Real-world Company Research Articles

The Role of Social Media in Promoting Sustainable Brands: Influencing Consumer Perceptions and Behavior Towards Sustainable Products

Sustainability has become a primary concern for consumers and businesses, and the need for brands to communicate their environmental initiatives effectively has significantly increased. Social media platforms have become powerful tools for shaping consumer perceptions and behaviors toward sustainable products. This study investigates how social media influences consumer trust, engagement, and purchase intentions related to sustainable brands. The study uses a mixed-method approach that includes qualitative meta-analysis, case studies, and a quantitative online survey. The study examines how transparency, influencer marketing, and consumer engagement on social media impact consumer attitudes and behaviors. Results indicate significant correlations between social media transparency and purchase intentions. According to the study results, influencer credibility is crucial in driving engagement with sustainable products. Case studies of brands such as Patagonia, IKEA, Unilever, and Tesla demonstrate how real-world companies use social media to promote sustainability, which further confirms the relevance of the study's findings.

Revolutionizing Network Intelligence: Innovative Data Mining and Learning Approaches for Knowledge Management in Next-Generation Networks

The Information and Communication (ICT) industry, a global giant among service sectors, is known for both its massive scale and its unforgiving demands. Here, downtime is unacceptable, requiring constant high availability – often at the stringent Sigma Six standard. Redundancy is a common solution, but it comes at a cost. To meet these demands proactively, the ability to predict load and growth becomes crucial. This project aims to develop a prototype, or proof of concept, that utilizes data mining to provide early warnings and growth forecasts for the ICT industry with good accuracy. Big data is key to making discoveries in any data analysis project. Normally, this data comes from real-time system logs. However, for this initial test, I used a dataset called MIT Reality Mining. This dataset is useful because real-world companies, especially in the tech industry (ICT), are often hesitant to share their current information. By using MIT Reality Mining, I could still find trends and potential reasons behind them in the ICT industry. It's important to remember that this is a limited functionality prototype. While it can serve as a guideline for Telcos looking to implement data warehouses, the actual implementation details will need to adapt to the specific needs of each industry.

Towards business use cases of process mining in product development and manufacturing: Deriving and classifying 18 application scenarios in industry

Process mining (PM) allows organizations to analyse their business processes based on data. This data is readily available in IT-systems, such es Enterprise Resource Planning (ERP) or Product Lifecycle Management (PLM). However, a wide variety of analysis techniques in PM lower the clarity for organizations how to exactly utilize PM within their product development or manufacturing processes. In this paper, we identify 18 application scenarios for PM in product development and manufacturing in collaboration with two real world companies. Each application scenario is classified based on standard criteria from literature, such as business benefits, analysis techniques or improvement possibility. We opt to use the term application scenario instead of use case, because we find that the term PM use case is not uniformly defined in literature. Hence, we propose a framework that relates the proposed application scenarios to the existing body of knowledge. From a managerial perspective, we provide organizations with the possibility to gain new ideas for utilizing PM within their product development or manufacturing. From a scientific perspective, we demonstrate the need for more sophisticated use case structures and characterization elements, especially for more complex domains like product development and manufacturing.

Finding an optimal distribution strategy path in an unpredictable environment

Purpose: This article introduces an innovative method designed to optimize distribution strategies with respect to future uncertainty. It goes beyond the limitations of traditional scenario-based planning that often leads to suboptimal strategies due to the unpredictability of future developments and the challenge of accurately assigning probabilities to these scenarios. Consequently, the method allows selection of the most economically viable future strategy. Methodology: Our methodology diverges from conventional approaches by refraining from making rigid assumptions about the probabilities of future scenarios. Instead, it comprehensively explores the entire allowable probability space to identify an optimal strategy that works well in possible future developments. We employed this method in the case study of a real-world company based in Czechia, where we devised three viable distribution strategies and four model development scenarios. Results: The application of our method demonstrated its effectiveness in selecting the most advantageous strategy, as evidenced by the results of our case study. However, the applicability of the method is contingent upon the accurate definition of potential future scenarios and the evaluation of the performance of different strategies within these scenarios. Conclusion: Our findings suggest that this approach significantly enhances strategic planning under uncertainty. Future research will seek to refine this method further by integrating causal relationships to convey additional information across different model periods, thereby improving the robustness and applicability of the strategy selection process.

Key determinants to supply chain resilience to face pandemic disruption: An interpretive triple helix framework.

Today, supply chain (SC) networks are facing more disruptions compared to the past. While disruptions are rare, they can have catastrophic long-term economic or societal repercussions, and the recovery processes can be lengthy. These can tremendously affect the SC and make it vulnerable, as observed during the COVID-19 pandemic. The identification of these concerns has prompted the demand for improved disruption management by developing resilient, agile, and adaptive SC. The aim of this study is to introduce an assessment framework for prioritizing and evaluating the determinants to supply chain resilience (SCR). To analyze the empirical data, fuzzy criteria importance through intercriteria correlation (fuzzy CRITIC) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) have been incorporated. Fuzzy CRITIC method was used to identify the critical determinants and fuzzy TOPSIS method was applied for determining relative ranking of some real-world companies. Finally, by developing propositions an interpretive triple helix framework was proposed to achieve SCR. This research stands out for its originality in both methodology and implications. By introducing the novel combination of Fuzzy CRITIC and Fuzzy TOPSIS in the assessment of determinants to SCR and applying these determinants with the help of interpretive triple helix framework to establish a resilient SC, this study offers a unique and valuable contribution to the field of SCR. The key findings suggest that 'Responsiveness' followed by 'Managerial coordination and information integration' are the most significant determinant to achieve SCR. The outcome of this work can assist the managers to achieve SCR with improved agility and adaptivity.

Segment Reporting and Segment Profitability: A Case Study on General Electric

ABSTRACT This case study uses segment data from General Electric’s SEC filings to provide students in managerial and cost accounting courses the opportunity to see how a variety of classroom topics apply to a real-world company. Students are required to find relevant segment-related data in 10-Ks. By using financial accounting data to make managerial accounting decisions, the case serves as a link between financial and managerial accounting. This case has four independent requirements that focus on a different topic related to segment reporting: (1) segment performance measures, (2) segment profitability, (3) segment decision-making, and (4) segment reporting changes as connected to corporate structure and strategy. In each requirement, students are asked to prepare a memo and supporting analysis based on the relevant segment-related data. The modular nature of the case provides instructors with flexibility for implementation in a variety of managerial and cost accounting courses at the undergraduate and graduate levels. JEL Classifications: M40; M41.

Enhancing corporate readiness for and resilience to future public health threats, development and deployment of the public health readiness and resilience (PHRR) assessment tool.

As the world suffered through the COVID-19 pandemic, it is increasingly clear that the health of populations is foundational to a high-functioning economy, corporate well-being, and a core driver of social justice. Thus, companies need to understand how to become more resilient to current and future threats. This study (1) explored dimensions of resilience from a public health risk-specific lens and reviewed existing evaluation tools and frameworks to develop a methodology and framework (Public Health Readiness and Resilience-PHRR Assessment Tool) for organizations; and (2) leveraged the framework to evaluate a sample of large corporations to validate the insights the tool can provide, confirm functionality, and evaluate the ability to leverage publicly available vs. propriety data to complete the assessment. We conducted a non-exhaustive search for relevant indices using key word searches and cascade sampling. For the initial review of indices (n = 24), the team evaluated each document based on predefined criteria. Gaps identified in the available indices informed the development of the PHRR assessment tool. The tool was then used to examine real-world companies (n = 22) from eight different industries. Findings from the PHRR tool illustrated variation in readiness and resilience as well as the availability of data. Approximately half of the companies analyzed (n = 11) indicated high levels of potential resilience and readiness with significant data available. Leveraging the PHRR Assessment Tool can inform investments and cross-sector partnerships that enhance companies' readiness and resilience to a variety of public health threats. Additional research is needed to further validate this tool.

빅데이터 분석을 통한 메타버스 동향 및 메타버스 공연으로서의 발전 가능성 연구

The purpose of this study was to study the trends of metaverse through big data analysis and to gauge the possibility of the development of metaverse performances in the field of culture and arts as a practical study of convergence science in the future. To this end, big data analysis was performed on Textom using metaverse as a keyword. Data were extracted and collected from portals and SNS that can be analyzed within Textom such as Naver, Daum, and Google. The data analysis period was divided into two periods, and the first period was set from October 13, 2020 to October 13, 2021, and the second period from November 14, 2021 to November 14, 2022. It was confirmed that metaverse, which was concentrated only in the game industry, is affecting companies, space, time, market, investment, and education, which are real-world companies. Through the UCINET matrix, it was found that the keywords related to metaverse performance, “Virtual Avatar” and “Entertech,” are emerging new topics related to metaverse, and through detailed sensitivity analysis, they are gradually increasing their favorability, but they are also rejected and skeptical.

Digital transformation of the maritime industry: A cybersecurity systemic approach

Information system solutions are increasingly being applied to the maritime industry, and eventually, all aspects of maritime operations will be aided by the digital transformation of the industry. The maritime industry is a sector driven by compliance and historically deals with security and safety matters at an international level. It has been recognized that safety and security in maritime heavily depend on cyber systems and cybersecurity implementation requirements have started to get integrated into maritime's regulatory context. This paper focuses on cybersecurity in the maritime industry, presenting an inside view of maritime cybersecurity aspects and offering a detailed case study analysis based on a real-world company's approach. The main objective of the paper is, therefore, to connect research with practice, presenting a maritime company's cybersecurity systemic approach with references to procedures and policies. The findings of the case study and the cyber security vessel audit survey that is performed show that the company was able to assess its current status, collect evidence and objectively determine security gaps, and achieve cyber risks mitigation. The gained knowledge will be used in the future to continuously improve the company's systems and move to a more predictive and proactive maturity level.

Digital business model configurations in the travel industry

This study is amongst the first applications of digital business models (BMs) research to the travel industry. A systematic and comprehensive taxonomy of digital BM configurations in the travel industry is developed, supported by examples of real-world companies. Based on qualitative research, 53 digital BMs are identified and classified based on primary value drivers, including 10 novel configurations that are absent from previous studies. The paper contributes to framing digital BM configurations in the travel industry and supports establishing a common understanding among scholars. From the practical side, this study offers templates for building or transforming BMs and could serve as a guide to the current digital travel business landscape.

Finite Mixture Models for Clustering Auto-Correlated Sales Series Data Influenced by Promotions

The focus of the present paper is on clustering, namely the problem of finding distinct groups in a dataset so that each group consists of similar observations. We consider the finite mixtures of regression models, given their flexibility in modeling heterogeneous time series. Our study aims to implement a novel approach, which fits mixture models based on the spline and polynomial regression in the case of auto-correlated data, to cluster time series in an unsupervised machine learning framework. Given the assumption of auto-correlated data and the usage of exogenous variables in the mixture model, the usual approach of estimating the maximum likelihood parameters using the Expectation–Maximization (EM) algorithm is computationally prohibitive. Therefore, we provide a novel algorithm for model fitting combining auto-correlated observations with spline and polynomial regression. The case study of this paper consists of the task of clustering the time series of sales data influenced by promotional campaigns. We demonstrate the effectiveness of our method in a case study of 131 sales series data from a real-world company. Numerical outcomes demonstrate the efficacy of the proposed method for clustering auto-correlated time series. Despite the specific case study of this paper, the proposed method can be used in several real-world application fields.

Service component recommendation based on LSTM

Service component selection is a core problem in software development process. With an enormous number of components available, it is often difficult for the developer to select the most appropriate one, as he or she might not be aware of all the possible business scenes ahead of time. Taking these challenges into consideration, we propose a deep learning-based system that automatically recommends service components based on component selection history during the software development process. We employ a sequential model with two long short-term memory (LSTM) layers and two fully connected layers, using SoftMax as an activation function, to predict the next service component. The model was trained, validated and tested on dataset with more than 120,000 examples from a real-world software company. The proposed network outperforms the baseline methods in terms of the evaluation criteria. In addition, the model results were deployed in a real-world software tool and gave positive feedback.

Reconsidering the Evolutionary Erosion Account of Corporate Fiduciary Law

This Article reconsiders the dominant account of corporate law’s duty of loyalty, which asserts that the courts have steadily relaxed standards of fiduciary scrutiny applied to self-dealing by corporate managers across more than a century of history—to the great detriment of the shareholder interest. The account originated in Harold Marsh, Jr.’s foundational article, Are Directors Trustees? Conflicts of Interest and Corporate Morality, published in The Business Lawyer in 1966. Marsh’s showing of historical lassitude has been successfully challenged in a recent book by Professor David Kershaw. This Article takes Professor Kershaw’s critique a step further, asking whether the evolutionary erosion account continues to exert normative power in today’s corporate governance context. The answer is that it does not, a result that obtains even though erosion of the standards that courts apply to management self-dealing has continued unabated ever since Marsh published in 1966, and even though there is no reason to think that management self-dealing benefits the shareholder interest. The result follows from the operation of the corporate governance system, which has assimilated and redeployed the erosion account’s motivating insight that officer and director self-dealing transactions do not make cost-benefit sense from the shareholder point of view. Regulation backs up the norm of aversion. Disclosure rules make self-dealing transparent to shareholders, who have no reason to like self-dealing and who now stand ready and able to register their preferences regarding such matters in corporate boardrooms. At the same time, the requirement of a majority independent board makes self-dealing transactions by board members highly inconvenient, because self-dealing undercuts independence. The practice reflects all of this, as shown by reference to hand-collected datasets of self-dealing transactions at publicly-traded companies and of litigation in respect of self-dealing transactions in the Delaware Chancery Court. The classic self-dealing transaction, although still a focal point of academic discourse on corporate fiduciary law, does not matter all that much in real world companies with dispersed shareholders. It is no longer an unsolved problem stemming from separated ownership and control.

Automatic network restructuring and risk mitigation through business process asset dependency analysis

In the 4th industrial revolution era, security of multiple interconnected devices has become a critical issue. A rapidly increasing number of cybersecurity incidents emerge due to complex interconnected sensors, devices, and systems used in the Internet of Things. In this paper, we tackle the need for automation in security risk analysis and restructuring of such networks. The presented framework models the connections of assets and devices so as to depict their interdependencies on a company's business processes and effectively reduces their overall risk against cybersecurity threats. It achieves this by (1) identifying critical components and dependency structural risks, (2) prioritizing assets based on their influence on business processes and (3) proposing network restructures and asset clusters. To do that, the proposed algorithm utilizes (i) dependency risk graphs for modeling and analyzing networks dependencies, (ii) graph minimum spanning trees, and (iii) network centrality metrics. We test the implementation on a real-world company and demonstrate its effectiveness. Results show that the framework can automatically identify critical components and dependency structural risks and propose different network topologies by creating the optimum number of asset subnets, while retaining business operations. Tests show that the closeness centrality metric combined with the midpoint on extreme values calculation type works best for network asset grouping and subnetting.

Importance of Context in Prediction Systems of Mobile Applications

Many traditional systems give recommendations to the users based on their past history without considering the context of the situation the user is in currently. Such systems may be good at prediction based on the past but do not consider the rapidly changing environment and the prediction may not be the best for the user. Context personalized to the user is important because it explains the situation the user is in. The recommendations to the user should also change according to the various contexts present. Context often represents the hidden state information that the user is in currently. Many systems often take into consideration the location of the user because the situation of the user generally changes with the location. In this paper, we explain why context is important while predicting results for the users by reviewing a set of papers where different contexts such as weather, time, location, user preference, and activity have been taken into consideration. These papers have taken context such that the recommendations to users change dynamically according to their situation or location and these recommendations can be of various forms such as search results or targeted advertisement. Location based Services, Location based advertisement and several types of context have also been discussed in the paper. A general architecture of context-aware systems has also been proposed. Several real world companies also make use of this contextual information so that the user has a dynamic user experience where all the states which might affect his decision making are taken into consideration.

Swimming with the sharks: An activity exploring cost, volume and profit analysis through the use of Shark Tank

This research presents an in-class activity and accompanying homework assignment designed to encourage students to engage in critical thought about managerial decisions made by a real-world company that appeared on the hit reality show Shark Tank. This alternative approach to teaching cost-volume-profit (CVP) analysis helps students engage in active learning while providing context that allows for a deeper understanding of the decisions managers make. Results indicate that students enjoy the activity and agree that it contributes to their learning and understanding of how CVP can be used in real-world businesses.

Exploring the link between inventory and working capital through variability reduction across a food-based CPG supply chain

The global uncertainties regarding the timing and type of economic recovery have made firms relentlessly focus on managing their working capital in a prudent manner. It is widely acknowledged that effective supply chain management practices can reduce operating costs and logistics expenses, significantly affecting a company's working capital. In this case-study, based on data from a real-world company that manufactures and distributes packaged food, we explore examining the variability across the supply chain (supplier-manufacturing-distribution). We specifically focus on mapping end-to-end supply chain variabilities and analyse ways to manage demand variability and forecast accuracy, operational variabilities in plant and the role of managing changeovers and centralisation vs. decentralisation of safety stock to manage distribution variabilities. Our study clearly pointed to forecast accuracy and arrival variability at production to be the two major performance-altering variables. Improving forecast accuracy was contingent on sharing and integrating information on promotions with production planning. Other variability inducing variables included changeovers and distribution safety stocks. Managing these further improves the working capital.

OTAs Selection for Hot Spring Hotels by a Hybrid MCDM Model

Tourism has been identified as one of the sectors that will contribute to Taiwan’s economic development. Online travel agencies’ role in producing economic value in the hospitality industry continues to grow. The hotel industry is used in this study as the research context to help managers of hot spring hotels select the optimal online travel agencies (OTAs) using a hybrid multi-criteria decision-making (MCDM) model. The aim of this paper is threefold: first, to obtain selection criteria for OTAs based on the fuzzy Delphi method; second, to extract interdependencies between the perspectives using decision making trial and evaluation laboratory (DEMATEL); and third, according to interdependencies between the perspectives, to rank the alternatives by analytic network process (ANP). Based on the proposed model, a real-world company was conducted and demonstrated OTAs selection problem; the results demonstrated the proficiencies and effectiveness of this model.

Smartphone zombies! Pedestrians’ distracted walking as a function of their fear of missing out

Smartphone use while walking (i.e., being a smartphone zombie) has become a prevalent phenomenon in many cities worldwide. Previous research shows that many pedestrians choose to interact with their phones as they walk around in cities, despite being aware that their behavior might be dangerous. To investigate potential reasons for the prevalence of distracted walking, the current study explores the construct Fear of Missing Out (FoMO) as a potential antecedent of pedestrians' smartphone use while walking. Hierarchical OLS and logistic regression analyses show that FoMO predicts distracted walking, the tendency to engage in virtual social interactions while walking, and dangerous traffic incidents—irrespective of participants’ age and gender. Virtual communication might serve as a compensation for real-world company, thus sidelining the need to traverse safely.

A Multicriteria Approach to Support Task Allocation in Projects of Distributed Software Development

Organizations are increasingly investing in Distributed Software Development (DSD) over the years. A typical decision‐making problem in the distributed scenario consists of deciding which team should be allocated each task. That decision takes into account a relative degree of subjectivity. That setting is suitable for applying Verbal Decision Analysis (VDA). This paper introduces an approach to support the allocation of tasks to distributed units in DSD projects, structured on the hybridisation of methods of Verbal Decision Analysis for classification and rank ordering applied to influencing factors and executing units. Firstly, a review of the literature was conducted aiming to identify the approaches to support the allocation of tasks in DSD contexts. Then, an approach was developed by applying VDA‐based methods for classification and ordering. Bibliographic research and the application of surveys with professionals allowed identifying and characterising the main elements that influence task assignment in DSD projects. Afterwards, experiences were carried out in five real‐world companies. In the end, the proposed approach has been submitted to the evaluation by the professionals of the participating companies and by some project management experts. The proposed approach comprises a workflow containing responsible actors and descriptions of the activities. Automated tools are also employed in automating the implementation of the approach. After applying the approach in five companies, task assignment recommendations are presented in groups for each company, according to the task type, i.e., requirements, architecture, coding, and testing, ranging from the most to the least preferable office. Results of the experiences and evaluations held during this work present evidence that the proposed approach is flexible, adaptable, and easy to understand and to use. Moreover, it helps to reduce decision subjectivity and to think of new aspects, supporting the task allocation process in DSD.