Search results

Blockchain technology and its applications have recently received the attention of practitioners and academics. Visualizing the full impact these technologies will have on the world is challenging since their adoption is still in the early stages. This chapter explores how blockchain can disrupt the general business and financial world. Blockchain offers information transparency, live data synchronization, and immutable records that prevent fraud. Digital contracts and digital asset management may heavily depend on the development of blockchain and the adoption of smart contracts. Smart contracts can execute financial transactions automatically and enforce all parties' obligations without needing an intermediary and its cost. They can increase speed and simplify processes, reducing licensing ticketing costs and overhead charges. Blockchain also offers technology adoption solutions, like fair payment, cloud storage, smart contracts for financial assets, and international transactions with bilateral double taxation. It has further facilitated the creation of decentralized finance.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This study aims to explore challenges and opportunities in the digitalization of the business-to-business (B2B) customer journey in different buying situations. It also investigates where in the customer journey digital marketing is most efficient.

This research adopts a single case study approach to examine a B2B company that implemented digitalization in its customer journey in different buying situations. Data were collected through semistructured interviews, complemented by internal documents and information from the company’s website and social media, to identify reasons for and against the decision to digitalize the B2B customer journey.

Digitalization can offer firms a cost-effective and value-creating way to interact with customers in a B2B context. The B2B buying situation, however, plays a significant role in decisions on how to implement digitalization. Moreover, in the prepurchase phase, digital marketing is more effective in building awareness; in the purchase phase, personal selling is more effective in addressing customers’ needs.

The use of a single case study cannot produce results directly generalizable to other contexts. However, the findings are applicable to the digitalization of B2B customer journeys in similar industrial contexts.

To successfully implement digitalization in the customer journey, B2B firms should choose digital tools according to different buying situations and phases in the customer journey, segment buyers by their needs rather than individual characteristics and integrate the sales and marketing functions.

This study contradicts prior research that claims that digital marketing can be used in a similar way in both B2B and business-to-consumer contexts. It further shows that the relevant demarcation is not between personal sales and digitalization but between automated digital marketing and individualized personal sales, regardless of medium.

Since previous literature provides fragmented and conflicting results about the use of digital data for product innovation, the article aims to comprehensively explore and shed light on how agri-food firms utilise external and internal digital data sources when dealing with different product innovations, such as incremental, architecture and radical innovation.

This paper adopts an exploratory multiple-case study and a theory-building process, focussing on the agri-food industry. We collected primary and secondary data from eight manufacturing companies.

The findings of this research show an empirical framework of six agri-food firms’ digital data utilisation behaviours: the supervisor, the passive supervisor, the developer, the passive developer, the pathfinder and the conjunction behaviour. These digital data utilisation behaviours vary according to a combination of data sources, such as internal data related to inside phenomenon measures (e.g. data generated by sensors installed in the production plan) or external data (e.g., market trends, overall sector sales), and innovation purposes.

This article offers guiding principles that assist agri-food companies when utilising internal and external digital data sources for specific product innovation outcomes such as incremental, architectural and radical innovation.

The significance of external and internal data sources in stimulating product innovation has garnered substantial attention within academic discussions, highlighting the critical importance of analysing digital data for driving such innovation. Nonetheless, the predominant approach is to study a single innovation outcome through the lens of digital technology. In contrast, our study stands out by adopting a fundamental perspective on data sources, enabling a more nuanced explanation of the overall product innovation outcomes within the agri-food sector.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

Given the implementation of a new Certified Public Accountant (CPA) licensure exam and the CPA Evolution Model Curriculum, accounting educators must integrate more advanced skills in their coursework. We illustrate how a commonly-used project in accounting classes, which teaches technical accounting content, can address skills and competencies identified by the Pathways Commission and the American Institute of Certified Public Accountants (AICPA) and, as a result, enhance skills all business school graduates need in the workplace.

We incorporate a financial statement analysis research project under a group work format in three levels of financial accounting classes. Using both quantitative and qualitative analysis, we evaluate changes in student perceptions of skills and competencies important for business graduates.

We find students perceive improvement in critical thinking, problem-solving, the ability to work with other people, their understanding of the course material, and data analysis abilities after completion of the project. We also find statistically significant increases pre-to post-project in student perceptions of their knowledge, confidence, competence, and enthusiasm with respect to accounting material.

We provide an example of how educators can align a commonly-used project with the CPA Evolution Model Curriculum, yet still meet the needs of non-accounting majors and prepare all students for future business careers. Group work has been studied and similar financial statement analysis projects have been implemented in the classroom for years. We contribute by not only extending and updating this research, but also by re-evaluating a project to determine whether it meets the shifting needs of a rapidly changing profession. By doing so, we answer recent researchers’ call for research in higher education that addresses employability and workplace skills.

This chapter discusses the evolution of online trading, its application in various market structures, and its benefits and potential concerns. Computers were first used in electronic communication networks among brokers and dealers to make trades and for informational purposes. Online brokers became popular with retail investors as the internet spread. Online trading comes with various trading protocols and order types. It enables traders to automate trading decisions and process data more easily using charting tools and customized programs connected to the broker's infrastructure. Electronic trading allows for greater centralization but can also be accompanied by market fragmentation. Market regulation has affected market structure and is still evolving. Centralization allows for more competitive prices and reduces search costs. Decentralized markets could cope better with asymmetric information.

With digitisation, a new kind of inequality has emerged in society between people and groups of people. A lack of digital inclusion creates challenges for the economic and social development of society and citizen participation. This study analyses how the country-level cultural factors defined by Hofstede are associated with citizens' digital skills and internet usage and how they moderate the effects of age, gender, educational level and income level.

This comparative cross-sectional study examines digital inclusion in 22 European countries. Data from the European Social Survey (N = 37,602) are analysed using a two-level regression analysis.

The study found significant effects of demographic and socio-economic factors and country-level indulgence on digital skills and internet usage. In addition, the study shows that a high value on the indulgence index moderates the negative effect of age.

The digital divide has been studied widely with regard to individual-level influencing factors and international comparisons. The significance of Hofstede’s cultural dimensions in terms of digitisation and digital divides has also been confirmed in previous studies. However, there is a lack of analysis combining the effects of country-level culture and individual-level demographic and socio-economic factors on citizens' digital skills and internet usage. Generally, the research emphasises the significance of national culture in digital inclusion and especially in supporting the digital inclusion of older adults.

This chapter introduces what a digital ID is, why it is important, how it works, the design choices, as well as how central banks can collaborate with other stakeholders in promoting digital ID infrastructures for use in digital financial services.