Business Scenarios Research Articles

Impact of international entrepreneurial orientation on internationalisation outcome: A mediating role of culture

In Emerging economies, MNCs (Multinational corporations) encounter several issues while devising Strategies to penetrate foreign markets, examining these SMEs’ performance in present times and assessing their internationalisation process is crucial. The purpose of this research is to investigate how international entrepreneurial orientation affects SMEs’ international performance during internationalization, as well as how organizational culture in the Kingdom influences the international performance of these MNCs. To attain this objective (n = 206) MNCs in the Kingdom have adopted internationalisation strategies. Questionnaires were administered as part of a survey approach for this study. To forecast and estimate relationships, partial least squares structural equation modelling (PLS-SEM) was employed. This study indicates that improving internationalization performance, mainly through active participation in foreign markets, is one of the SMEs’ strategies during the internationalization process. The empirical findings demonstrate that international entrepreneurial orientation influences the internationalisation performance of SMEs largely influenced by organisational culture. Previous research shows that the success of SMEs’ internationalization, however, is not directly impacted by their international entrepreneurial orientation. This study supports the significance of organisational culture during internationalisation. This study offers insightful information that motivates policymakers and owner-managers in developing nations, especially in KSA, to build organizational cultures and dynamic capacities that meet the demands of globalization in today’s business scenario.

Game Theory in Business: Strategic Decision-Making for Competitive Advantage

Game theory serves as an analytical framework for understanding strategic interactions among rational decision-makers in business. This paper explores the application of game theory in business strategy, focusing on how companies use game-theoretic models to enhance decision-making and gain a competitive advantage in complex markets. The paper includes case studies of companies across different industries, such as Infosys, competition between the firms like Apple and Samsung in the global smartphone market, Amazon and Flipkart in the e-commerce sector, and Coca-Cola and Pepsi in the soft drinks industry. Each case study demonstrates the relevant use of game theory in real-world business scenarios,and shows how firms use game theory models, such as Nash equilibrium, mixed strategies, and repeated games, to navigate competitive pressures, optimize pricing, and anticipate rival actions. The paper concludes that game theory is an essential tool for strategic planning, allowing firms to anticipate competitor moves and react effectively.

FEASIBILITY ANALYSIS OF INTERNATIONALIZATION OF STARTUPS USING A SYSTEMATIC FRAMEWORK

Internationalization is the process of commercial expansion of a business to other countries, increasing its visibility and sustainable growth. In order to analyze the viability of internationalizing a startup, this case study presents the use of a systematic framework, encompassing analysis of opportunities and partners, market research, planning through a flight plan, cost structuring, pricing definition, and takeoff for execution of the proposal. It is understood that, for startups, internationalization is not only a path to growth, but also a boost for innovation and competitiveness in the global business scenario. The results of the application of the framework highlight the potential for scalability and expansion of startups, and the need for careful planning that considers all the variables of this process. This article contributes to the field of international business management, presenting valuable information for entrepreneurs seeking effective expansion strategies.

Research on BIM sub-model data of construction engineering based on MVD

BIM technology has been widely applied to various stages of construction projects in China. The data required at each stage and scenario of construction projects varies, necessitating the definition of target BIM data according to data needs. When BIM software generates IFC models, different MVD templates can be configured to output BIM sub-models. To study the characteristics of data configuration for different MVDs, this article takes construction projects as an example and designs an analysis experiment for BIM sub-model output based on MVD. It analyzes the differences and characteristics of IFC model data from both the overall project level and the professional component level according to key indicators, with the aim of providing references for construction project personnel to select matching MVDs, ensuring the effectiveness of BIM sub-models, and meeting the needs of business scenario applications.

PREDICTING INSOLVENCY IN BRAZILIAN COMPANIES USING MACHINE LEARNING:

Corporate insolvency prediction serves as a valuable strategic parameter to proactively identify financial and managerial risks. Recent approaches to bankruptcy prediction have predominantly leveraged machine learning algorithms, demonstrating superior predictive accuracy compared to conventional methods. This study highlights the effectiveness of the Random Forest methodology in insolvency assessments. Consequently, an exploratory analysis was conducted to evaluate the feasibility of employing a bankruptcy prediction model within the scope of publicly traded Brazilian companies using machine learning techniques. The sample is made up of companies with delisting status on the B3 stock exchange due to insolvency, between 2005 and 2018, a period in line with the enactment of Brazilian legislation on the subject. Evaluation of the model in Brazilian companies revealed an accuracy of 98% in predicting bankruptcies, highlighting the effectiveness of the Random Forest model as a valuable resource for investors, corporate decision makers and interested parties. This research contributes significantly to the discourse surrounding the adoption of machine learning tools in the field of bankruptcy prediction in the Brazilian business scenario.

Cloud Platform Business Scenario Modeling Based on Multi-Model Fusion Recommendation Algorithm

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

Outsmarting Artificial Intelligence in the Classroom—Incorporating Large Language Model-Based Chatbots into Teaching

ABSTRACT Since the release of ChatGPT in November 2022, large language model-based chatbots have attracted much attention. Although businesses value their potential for efficiency gains, academics are concerned about their effects on learning and assessments. This Case enables instructors to integrate large language model-based chatbots into the curriculum. Students assume the role of a professional accountant who retrieves an artificial intelligence-written text that has to be evaluated and improved before presenting it to a client. The analysis and writing activity help students gain domain-specific accounting knowledge and improve their writing skills. Students also improve their computer literacy as they learn to engage with artificial intelligence effectively, assess generated output, and evaluate output quality. These learnings enable students to judge in which business scenarios chatbots are beneficial and when original human contributions will likely remain superior. Finally, this activity expands other cognitive skills, such as critical thinking and judgment. JEL Classifications: A22; A23; M40; M41; M49.

Power text information extraction based on multi-task learning

In order to improve the analysis and processing speed of power system fault text in actual business scenarios, a power fault text information automatic extraction model based on pre-training and multi-task learning is proposed. The pre-training model is used to learn the context information of power text words, and the first-order and second-order fusion features of words are mined to enhance the representation ability of features. The multi-task learning framework is used to combine the learning of named entity recognition and relationship extraction tasks to achieve mutual complementation and mutual promotion between entity recognition and relationship extraction, thereby improving the performance of power fault text information extraction. Finally, the model is verified by the daily business data of a power grid data center. Compared with other models, the accuracy and recall rate of power fault text entity recognition and relationship extraction are improved.

Metaverse Insights: Research Advancements, Innovation Trends and Industry Implications

The metaverse is a universe of communication across boundaries. The provision of new experiences with the highest level of immersion through virtualisation, a greater degree of flexibility to produce and distribute information, and a space for new social communication all occur here. With the aid of technology and gadgets, people can engage digitally and visually in the virtual environment provided by metaverse. It is considered to be an ‘extension of the internet’ in fact. The metaverse has no restrictions based on location. It is based on an ‘immersive digital environment’ that is unbroken, without constraints and without borders. Although it seems like something out of a science fiction film or book, it is actually the way that communication will be done in the future. This article is an attempt to analyse the selective literature from the Scopus database to form an opinion about what can be the trends of research in metaverse and what are the areas of applicability of this phenomenon in business scenarios. It highlights the research areas mushrooming in the field of metaverse.

STC-BERT (Satellite Traffic Classification-BERT): A Traffic Classification Model for Low-Earth-Orbit Satellite Internet Systems

The low-Earth-orbit satellite internet supports the transmission of multiple business types. With increasing business volume and advancements in encryption technology, the quality of service faces challenges. Traditional models lack flexibility in optimizing network performance and ensuring service quality, particularly showing poor performance in identifying encrypted traffic. Therefore, designing a model that can accurately identify multiple business scenarios as well as encrypted traffic with strong generalization capabilities is a challenging issue to resolve. In this paper, addressing the characteristics of diverse low-Earth-orbit satellite traffic and encryption, the authors propose STC-BERT (satellite traffic classification-BERT). During the pretraining phase, this model learns contextual relationships of large-scale unlabeled traffic data, while in the fine-tuning phase, it utilizes a semantic-enhancement algorithm to highlight the significance of key tokens. Post semantic enhancement, a satellite traffic feature fusion module is introduced to integrate tokens into specific low-dimensional scales and achieve final classification in fully connected layers. The experimental results demonstrate our approach’s outstanding performance compared to other models: achieving 99.31% (0.2%↑) in the USTC-TFC task, 99.49% in the ISCX-VPN task, 98.44% (0.9%↑) in the Cross-Platform task, and 98.19% (0.8%↑) in the CSTNET-TLS1.3 task.

Exploratory Study of Metaverse in Business in Indonesia Using Scenario Planning Approach (Metaverse Case Study in Digital Business & Technology Division PT Telkom Indonesia)

The Metaverse, as the next step after the internet (Web1) and Social Media (Web2), gained attention after Facebook rebranded to Meta in 2021, signaling a focus on its development. While still in its early stages, with limited business applications beyond marketing, this research explores potential metaverse scenarios in Indonesia through PT Telkom’s Digital Business & Technology Division. Using the TAIDA method and a qualitative approach with primary interviews and secondary data, the study highlights Indonesia’s strong potential for metaverse growth, driven by internet adoption, VR/AR technology, cryptocurrency, and government support. Key uncertainties include low adoption, early-stage development, and unclear regulations. Four business scenarios are analyzed, focusing on promotion inefficiency, digital gaming interest, cybersecurity concerns, and potential platform discontinuation. Business scenarios encompass a booming metaverse, metaverse as a game, metaverse as a virtual world threat, and social media dominance. Scenario 1 analysis reveals PT Telkom’s focus on developing metaverse with risks related to promotional inefficiency. Scenario 2 involves developing metaverse’s digital game with the risk of low public interest. Scenario 3 emphasizes cybersecurity with risks related to societal disinterest. Scenario 4 envisions discontinuing metaverse with risks tied to customer misunderstanding. The primary objectives target awareness, cybersecurity, and customer understanding while ensuring platform reliability and regulatory guidance.

Optimal ordering strategy for deteriorating items with maximum lifetime using trade credit financing under imprecise environments

In many business scenarios, a retailer is permitted specific credit period to pay back for the products bought earlier. This facility enables retailers to continue their business operations even when they are unable to raise funds or secure a business loan. To boost the market's demand, promotional effort is a very effective business strategy to the retailer for maximizing the profit. On the other hand, a sudden and significant rise in customer demand for an inventory leads to shortages. Moreover, inventory relevant unit cost coefficients become imprecise due to insufficient data, human error etc. Nowadays, neutrosophic set quantifies the impreciseness more realistically. Considering these facts, an imprecise EOQ model for deteriorating items with maximum lifetime is formulated under trade credit facility. In addition, this article allows shortages, which are linearly time-dependent partially back-logged. Here, the unit cost coefficients are expressed as single-valued trapezoidal neutrosophic numbers. Furthermore, particular models are derived under different environments - intuitionistic, fuzzy, and crisp. Step-by-step solution procedures are suggested for all models to obtain optimal solutions. Models are numerically illustrated with real-life data, and some sensitivity analyses are performed. Managerial insights demonstrate that depletion time always depends on demand. Again, the present study suggests to reduce demand by halting the promotional activities during the shortage period and choose products with a larger lifetime.

A Gnn-Enhanced Ant Colony Optimization for Security Strategy Orchestration

The expansion of Internet of Things (IoT) technology and the rapid increase in data in smart grid business scenarios have led to a need for more dynamic and adaptive security strategies. Traditional static security measures struggle to meet the evolving low-voltage security requirements of state grid systems under this new IoT-driven environment. By incorporating symmetry in metaheuristic algorithms, we can further improve performance and robustness. Symmetrical properties have the potential to lead to more efficient and balanced solutions, improving the overall stability of the grid. We propose a gnn-enhanced ant colony optimization method for orchestrating grid security strategies, which trains across combinatorial optimization problems (COPs) that are representative scenarios in the state grid business scenarios, to learn specific mappings from instances to their heuristic measures. The learned heuristic metrics are embedded into the ant colony optimization (ACO) to generate the optimal security policy adapted to the current security situation. Compared to the ACO and adaptive elite ACO, our method reduces the average time consumption of finding a path within a limited time in the capacitated vehicle routing problem by 67.09% and 66.98%, respectively. Additionally, ablation experiments verify the effectiveness and necessity of the individual functional modules.

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

IDIOMS IN BUSINESS ENGLISH TEXTBOOKS: THE CASE OF MARKET LEADER AND BUSINESS RESULT

Idioms represent an important part of effective communication between native speakers, but they are quite necessary among non-native speakers too. Their crucial role in making communication flow easily makes them attractive and important to be studied. Their use in not very common among non-native speakers, because they are usually taught at higher levels, such as C1 and above. This paper is a systematic analysis of idioms representation in Business English textbooks used in higher education, both Market Leader and Business Result. The focus is in identifying their presence in these textbooks, followed by an analysis and categorization of idiomatic expressions across various units and topics. A thorough approach of their complex nature and connection to real-world business scenarios is taken in order to see the efficacy of their inclusion in the textbooks. The paper contains extractions, which show how these expressions are introduced to students in these two textbooks, and if they are equivalent to the requirements in the Common European Framework of Languages. This research’s objective is to help teachers, educators and curriculum see how idiomatic language is treated in Business English textbooks. Also, the study adds a contribution of raising awareness of the role idioms play in language acquisition and the implications for effective business communication.

Detecting Process Duration Drift Using Gamma Mixture Models in a Left-Truncated and Right-Censored Environment

Within the realm of business context, process duration signifies time spent by customers between successive activities. This temporal perspective offers important insight to customer behavior, highlighting potential bottlenecks, and influencing business management decisions. The distribution of these process duration often changes over time due to factors such as seasonality, emerging legislation, changes to supply chains, and customer demand. Referred to as concept drift, these variations pose challenges for robust process modeling, understanding, and refinement. Subsequently, gamma mixture models are widely employed to model durations. These source data can, however, become left-truncated and right-censored within any specific observation window thereby necessitating a (well-known) modification to the likelihood function. The approach reported in this article leveraged this adapted likelihood across a series of observation windows, applying the likelihood ratio test to identify duration changes/concept drift. Due to its flexibility in modelling any duration distribution, the gamma mixture model was used with Nelder–Mead optimized likelihood for the left-truncated and right-censored data. The number of gamma components was determined by the Bayesian information criterion. The proposed framework underwent validation through simulated exponential samples, leading to recommendations for its practical application. Subsequently, we applied the methodology to three real-life event logs exhibiting diverse characteristics. Experimental results showcase the effectiveness of our approach in terms of data fitting, as compared to Kaplan–Meier curves, and in detecting instances of drift. This comprehensive validation underscores the practical utility and reliability of our framework for dynamic business scenarios.

After-sales service and brand reputation: a case of kitchen appliance industry

PurposeTo build long-term relationships and gain a competitive edge, marketers need to provide customers with unique and distinct experiences that they cannot find in other companies. According to the literature, after-sales service helps to achieve these goals. By modeling the linkages between after-sales service, service quality, customer attitude and purchase intention, this study aims to understand how customers perceive after-sales service in decision-making in kitchen appliance industry.Design/methodology/approachThrough purposive sampling, 324 respondents, primarily female, answered a structured questionnaire about their perception of after-sales service for kitchen appliance products. Previously well-established, validated scale measures from the extant literature were used. The responses were gathered using a seven-point Likert scale.FindingsAccording to the findings, after-sales service quality is vital in kitchen appliance buying decisions. Accordingly, the higher the quality of service perceived by the customer, the more favorable the brand's attitude and purchase intention will be. Additionally, brand reputation was found to be an essential moderator between customer attitude and purchase intention, suggesting that the reputation of the kitchen appliance brand plays a positive and significant role in consumers’ purchase intentions.Originality/valueIt is well known that after-sales service plays a crucial role in current business scenarios, but empirical research on kitchen appliances has been scarce. This study aims to fill a void in the existing literature by investigating the relationships between after-sales service, after-sales service quality, customer attitude and purchase intention in the domain of kitchen appliances.

SecretFlow-SCQL: A Secure Collaborative Query Platform

In the business scenarios at Ant Group, there is a rising demand for collaborative data analysis among multiple institutions, which can promote health insurance, financial services, risk control, and others. However, the increasing concern about privacy issues has led to data silos. Secure Multi-Party Computation (MPC) provides an effective solution for collaborative data analysis, which can utilize data value while ensuring data security. Nevertheless, the performance bottlenecks of MPC and the strong demand for scalability pose great challenges to secure collaborative data analysis frameworks. In this paper, we build a secure collaborative data analysis system SCQL with a general purpose. We design more efficient MPC protocols and relational operators to meet the demand for scalability. In terms of system design, we aim to implement a system with security, usability, and efficiency. We conduct extensive experiments on SCQL to validate our optimization improvements: (1) Our optimized secure sort protocol sorts one million 64-bit data in only 4.5 minutes, 126× faster than EMP (9.4 hours). (2) The end-to-end execution time of the typical vertical scenario query is reduced by 1991× from the state-of-the-art semi-honest collaborative analysis framework Secrecy (rewritten with Additive Secret Sharing protocol), with appropriate security tradeoffs. (3) We test the system in the WAN setting with input size = 10 7 to demonstrate the scalability. We have successfully deployed SCQL to address problems in real-world business scenarios at Ant Group.

Research on the Construction of a Digital Twin System for the Long-Term Service Monitoring of Port Terminals

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations.