalization of MSMEs

Endek cloth is a cultural heritage on the island of Bali and even an icon of Bali, which was first introduced in Gelgel, Klungkung Regency but is currently experiencing a decline in interest in producing it. This makes this service carried out in order to be able to provide encouragement for innovation to green products, care for the green environment, the use of natural dyes with the utilization of environmental waste, and concern for the environment related to pollution, with the aim of encouraging promotional strategies so that businesses will be sustainable. The method in this service is qualitative observation, involving owners as business actors, workers, and users in obtaining information. The results of this study provide encouragement to innovate more varied products by using environmentally friendly colors from nature, then utilizing organic waste that can produce natural colors so that it will reduce environmental pollution. Green product innovation is also balanced with STP (Segmentation, targeting, and promotion), Tini Zhop after getting support in product innovation into green products also promoted to millennials by promoting to students by conducting online promotions and exhibitions to campuses.

Framework to enable and test conversational assistant for APIs and RPAs

AbstractIn the realm of business automation, conversational assistants are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through application programming interface (APIs) and robotic process automation (RPAs). To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the “build” phase to assist humans in creating skills for digital assistants. As a result, the system does not need to rely on LLMs during conversations with business users, leading to efficient deployment. Along with enabling digital assistants, our system employs LLMs as proxies to simulate human interaction and automatically evaluate the digital assistant's performance. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.

Strategi Komunikasi Pemasaran Milenial Studio Melalui Media Sosial TikTok

As time goes by, TikTok social media now occupies the top rank in the category of most social media usage time. TikTok is widely used by people today because it is easy to access. That's why almost every group has social media. TikTok's reach is very wide so that the information and content created spreads more easily and quickly. Therefore, TikTok social media information technology is widely used by business or company users to promote their products and services. One of the companies in the field of photo and video services is the Milenial Studio photo studio. This research aims to determine the marketing communication strategy for the Milenial Studio photo studio via TikTok social media. The subject of this research was a photo studio located in Karawang and consisted of 4 informants. The data collection techniques used were in-depth interviews, observation, documentation and literature study.The research results show that the marketing communication strategy is very effective and important for the Milenial Studio photo studio to promote and sell its photo and video services. Some of the strategies that have been identified include the marketing mix and what the owner of Milenial Studio himself said was that they also collaborate with schools by providing photo and video services for school yearbooks.

Legal Perspective on Consumer Personal Data Protection in Fintech Services

The development of science and technology has significantly affected the financial transaction system in Indonesia, especially with the emergence of the Fintech business model. The rapid growth of Fintech indicates the need for smart management in order to provide optimal benefits to society. However, the potential misuse of consumers' personal data poses a risk that needs to be considered. This research uses a normative juridical and analytical descriptive approach to understand the legal dynamics in the Fintech industry and legal measures aimed at Safeguarding user data security in Indonesia, in accordance with the provisions of Financial Services Authority Regulation Number 13/Pojk.02/2018. The results show that Fintech business organizers and users have a relationship that is regulated through an electronic agreement. The legal safeguarding of personal data for Fintech user is governed by Financial Services Authority Regulation Number 1/Pojk.07/2013 and other pertinent regulations, addressing aspects such as confidentiality, data security, and the governance and risk management of information technology.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means

The rapid growth in the e-commerce industry demands the development of smarter and more focused marketing strategies. One approach that can be applied is customer segmentation using various features such as Recency, Frequency, and Monetary (RFM), along with machine learning-based clustering methods. The objective of this study is to design and develop a web-based e-commerce customer segmentation application using a combination of RFM features and clustering methods. The study proposes the K-Means algorithm and compares it with K-Medoids and Fuzzy C Means using publicly available e-commerce datasets. Experimental results showed that the K-Means algorithm outperformed K-Medoids and Fuzzy C Means (FCM) based on the Silhouette Score of 0.67305, Davies Bouldin Index of 0.51435, and Calinski Harabasz Index of 5647.89. Through analysis and testing, the designed application has proven effective in grouping customers into relevant segments. These segments are divided into three categories: Loyal, Need Attention, and Promising, visualized in a web-based application dashboard using Streamlit. The developed application allows e-commerce business owners and users from the business, management, and marketing divisions to categorize customers based on transaction data. The results of this study are expected to provide valuable insights to e-commerce management and marketing professionals who are facing increasingly fierce competition.

Knowledge-reinforced explainable next basket recommendation

The next basket recommendation task aims to predict the items in the user’s next basket by modeling the user’s basket sequence. Existing next basket recommendations focus on improving recommendation performance, and most of these methods are black-box models, ignoring the importance of providing explanations to improve user satisfaction. Furthermore, most next basket recommendation methods are designed for consumer users, and few methods are proposed for business user characteristics. To address the above problems, we propose a Knowledge Reinforced Explainable Next Basket Recommendation (KRE-NBR). Specifically, we construct a basket-based knowledge graph and obtain pretrained embeddings of entities that contain rich information of the knowledge graph. To obtain high-quality user predictive vectors, we fuse user pretrained embeddings, user basket sequence level embeddings, and user repurchase embeddings. One highlight of the user repurchase embeddings is that they are able to model business user repurchase behavior. To make the results of next basket recommendations explainable, we use reinforcement learning for path reasoning to find the items recommended in the next basket and generate recommendation explanations at the same time. To the best of our knowledge, this is the first work to provide recommendation explanations for next basket recommendations. Extensive experiments on real datasets show that the recommendation performance of our proposed approach outperforms several state-of-the-art baselines.

Business Data Visualization, Beyond the Boring.

Business uses of charts and visualizations, and by extension business users, are usually considered mundane and boring. But they, too, want to get their audience's attention, emphasize a point they are making, or simply break out of the monotony of the limited palette of common chart types. I believe that there is ample opportunity to develop new approaches and build better tools that go far beyond the current one-size-fits-all approach to creating charts-much more than is currently recognized in the visualization community. The first step is to reexamine our notions of who business users are, and what they actually want and need.

MDAPW3: MDA-based development of blockchain-enabled decentralized applications

Web3.0 Decentralized Application (DApp) is a class of decentralized software in which at least the business logic of the software is implemented using blockchain-based smart contracts. Features such as transparency, decentralized execution environment, no need for a central authority, immutability of data from manipulation, as well as a native transaction-based payment system based on cryptographic tokens are the main advantages of Web3.0 DApps over conventional Web2.0 software in which the business logic and user data are centrally controlled by companies with no transparency. However, the development lifecycle of Web3.0 DApps involves many challenges due to the complexity of blockchain technology and smart contracts as well as the difficulties concerning with the integration of DApp on-chain and off-chain components. To alleviate these challenges, a Model Driven Architecture (MDA) approach for the development of Web3.0 DApps is proposed in this paper that streamlines the development of complex multi-lateral DApps and results in a product that is verifiable, traceable, low-cost, maintainable, less error-prone and in conformance with blockchain platform concepts. Opposed to previous studies in this area that applied MDA only for the development of smart contracts, our proposed MDA-based approach covers the full architecture of Web3.0 DApps: on-chain, off-chain and on-chain/off-chain communication patterns. The method application was demonstrated by implementing a land leasing Dapp where the requirement model (a BPMN choreography model) was transformed into CIM, PIM, and PSM instances successively, and finally, the code-base was generated based on the Ethereum platform technology stack. Epsilon Validation Language (EVL), Epsilon Object Language (EOL), and Epsilon Comparison Language (ECL) were used for the verification/validation of the model instances at each step. Furthermore, by evaluating the quality metrics of the proposed meta-models, we show that they have a better ontology coverage and are more reusable and understandable compared to previous meta-models.

PresAIse, a prescriptive AI solution for enterprise

Prescriptive artificial intelligence (AI) represents a transformative shift in decision-making by offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption of prescriptive AI faces several challenges. One such challenge is caused by the lack of experimental data for many enterprises, making it hard to attribute differences in outcomes to interventions alone. The second pertains to the explainability of AI recommendations, which is crucial for enterprise decision-making settings. The third challenge is contributed by the silos between technologists and business users, hindering effective collaboration. This paper outlines an initiative from IBM Research, PresAIse, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. Leveraging insights from various research papers, the solutions include scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models (LLMs) to bridge the communication gap via a conversation agent. A proof-of-concept demonstrates the solutions’ potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface, democratizing advanced analytics for strategic decision-making.

Research of Factors Affecting Satisfaction in E-Procurement

Theoretical background: Nowadays, the development of digital technologies, including e-service portals in the public procurement, is crucial for improving purchasing processes and expanding markets for companies, including family businesses. Understanding the factors influencing satisfaction with the use of e-procurement is important for optimizing these processes and improving work efficiency. In the context of public procurement, such studies are few. None of the publications found results of research combining the areas of business family, e-procurement, and user experience. This shows that there is a research gap related to the experiences of employees of family businesses and their interactions with e-procurement for public area, especially in the context of the impact of UX on the efficiency and satisfaction of procurement processes in the public sector. Purpose of the article: The main objective of the study, of which results are presented below, is to develop a model of key factors and the relationships between them that influence the efficiency of work in an e-procurement dedicated to public area. Despite the numerous benefits of using e-procurement, there are also many challenges and barriers that can affect the level of satisfaction of employees using these types of solutions, including in the procurement area. Understanding the factors that influence satisfaction and dissatisfaction with the use of e-procurement is crucial for improving procurement processes and work efficiency. In the application area, the research results can be helpful for creators and suppliers of e-procurement, enabling them to tailor their solution to the specific needs of family business employees. Research methods: In order to examine the factors of satisfaction with the use of e-procurement in the public area, a survey was conducted among contractors submitting offers in public procurement procedures. The online survey was sent to over 10,000 business entities, obtaining 406 correctly completed responses, including 109 from family businesses. The analysis of the obtained results allowed, among others, to identify satisfaction factors in the use of e-procurement. Main findings: In a study assessing the use of e-procurement in the public area, respondents rated their digital competences on average at 4.7 on a scale of 1–6, indicating high digital skills. Intuitiveness of use and ease of searching for advertisements turned out to be crucial for satisfaction with e-procurement, which emphasizes the importance of good UX practice in the design of these tools. Respondents also highly rated functions such as automatic encryption of offers and the ability to create an electronic signature, which affects the security and efficiency of purchasing processes. Surprisingly, training for users of e-procurement was considered less important, which may be due to the already high digital competences of the respondents.

Strategic Implementation of Big Data Automation for Wastage Management Reporting Using Analytical Hierarchy Process in The Tobacco Industry

In today's data-driven era, big data automation is crucial, often referred to as "the new oil." Industries, particularly the fast-moving consumer goods (FMCG) sector like the tobacco industry, must undergo digital transformation to stay competitive. The integration of big data automation with reporting processes is significantly correlated, as it can automate repetitive reporting tasks, enhancing efficiency. This automation enables decision-makers to make faster and more accurate decisions. This research focuses on assessing the capacity and factors involved in the collaboration between the operations department and the digital team to automate repetitive reporting processes by integrating big data from various sources such as SAP and Microsoft Forms. The study employs a combination of qualitative and quantitative methods, along with the Analytic Hierarchy Process (AHP), to identify optimal business solutions. Insights from this research prioritize big data automation and reporting projects to meet business needs. Results indicate among four alternative project groups, the Central Data Wastage project is the top priority with a score of 51.7%, followed by SMD Wastage at 25.2%, PMD Wastage at 14.7%, and FMD Wastage at 8.4%. Five stakeholders participated in this research, including a product manager, business user, business analyst, and two developers. These participants contributed to assessing criteria, sub-criteria, and alternative project groups. This research not only helps prioritize projects but also facilitates seamless digitalization within the operations team, fostering synergy with the digital team.

From Data to Decisions: Optimizing Supply Chain Management with Machine Learning-Infused Dashboards

This paper examines how business users can leverage machine learning and data analytics through dashboards to optimize their decision making in demand-side supply chain management. We present a case study of an Austrian B2B hygiene product retailer that needed to provide its top management, sales representatives, and marketing managers with more relevant information to improve business intelligence and to enhance customer acquisition and retention. To generate this information, we utilized various data analysis and machine learning methods, including RFM analysis, market basket analysis, TURF analysis, and demand forecasting, using real-life transaction data. To provide business users with easy access to this information, we developed dashboards that integrate these methods providing an interactive and visual tool for data exploration and understanding. We conclude that dashboards can enable, business users to make better informed and effective decisions on the demand side of supply chains leading to improved sales performance and increased customer satisfaction.

PROGRAMMATIC SOLUTIONS FOR BUSINESSES THAT HAVE MULTIPLE ACCOUNTS

Since ancient times, people have used various types of monetary units to perform various trade transactions.A large amount of money is stored in certain computer systems. Accounting for such units is no less important thanaccounting for physical money, and because of this, banks are developing client applications. However, almost all clientbanks have one significant drawback for business users, namely the lack of a centralized place to track information onall their accounts in different banks. To make sure that a large number of people need to view accounts in different banks,it is enough to look at the statistics of the NBU (National Bank of Ukraine) and UkrStat (State Statistics Service). It is notuncommon for entrepreneurs to open not only several accounts, but also in different banks. Which makes it difficult tokeep records without some kind of system where you can see all transactions in one place. This work is devoted to thedevelopment of a software solution for a business that has several accounts. Also, this development can be used byindividuals who have several accounts in different banks. This work covers some of the practical challenges that abusiness may face and offers one of the solutions to them by creating a desktop application that allows you to monitormultiple bank accounts in one place, providing vital data such as transaction history and recent balances, allowing youto add personal notes

Towards an understanding of multi-generational higher education cohorts in gamified entrepreneurship education

The increasing inclusion of gamified courses in entrepreneurship programmes in higher education have left gaps in understanding the critical essentials of the multi-generational student cohort undertaking these programmes. In this paper, we interrogate the educational experiences of multi-generational higher education students in a core gamified entrepreneurship course in an undergraduate business school programme. The research analyzed 392 course feedback responses from three generations (X, Y and Z) of a multi-generational cohort. The study developed and validated a behaviour-results model for gamified entrepreneurship courses leading to student entrepreneurial intention and entrepreneurial orientation, and disaggregated student engagement into it's multiple dimensions of cognitive, behavioural and emotional. The model also validated six dimensions of individual entrepreneurship orientation. Using the model, the study found differences in the component variables based on student Generations X, Y, and Z. Also, student cognitive and behavioural engagement led to entrepreneurial intention which also influenced student entrepreneurial orientation. There were marked differences in student grit, cognitive engagement, and emotional engagement between Generations X and Z. Furthermore, generational differences existed amongst Generation Z and Y, and also for Generation Z and X in student entrepreneurial intention. The study also confirmed the difference in entrepreneurial orientation between Generations X and Z. Additionally, the study found that there is a need to contextualize student engagement facilitators such as results demonstrability of the business simulation platform, student grit and user characteristics as they have selective effects on student cognition, behavioural and emotional engagements in a multi-generational student cohort of Generation X, Y and Z.

Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach

Competitive analysis in digital and technology is trending in the business field. However, the field of digital and technology in the business world is vast and challenging to analyse. The purpose of this research is first to identify the success factor which represent the company performance. Second, is to identify the significant services provided by the company to their business user. Then, based on the first and second objectives, a predictive modelling is developed to produce the best solution to their business user. The research is implementing a case study from Telecommunication Company and using data science life cycle methodology. The statistical modelling that is used to develop the competitor’s analysis model is generalised linear model (GLM) which integrated with machine learning approach. Furthermore, the synthetic data set is created by using Gamma Distribution, Gaussian Distribution and Poisson Distribution due to some data from the case study is confidential. The synthetic data set is based on existing real data which are from Telecommunication Company sentiment analysis data, were used to investigate the performance of the proposed model. The machine learning technique is used to get the accuracy of the significant GLM which has been developed. The accuracy is tested by using the error rates of the machine learning technique which are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared. This research discovered that the business solution to the significant service for the business user and discovered the best statistical model to be used for the business solution. The results show that the Gumbel distribution is the best fit model for the synthetic dataset where the values of RMSE is 1.0574, MAE is 0.9168 and R-squared is 0.3994, and the significant success factor that has been identified by using the GLM is advertising success factor. The model developed can be improved with another type of data set and different sizes of data. Hence, further studies and real-world data are required for better validation.

МОДЕЛЬ ОБРАЩЕНИЯ ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ НА БАЗЕ ОТЕЧЕСТВЕННОЙ АНАЛИТИЧЕСКОЙ ПЛАТФОРМЫ LOGINOM

Striving to improve the economic efficiency of pharmaceutical business in an unstable market situation and high competition requires the subjects of drug circulation to implement modern approaches to the organization of their activities. In these conditions, an important problem that the management has to solve in the process of circulation of medicines is the problem of quality of source information accompanying the business processes of pharmaceutical activities. The source information is not intended for the purposes of analysis, is presented, as a rule, rather raw material that requires preliminary processing by special methods, including their sampling, purification and transformation. The article discusses the advantages of practical application of the capabilities of Loginom analytical platform, which implements the above mentioned methods of pre-processing of raw material and allows solving the tasks of modeling the characteristics of drug circulation. Based on the concept of low-code architecture that provides visual design of complex data analysis scenarios, without involving IT-specialists and realize programming tasks by the business user. The object of the research was the current data obtained from the accounting system of material assets of a wholesale pharmaceutical organization. When organizing a request to the accounting system, based on the considerations of commercial confidentiality, it was decided to limit the 12-month time period and the assortment group, including only antibiotics. During the research, a data processing scenario was developed in the Loginom environment, representing the sequence of actions that need to be carried out to analyze the data. A processing script is a combination of data processing nodes configured by the developer to solve a specific problem. Each script node performs a separate operation on specific data. The drug circulation model scenario developed during the study, consisting of five nodes, includes two nodes with initial data “Receipt Data” and “Consumption Data”, as well as nodes “Data Combination”, “Sorting” and “Grouping”. The developed drug circulation model is an alternative to modeling based on the popular Excel spreadsheet processor, the capabilities of which are limited by the volume of processed data, low productivity on large volumes, the difficulty of automating the execution of sequences of actions, and the inconvenience of reusing processing scripts.

A typology of evaluative health platforms: Commercial interests and their implications for patient voice

Interactions in the healthcare system today involve an important new set of actors: evaluative health platforms (EHPs). These platforms are not neutral intermediaries, but active moderators of how patients express opinions, choose providers, and consume health-related information. This paper adds to our understanding of the varied and evolving commercial interests of EHPs and the implications these have for patient voice. We analyze 71 platforms in the USA, UK, and Germany and identify five ideal types: subscribers, analyzers, advertisers, regulators, and scammers. Each platform type enacts a unique competitive strategy through an evaluative infrastructure which constrains but also generates possibilities for patient voice. Based on our typology, we develop three contributions. First, we nuance universalizing claims about the consequences of platform capitalism by specifying the diverse strategies underpinning competition between EHPs in different countries, and showing how each strategy leads evaluative infrastructures to develop in ways that impact patient voice. Second, we show how patients can navigate the challenges of a complex EHP space by exercising their ability to choose between platforms. Finally, we outline the conditions platforms need to fulfil to become empowering. Overall, this study highlights the varied and complex relationship between platform business models and user voice, which exists not only in healthcare, but also in many other fields.

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.