Abstract

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Introduction

The building and infrastructure industries are gradually transitioning towards building information modelling (BIM) as an object-based digital representation of an asset’s physical and functional characteristics [1], [2] and digital engineering (DE) practices to enable more productive methods of planning, designing, constructing, operating, and maintaining assets, using digital processes [3]. As detailed later in the paper, “digital” encompasses approaches, tools and technologies such as BIM and DE together with data science, the Internet of Things (IoT), and AI solutions integrated to digitise assets throughout the lifecycle. Asset owners recognise the importance of digital transformation (DT) in delivering and managing assets [4], as it offers many benefits throughout the asset lifecycle and can enhance efficiency, value for money, productivity, innovation, and safety.

1.1. DT of large infrastructure asset ownersGovernments and government-owned corporations (GOCs) are typically owners and operators of large assets, such as buildings, infrastructure, and other asset holdings across the spectrum of services. Governments and industries must transition from ‘digital by exception’ towards ‘digital by default’ [5], [6]. The infrastructure sector should adopt an evidence-based approach to infrastructure planning, delivery, and operations, including BIM, DE, embedded sensors, and digital asset management [7]. DT offers significant potential value and benefits to government agencies that manage assets across the whole-of-asset lifecycle. DT can assist in strategic investing and partnering with local governments and industry to deliver more benefits, including [8]:

Conclusion

The dynamic evolution of the building and infrastructure sectors towards digitalised practices underscores the growing importance of asset owners engaging in comprehensive DT. This research project has developed a multifaceted framework for assessing DT maturity tailored to the needs of large GOCs.

The existing assessment tools and methods have been reviewed for suitability in assessing the DT in whole-of-life asset owner organisations. The reviewed tools offer varying assessment scopes, including a capability assessment, maturity assessment, and benchmarking. The need and potential benefit of a new maturity assessment tool that would balance assessment accuracy and completion efficiency were established based on the shortcomings and limitations of existing DT maturity tools and methods. The key contributions of the new DT maturity assessment framework and tool developed by the research team include (i) a holistic view on the assessment of how well organisations use data and digital solutions, focusing on BIM capabilities, the value of information, data integration aspects, components of the smart city concept and critical information management competencies; (ii) an evidence-based scoring approach including assigning weight to various assessment indicators based on their importance to ensure the accuracy of the assessment; (iii) a multiperspective assessment allowing for more accurate organisation-wide analysis; (iv) short-term and long-term recommendations based on the current and target levels gap to facilitate performance improvement while realising the benefits and value of getting to each maturity level.

Abstract

The Internet of Things (IoT) leads to the next phase of human interaction with technology. With the help of the IoT, physical objects can be given the ability to generate, receive, and seamlessly trade data with one another. The IoT includes a wide variety of applications, each of which focuses on automating a specific task and works to give inanimate objects the ability to act independently of human intervention. The currently available and upcoming IoT applications hold a great deal of promise for enhancing the level of convenience, productivity, and automation enjoyed by users. High levels of security, privacy, authentication, and the ability to recover from attacks are required for the implementation of such a world in a manner that is constantly expanding. In this light, it is necessary to make the necessary adjustments to the architecture of IoT applications to accomplish end-to-end security in IoT environments. In this article, a comprehensive review of the security-related challenges and potential sources of danger posed by IoT applications is provided. Following a discussion of security concerns, a variety of new and established technologies that are focused on achieving a high degree of trust in the applications of the IoT are covered. Machine learning, fog computing, edge computing, and blockchain are just a few of the technologies that help the IoT provide greater security.

Introduction

The Internet of Things (IoT) is one of the disruptive technologies of the 21st century that has revolutionized every business dimension across the globe. The Internet has become an essential component in daily activities such as e-commerce, e-learning, e-conferences, etc. According to [1], the number of IoT devices deployed may exceed 21.3 billion by 2022. Intelligent interfaces connect IoT devices in order to collaborate, gather, communicate, and store data. Deploying IoT devices enhances business productivity by optimizing operational procedures and maximizing the utilization of available resources. IoT brings innovation in the way things are monitored and managed remotely with the help of real-time data acquired from the sensors. With the unrestrained volumes of data generated by billions of connected devices, storage, management, and security are arduous. Security aspects such as authentication, authorization, privacy, confidentiality, availability, and integrity form the basis for information exchange in a trusted environment. Authentication is the process of identifying the genuineness of an entity. The authorization process allows only authenticated users to access the resources. The protection of privacy prevents hackers from accessing the user’s private information. Data confidentiality is an outcome of authentication and authorization. It prevents sensitive data from being reclaimed and exploited. A device’s availability confirms that IoT devices and services are resilient to attacks and are accessible all the time. Data integrity is the process of providing a tamper-proof platform for information communication, exchange, and storage.

Conclusion and future directions

In this paper, the methods that are currently available in the literature are discussed, and this paper also presents the methods that are currently available. The subsequent step is an in-depth discussion of the numerous security risks and the importance of threat modeling. The subsequent topic is a discussion of the various IoT applications’ necessary security precautions. It reviews the prior research on the numerous security issues and the solutions put forth by various researchers. The holes that were found while conducting this literature review served as inspiration for the current research work.

7.1. Research challenges
The billions of IoT devices deployed across the globe collect personal and sensitive data and exchange it with other networks using intelligent interfaces. Providing device authentication, authorization, data privacy, and security in such an un trusted environment is a challenge. Centralized trusted infrastructure cannot scale to the dynamic and ever intensifying traffic and, thus, leading to bottlenecks in the network. Distributed authentication can scale to the increasing demands of the IoT networks, but requires distributed trusted systems. The majority of the work in the IoT security domain focuses on embracing the security mechanisms of WSNs and conventional Internet-based applications. However, these mechanisms are far from being implemented in real-time scenarios due to the specific challenges of IoT, such as

Abstract

Due to the industrial revolution and, thereby with, the emergence of Industry 4.0, there has been an impact on the marketing of business firms, enabling them to digitally transform and adopt Marketing 4.0 practices supported by smart technologies. One of these is Big Data Analytics (BDA), which enables a firm to improve its marketing practices. The main objective of this study is to evaluate how integrating big data technology improves the marketing strategies of Indian FMCG companies operating in a dynamic and highly competitive market distinguished by its size and diversity, constantly changing consumer behaviour, and Marketing 4.0 trends. This study adopts a systematic approach to coding an interview with seasoned Indian FMCG professionals to explain the capabilities of Big Data in molding marketing practices. A framework is constructed based on the same. The theoretical and practical implications are discussed to visualize its applicability in business. Further, the Resource-based View (RBV) theory is discussed for describing the insights into the role and adoption of Big Data by Indian FMCG firms for enhancing performance and achieving competitiveness, along with demonstrating its benefits for decision-making, customer loyalty and internal capability assessment.

Introduction

With technological advancements, industries now utilize innovative technologies that bolster a range of corporate activities and procedures, profoundly influencing the internal operations of these firms. (Cammarano et al., 2023, Singh et al., 2022b). The fourth industrial revolution, known as Industry 4.0, which is driving this technological change for business firms, serves as a bridge between intelligent products and adaptive processes and has a significant impact on both business models and production processes (Rosário & Dias, 2022; Salkin et al., 2018). Industry 4.0 enables the digital transformation of firms, which is further impacted by different internal and external factors related to the firm. The digital transformation is required to improve customer experiences and retain customers (Kitsios et al., 2021). Despite their implementation challenges and hazards, these advancements increase market analytics, cost, channel management, client connections, professional skills, and organizational procedures (Pascucci et al., 2023). With Industry 4.0, there is a steady trend towards understanding the consumers, which includes their needs, behaviours, storytelling, and anticipation (Lampropoulos, 2023; White & Pathak, 2023). These are the core components of marketing techniques that have evolved into marketing 4.0 (Yakut, 2022). As an outcome of the emergence of trends like Big Data (BD), social media, corporate social responsibility, and e-commerce, the shift to Marketing 4.0 is accelerating (Guven, 2020). Marketing 4.0 is driven by the convergence of offline and online marketing strategies and incorporates technical advancements to provide better flexibility (Rathod et al., 2020; Kotler et al., 2017). The improvement of the consumer experience, the induction of repurchase intention, and brand promotion are some of the significant achievements of Marketing 4.0 (Dash et al., 2023). Consumer usage patterns of social media, online platforms, search engines, contextual interactions, and community influences are some of the areas the companies are looking into. Companies use the information mainly for promotional and consumer engagement activities. The consumers are seen to be transformed into advocates through marketing 4.0, where a sense of community building takes place, resulting in a meaningful engagement of the marketers and consumers (Gau, 2019).

Conclusion

This study discusses and examines the role of BD and how an FMCG firm can adopt BD to improve its overall performance and attain a competitive position in the market. This research is the first of its kind to explore the implications of BD in the Indian FMCG sector. The relationship among BD, marketing practices and FMCG from in Indian perspective is a unique contribution to this paper. In addition to its usefulness, the study explores the crucial roles that automation and decision science play. This study discusses the benefits the firm achieves for accepting BD in the business. The study reveals some of how BD helps in decision-making for FMCG firms. For Indian FMCG firms, BD is crucial to value creation and achieving business operations’ scalability. FMCG organizations are using BD and analytics solutions more and more since they help create value, scale corporate operations, and increase output in today’s competitive market. BD makes it possible to analyze consumer preferences and behaviour at each transaction stage with a business. FMCG companies may now provide personalized experiences through targeted messaging, unique offers, and channel-specific communication optimization. BD also offers in-the-moment customer insights via sentiment, preference, and behaviour research, which aids FMCG companies in creating goods that enhance customer experiences and provide them with the necessary competitive edge.

Additionally, BD facilitates effective market campaigns through real-time predictions, data processing, and analysis. BD can also be strategically used through automation, new business model innovation, decision science, and risk management. Furthermore, customer segmentation based on BD makes marketing techniques more effective, and real-time customer insights, like sentiment analysis and tracking of consumer preferences and behavior, help organizations respond quickly to market changes.

Abstract

     One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Introduction

     Wireless Sensor Networks (WSNs) have become increasingly popular in recent years, as they offer a powerful and cost-effective solution for monitoring and collecting data from a variety of environments. A Wireless Sensor Network (WSN) consists of a large number of distributed sensor nodes that cooperatively monitor the physical world [1]. Each node in these networks is typically equipped with a wireless communication device, a small microcontroller, a memory unit and a power source. The nodes in a WSN are typically battery-powered and have limited processing power, memory, and communication bandwidth. Therefore, the design of a WSN requires careful consideration of these constraints, such as the choice of communication protocol, data routing algorithms, and energy management strategies [2].

     The complexity of the WSN domain as well as the presence of many sensor nodes unavoidably introduces a large amount of data in these networks that must be processed, transmitted and received. The sheer amount of data generated by WSNs can pose a significant challenge for the network design and operation. The sensors in a WSN can generate data continuously or periodically, depending on the application requirements. This data must be processed, stored, and transmitted to the base station in a timely and efficient manner. So the complexity of the WSN domain and the presence of many sensor nodes can introduce a large amount of data that must be processed, transmitted, and received [3]. Despite their profound advantages, the utilization of WSNs is often battery-powered and strictly limited due to energy constraints. In fact, most of the energy expenditure of a sensor node occurs during wireless communication, and the remaining energy is consumed during sensing and data processing. The radio transceiver of a node consumes a significant amount of energy during data transmission and reception. Therefore, the energy consumption of a node must be carefully managed to ensure the longevity of the network [4]. Transmission power adjustment for a special transmitter-receiver pair depends on several environmental conditions. The transmission power required to reach the receiver is affected by two main factors including distance and wireless connection quality. Distance affects transmission power. As the distance between the transmitter and receiver increases, the signal strength decreases, resulting in a weaker connection.

Conclusions and future works

     We studied the transmission range of sensors in wireless sensor networks in this study, and proposed a simple yet effective distributed transmission power control algorithm for sparse wireless sensor networks. We showed that this algorithm has advantages in many cases. The main idea of algorithm is simple, but performance of algorithm in targeted scenarios is excellent. We analyzed the performance of this method theoretically and show that using this method is worthwhile in some scenarios, especially in sparse wireless sensor networks with high amount of communication traffic. In these scenarios, our method can be used as a pre-step of any routing algorithm and decrease energy consumption and radio interference. As a future work, we intend to design methods to overcome the collisions problem. Designing methods for resisting the algorithm against the sensor failures is another future work. Furthermore we intend to work on using learning methods like Reinforcement learning for power control. Designing a good energy efficient reward mechanism is one of the main challenges of these methods.

Abstract
This paper examines the relation between bank profit performance and business models, using a machine learning–based approach. The analysis contributes to the literature on this relation by considering the bank portfolio’s ability to yield profits as the identification criterion of strategic profiles and by including all the components of the business model simultaneously in the identification process. Our research strategy is applied to the European Union banking system from 1997 to 2021. The paper’s primary finding indicates that specialization seems to be a strategy that results in banks adopting business profiles with better profit performance, particularly if the banks specialize in the standard retail-oriented model.

Introduction

      What characteristics of bank business models enhance profitability? The search for answers has been among supervisory authorities’ priorities since the end of the Great Recession and is likely to maintain its relevance in the aftermath of the COVID-19 pandemic. The reasons for such interest lie in the fact that the relation between business models and profitability can reveal the viability of banks’ strategic plans and, hence, the viability of banks themselves. In this regard, the Anon (2016) links poorly adapted business models to low bank profitability in advanced economies, identifying the former as an eroding agent of bank resilience over time. However, concern about the profitability of banks’ business profiles has been especially strong in the European Union (EU), as reflected by the fact that the European Central Bank launched a thematic review in 2016 to assess the relation between profitability and business models (European Central Bank, 2018).

      Banking research has examined this relation in two broad and different ways, using regression analysis to explore the effects of the characteristics of the bank business profile on profitability or grouping banks into business models by means of clustering techniques.

Conclusion

     The ability of business models to generate profits is an ongoing concern for banking authorities, especially in the EU. Closely related to this concern, we propose a new methodological strategy to identify business models and to assess their relation to profit performance. Previous works on business models have assumed the business models as a given or have identified them using similarities in balance sheet components. In either of these cases, profit performance is a byproduct of the business model. By contrast, in our identification strategy, profitability is at the core of the definitions of banks’ strategic choices. In particular, business models are obtained using the contributions of the bank portfolio components to profitability as the instruments of clustering analysis.

      The empirical exercise focuses on the EU banking system in 1997–2021. According to the classification of business profiles based on their overall contributions to profit performance, the best profile is a standard retail-oriented model, the second-best one is a standard wholesale model, and the worst profile, which has only equity as a characteristic component, does not match any standard model. Therefore, specialization seems to be a strategy that results in banks adopting business profiles that perform better in terms of their contributions to profitability, particularly if the banks specialize in the standard retail-oriented model.

Abstract

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study’s algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

Introduction

At this stage, there are many problems in the traditional HRM, such as the complicated work of the HRM department, the weak teamwork of the enterprise, and the imperfect employee incentive mechanism, which restrict the overall development of the enterprise. In the new era, the traditional HRM simply cannot meet the new needs of enterprise management, so it needs to change this status quo in line with the development of the times. The arrival of the IoT era has connected the world as a whole, which not only provides many conveniences for human society, but also promotes the economic development of all walks of life. Therefore, this paper proposed to apply IoT technology to enterprise HRM, so as to promote the overall development of enterprise HRM.

This research leverages IoT technology and radial basis function neural networks to significantly enhance the predictive modeling of enterprise human resource needs, outperforming traditional algorithms in accuracy and efficiency. Our model not only predicts employee numbers with an impressive 90.2 % accuracy (compared to the traditional 83.9 %) but also excels in forecasting sales revenue, achieving a remarkable 93.9 % accuracy against the conventional 85.2 %. These advancements highlight our model’s capability to provide more reliable and actionable insights for human resource planning and sales forecasting. Further extending our research’s applicability, we delved into the realm of wireless communication network security, achieving prediction accuracies ranging from 90.29 % to 92.85 % in various tests. This high level of precision in security risk prediction underscores the potential of IoT and data-driven approaches in addressing complex challenges in both human resources and information security management. The comprehensive examination and validation of our predictive models across different domains underscore their significant contribution to enhancing strategic decision-making and risk management in an increasingly digital and interconnected business environment.

Conclusions

In the era of the IoT, there are many problems in the traditional enterprise HRM, which is difficult to meet the new needs of enterprise strategic development, and needs to follow the pace of the times to make changes. The IoT not only brings a lot of convenience to human beings, but also brings some hidden dangers to the security of communication networks. It also needs to combine data driven technology to improve the security of communication networks. In order to promote the development of enterprise HRM, this paper proposed to apply the IoT to enterprise HRM, and combined radial basis function neural network to build a prediction model of enterprise human resource demand. Finally, the model was tested. Under this algorithm, the prediction accuracy of the enterprise human resource demand prediction model for sales revenue and the number of employees was very high, and the prediction accuracy had been significantly improved. This paper also evaluated the wireless communication network security risk prediction model, and the experimental results showed that the prediction accuracy of this model was high.

Abstract

In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction.

Introduction

Due to advancements in powerful computing tools, hardware, widespread use of cloud data, and the evolution of the Internet of Things, the accessibility of large datasets has significantly improved. This has promoted the emergence of many efficient machine learning (ML) algorithms. These algorithms, aided by continuous advancements in data analysis technology, have demonstrated effectiveness in training models and solving specific problems. If ML algorithms are complex and deep enough in terms of layers, then ML can become so-called deep learning (DL) [1]. Regardless of the name, ML seems to have a wide range of applications in modern society, because the achievements of artificial intelligence (AI) often exceed those achieved by manual control design.

In 2021, Nature Photonics further advanced its exploration of the hot topic of optics/photonics through a focused issue called “Machine learning of Light”. As an important type of microphysics, optics seems to have similarities in complexity with some of the “black box” processes in ML. In the Q&A session, David Pille and Aydogan Ozcan discussed the two primary drivers behind the current application of ML in photonics [2]. One direction is to use AI methods on hardware to design optical structures and devices with specific performance. Another direction is to use optical systems as fully optical/hybrid statistical inference models to achieve AI calculations.

Optics and photonics applied to ML computing offer unique advantages, allowing for ultrafast calculations at extreme frame rates and low energy consumption [3], [4], [5], [6]. For instance, Feldmann et al. [7] proposed a fully optical neural synaptic system. They use wavelength division multiplexing technology to implement a scalable circuit architecture for photonic neural networks (NNs) and use optical pulse signals to regulate the phase change materials that encapsulate neurons. This phase change material can regulate transmittance and reflectivity through a controllable laser. This system can perform both supervised and unsupervised learning. The work successfully demonstrates direct pattern recognition in the full optical system. This photonic NN has the potential to leverage the inherent high-speed and high bandwidth of optical systems, thereby enabling the direct processing of full optical communication and visual data. However, logic gates, computation, and NNs are all linear (passive) systems that cannot address nonlinear (active) issues. The lack of effective nonlinear optical processes as activation functions for many nodes poses challenges for all-optical implementation [8].

Conclusion and future perspectives

This paper reviews the application of the ML algorithm in optical fiber sensors. In recent years, the advent of ML has greatly impacted optical development. Compared with static demodulation algorithms and complex spatial optical paths, the ML algorithm offers better signal processing mechanisms for optics. ML algorithms enable automated processing, analysis, and recognition of spectrums, thereby enhancing the efficiency and accuracy of spectrum analysis. Using ML algorithms to classify and identify the spectrum of different substances, enabling qualitative and quantitative analysis of substances. By learning from known corresponding spectrums and building models, ML algorithms can predict and analyze unknown spectrums. ML algorithms can extract and recognize features from the spectrum, analyze the content and proportion of various components, and reveal the intrinsic nature of substances. With the continuous development of spectroscopy and ML algorithms, the application prospects of ML in spectroscopy will become increasingly widespread. This advancement will contribute to more efficient and accurate analytical methods in chemical analysis, environmental monitoring, food safety, and other fields, promoting progress in scientific research and expanding application fields.

Abstract

Most of the companies’ sustainability and growth depend on how well its employees perform. However, the measurement of employees’ performance until now is inconclusive and inexhaustive. To accurately assess and predict an employee’s performance, numerous external factors (physical/environmental, social, and economic) related to an employee’s life have been taken into account in this work. The purpose of this research is to explore an unbiased AI algorithmic solution to predict future employee performance considering physical, social, and economic environmental factors that affect employee performance. We collected data of 1109 employees from the ‘For-Profit Organization’ in Bangladesh from both employers and employees to cover all the factors that justified the unbiased outcome. We utilized a few machine learning tools in this study including the Logistic Regression classifier, the Gaussian Naive Bayes, the Decision Tree classifier, the K-Nearest Neighbors (K-NN), the SVM classification, etc., in order to predict the employee performance evaluation. Then, we compared the effectiveness of those machine learning models by analyzing their precisions, recall, F1-score, and accuracy. This work can be utilized to obtain bias-free employee performance reviews. This fair employee performance assessment can aid decision-makers in making moral choices regarding employee promotions, career advancement, and training needs, among other things. The study also describes notes for future researchers.

Introduction

Performance evaluation, which includes assessing current performance, identifying good and poor performers, and providing feedback to staff, is one of the most challenging components of Human Resource Management (HRM) (Cherian et al., 2021, Fogoroș et al., 2020, Stone et al., 2015). Employee performance evaluations are not practiced systematically by numerous organizations. As a result, the evaluation method becomes erratic and ineffective. A systematic approach should be adopted in order to evaluate employees at the planning stage on a regular basis (Ahmed et al., 2013). Employees with these attributes—skills, dedication, attitudes, and knowledge—are valued as assets by the company (Al-Tit et al., 2022, Li et al., 2008, Yang and Lin, 2009). By creating new knowledge at firms’ level, an organization’s human resources can the firms to innovate (Terán-Bustamante et al., 2021). The accurate assessment of the employees’ performance contributes to the mission of the company with the maximum satisfaction of the employees (Pap et al., 2022). Since company progress depends on employee advancement, numerous executives search for efficient ways to improve performance drastically (Abbas and Yaqoob, 2009, Salam, 2021). To boost performance, employers first need to know the performance condition of the employee in any organization. An article in the Harvard Business Review (Antonio, 2018) claims that essential functions of the organizations, such as prediction, upselling, cross-selling, and performance management can be remarkably influenced by AI technologies (Ledro et al., 2023). In the future, businesses, communities, and nations will be significantly impacted by big data, automation, and machine learning (Lada et al., 2023, Tao et al., 2023). In this regard, AI has started to play a role in business, particularly in HRM, concerning the prediction and decision-making (Nilashi et al., 2023, Qureshi et al., 2023).

Conclusion

The performance of the company’s personnel determines a large portion of its sustainability and growth. Many external aspects (physical/environmental, social, and economic) relevant to an employee’s life have been included in this work in order to measure and anticipate an employee’s performance effectively. The goal of this project is to create an AI algorithmic-based ethical decision-making framework that takes into account the various environmental factors—physical, social, and economic – that have an impact on worker performance. Our results were impartial since we gathered information from a few “For-Profit Organizations” in Bangladesh, both objectively and subjectively. In this study, we used a variety of machine learning methods, and we were able to acquire a reasonable accuracy score. The Random Forest model obtained the highest accuracy score, and the Gaussian naïve Bayes model had the lowest. An impartial employee performance review can be obtained by using this work. Decision-makers can use this equitable employee performance evaluation to help them make moral decisions about training requirements, career advancement, and employee promotions, among other things.

Abstract

Developmental prosopagnosia (DP) is a condition that indicates the inability to recognize individuals by their faces from birth, without any history of brain damage. The assessment of face recognition ability and diagnosis of DP involve the use of face tests such as the Cambridge Face Memory Test (CFMT) and the Cambridge Face Perception Test, along with self-reported measures like the 20-Item Prosopagnosia Index (PI20). Face recognition accuracy is affected by anxiety. However, previous studies on the relationship between face recognition ability and anxiety have not used the PI20 measure. This study aimed to investigate the relationship between self-reported measures of face recognition ability and anxiety tendencies among healthy young individuals for DP diagnosis and its implications. We used a face recognition test, involving the PI20, CFMT, Visual Perception Test for Agnosia–Famous Face Test (VPTA–FFT), and State-Trait Anxiety Inventory (STAI). We assessed the performance of 116 Japanese young adults (75 females, median age of 20.7 years, with a standard deviation of 1.2). Subsequently, we conducted a statistical analysis to examine the relationship between the outcomes of the face recognition tests and STAI scores using Pearson correlation analysis and single correlation coefficients. The results showed a positive correlation between state anxiety and PI20 (r = 0.308, p = 0.007), and a weak positive correlation was also observed between trait anxiety and PI20 (r = 0.268, p = 0.04). In contrast, there was no correlation between CFMT and VPTA–FFT with respect to STAI. The results of the hierarchical multiple regression analysis also suggested that the correlation between the performance on the PI20 (self-report) and objective measures of face recognition performance (the CFMT and the VPTA–FFT) are driven by differences in anxiety. This study is the first to explore the relationship between face recognition abilities and anxiety using the PI20 self-report measure. There are implications for future research on the diagnosis of DP and the relationship between anxiety and face recognition.

Introduction

Face recognition ability enables individuals to differentiate and identify faces, playing a crucial role in interpersonal recognition and everyday communication. Clinical and functional imaging studies have highlighted that brain lesions, particularly in regions such as the fusiform gyrus, can lead to face recognition impairment. In recent years, there has been a growing interest in the field of face recognition research regarding developmental prosopagnosia (DP). DP refers to a condition in which individuals experience lifelong impairments in face recognition despite possessing normal visual and intellectual capabilities since birth. Clinically, there is no indication of overt central nervous system disorders, and neuroimaging studies also reveal the absence of organic brain lesions (Bate & Tree, 2017; Behrmann & Avidan, 2005; Cook & Biotti, 2016; Duchaine & Nakayama, 2006a). Epidemiological studies in Europe and Asia have reported a prevalence rate of approximately 1.9 %–2.9 % of the population (Bowles et al., 2009; Kennerknecht et al., 2006; Kennerknecht et al., 2008). People with DP encounter challenges and psychosocial difficulties in their interpersonal interactions due to impaired face recognition (Yardley et al., 2008). Early diagnosis and support are crucial, especially before adulthood and entering the workforce.

Currently, diagnosing DP presents challenges due to the absence of established strict objective criteria or biological markers (Barton & Corrow, 2016; Susilo & Duchaine, 2013). Previous studies have revealed ambiguous criteria for diagnosing DP and differentiating it from conditions other than DP in face recognition tests (Bate & Tree, 2017; Dalrymple & Palermo, 2016). Dalrymple and Palermo (2016) provided guidelines that define DP as objectively poor performance on face memory tests and a subjective feeling of repeated face recognition failures in daily life. The authors emphasized the importance of using questionnaires and interviews to assess face recognition difficulties in daily life and highlighted the limitations of relying solely on questionnaires for DP diagnosis, as individuals with DP may lack accurate insight into their own face recognition abilities.

Conclusions

The present study is the first to investigate the relationship between self-reported face recognition abilities and anxiety in young, healthy Japanese individuals using the self-perception face recognition test PI20. The findings indicated that unlike the objective face recognition tests CFMT and VPTA–FFT that showed no association with anxiety, the self-perception test scores may be influenced by state anxiety. Self-reporting, at least in its current form, provides limited insights into predicting face recognition performance in young individuals and diagnosing DP. Therefore, self-reporting is insufficient to evaluate face recognition abilities in young individuals, and DP cannot be diagnosed solely based on self-perception tests. It is necessary to combine self-perceived indicators of face recognition impairment with other perception-based face recognition function tests and consider the presence and extent of anxiety tendencies. Our findings have significant psychological implications for understanding face recognition impairments, DP diagnosis, and clinical practices. In the future, expanding research to investigate the interaction between facial and expression recognition abilities and anxiety is expected to enhance the accurate diagnosis and support for young individuals with facial recognition impairments.

Abstract

This research contributes to understanding the spillover effect of customer digital transformation along the supply chain. We take a supply chain relationship perspective to explore the influence of customers’ digital transformation on suppliers’ audit fees and find a significant reduction in such fees when customers undergo digital transformation. An economic mechanism analysis reveals that this transformation reduces audit fees by lowering the risks and costs encountered by auditors. This is achieved by mitigating suppliers’ business risks and improving earnings quality. Heterogeneity analysis reveals that the impact of customers’ digital transformation on suppliers’ audit fees is more pronounced when the supply chain is geographically distant, suppliers with more specific investments and with high levels of market competition.

Introduction

Digital transformation involves shifting from a traditional “industrialized” management model to a digital management model ( Verhoef et al., 2021 ). This transition goes beyond simply applying digital technologies to technical aspects of a business and involves a complete restructuring of business models and operational management. Research on the economic consequences of digital transformation emphasizes its potential influence on the quality of corporate disclosures and business risks. For example, digital technologies can improve operational management, promote networked and flattened organizational structures ( Nambisan et al., 2019 ), increase corporate disclosure transparency and the quality of accounting information and enhance communication, production and operational efficiency ( Wu et al., 2021 , Chen et al., 2022a ). By digitally collecting and analyzing data, firms can effectively visualize information, enhance interdepartmental coordination, refine risk control processes, mitigate operational risks and reduce management fraud and decision-making errors that lead to losses, thereby enhancing their market reputation ( Manita et al., 2020 , Zhou and Li, 2023 ). Most studies focus on the direct effects of digital transformation on firms’ risk responses and information transfer capabilities, while its broader effects on the supply chain have not been sufficiently investigated ( Guo et al., 2023 ).

The production operations of suppliers and their financial decisions are influenced and informed by their customers, who therefore play a vital role in the supply chain ( Ak and Patatoukas, 2016 ). Investing in supply chain relationships promotes the economic interdependence of suppliers and customers. The value of such investment depends on customers’ growth prospects, and ensuring the stability of their businesses can lead to higher expected returns from supply chain collaboration. Conversely, if customers face business challenges or go bankrupt, they can jeopardize the value of the assets allocated by suppliers ( Raman and Shahrur, 2008 ). This increases suppliers’ cash flow and operational risk ( Itzkowitz, 2013 ). Thus, suppliers are highly sensitive to the operations and activities of their customers. The risks and information associated with customers can generate a spillover effect in the supply chain, which can then trigger cascading changes in suppliers’ operational risks and influence their investments and financial decisions ( Hertzel et al., 2008 , Jacobson and von Schedvin, 2015 , Chiu et al., 2019 ). By gaining access to more transparent customer information, suppliers can better forecast customer demand, reduce transaction costs, alleviate shortages and losses and enhance production efficiency and inventory management ( Yang et al., 2020 ). Digital transformation can have positive effects on business risks and disclosure quality, in addition to the supply chain, but does the digital transformation of customer businesses also lead to additional spillover effects on suppliers?

Conclusion and discussion

Amid increased economic uncertainty, businesses are increasingly turning to digital transformation to enhance their resilience and optimize their resource allocation. The supply chain is a critical component of business operations that integrates logistics, information flow and capital flow. Microenterprises aiming to gain a competitive advantage can enhance their collaborations within supply chains. We investigated the spillover effect of customer digital transformation on suppliers through assessing its impact on audit fees. This extends research on the economic consequences of digital transformation beyond the boundaries of supply chain relationships. Our empirical findings indicate that customers’ digital transformation can reduce suppliers’ audit fees. Our heterogeneity analysis shows that this effect is more evident when there is a greater geographic distance between suppliers and customers, higher levels of dedicated investment and increased competitiveness in the supplier market. Through the economic mechanism of mitigating supply chain risk and facilitating collaboration and information transfer in supply chains, customers’ digital transformation reduces suppliers’ audit risks and costs, thus leading to lower audit fees.

Based on previous research findings, we offer the following conclusions. First, the implementation of digital transformation by suppliers’ customers can potentially cause a spillover effect across the supply chain, thereby affecting the economic interests and decisions of suppliers. Consequently, when faced with fierce market competition, firms should fully realize the beneficial effects of digital transformation on information transfer efficiency and collaboration within the supply chain. Such transformation should therefore be actively promoted, because through it firms can facilitate a seamless connection of resources, information and knowledge within the supply chain, thus fostering sustainable economic growth through enhanced coordination and cooperation.

Abstract

Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address how explainable artificial intelligence can impact decision-making processes. Using an experimental design, empirical data is collected to test the research model. This paper is one of the pioneer papers providing empirical evidence about the impact of explainable artificial intelligence on supply chain decision-making processes. We propose a serial mediation path, which includes transparency and agile decision-making. Findings reveal that explainable artificial intelligence enhances transparency, thereby significantly contributing to agile decision-making for improving cyber resilience during supply chain cyberattacks. Moreover, we conduct a post hoc analysis using text analysis to explore the themes present in tweets discussing explainable artificial intelligence in decision support systems. The results indicate a predominantly positive attitude towards explainable artificial intelligence within these systems. Furthermore, the text analysis reveals two main themes that emphasize the importance of transparency, explainability, and interpretability in explainable artificial intelligence.

Introduction

Automotive industry leaders, such as Tesla, have made substantial investments in artificial intelligence (AI) to expedite the introduction of self-driving vehicles to the market, enhancing their competitive capabilities. The integration of AI in supply chain operations has played a crucial role in enabling Tesla to optimize its operational costs [64] while simultaneously facilitating the establishment of a Gigafactory in China [78]. We are witnessing a rapid digital transformation driven by the integration of AI in supply chain management. The COVID-19 pandemic forced companies and organizations to expedite the digitalization of their operations [6]. To enhance their competitive edge, prominent companies, including Amazon, Walmart, Alibaba, Siemens, and Toyota have embraced AI-based technologies to automate and digitalize their operations and supply chain activities [1,32]. Digital transformation also introduces new possibilities for potential cyberattacks. However, using AI-based technologies for decision-making during cyberattacks (e.g., American Express monitoring; [75]) offers significant advantages that outweigh the potential losses incurred.

Although AI-based technologies can contribute to decision-making processes in operations and supply chain management, many industry players are lagging behind pioneering companies in utilizing AI-driven technologies, which is a main problem. Therefore, the adoption of AI-based technologies in decision-making processes, particularly during sensitive situations such as cyberattacks, may encounter potential barriers that delay their usage. Due to its vital advantages, AI has received much attention from decision-makers to address resilient case studies [45] and other sensitive problems such as healthcare [69]. The lack of explanations of the underlying AI processes leads to the rejection of AI in decision support systems [60]. Leveraging AI-powered decision-making platforms can significantly facilitate and expedite the decision-making process, which results in improved overall performance. For instance, the Colonial Pipeline, a U.S. oil supplier, faced a cyberattack and, after a week of deliberation, opted to pay around $4.4 million to solve the issue [21]. A quick decision on the first day through agile decision-making could have saved them money and enabled the uninterrupted continuation of their operations with stockholders.

Results

We employed regression analysis to test the hypothesized relationship between research model variables. We conducted required tests relating to the experimental design along with method assumptions and bias checks.

4.1. Design check
To check the design, the scenario test, the realism test, the validity test, and the manipulation test were performed. We conducted the scenario check to measure the participants’ learning process regarding the provided information. The results showed that participants’ learning was strong enough to support the scenario design (in the first scenario, the Mean = 5.3 out of 7, with a Standard Deviation = 0.99, and in the second scenario, the Mean = 5.5 out of 7, with a Standard Deviation = 0.85). The realism check was conducted through a face validity process and tested using the quantitative approach proposed by Thomas et al. [65]. Participants supported the realism check by agreeing that the scenarios were close enough to reality (Mean = 4.08 out of 5; Standard Deviation = 0.44). Similarly, the validity check supported the scenario design (Mean = 4.07 out of 5; Standard Deviation = 0.43). The manipulation test supported the manipulation by showing a significant difference between the two manipulated levels (p-value <0.001, the first level, the low level of XAI: Mean = 2.68 out of 7; Standard Deviation = 0.12; and the second level, the high level of XAI: Mean = 5.9 out of 7; Standard Deviation = 0.10).

Abstract

This study aims to investigate the influence of blockchain and artificial intelligence on the audit quality of firms from Turkey. Primary data from 300 respondents are collected through random sampling to attain the study’s objectives. PLS-SEM is used to investigate the relationship between exogenous and endogenous variables. Our findings show that blockchain technologies and artificial intelligence (AI) utilization in their financial system positively impact audit quality by assisting in the audit process and the detection of fraud, which also improves financial reporting. Blockchain and Artificial Intelligence in the financial system create confidence for investors, stakeholders, and legislators. Moreover, this study advocated significant implications for investors, government, firms, and policymakers. Investors can make investment decisions based on the accuracy of the financial accounts; the government and policymakers can improve the governance mechanism by using the study’s findings.

Introduction

Audits play a vital role in ensuring compliance with laws, regulations, and accounting standards. Companies are subject to various regulatory requirements and must adhere to accounting principles and reporting frameworks [1]. High-quality audits provide independent assurance that these compliance requirements are met, reducing the risk of penalties, legal consequences, or reputational damage [2]. Quality audits contribute to effective corporate governance by holding management accountable and independently assessing the company’s financial reporting [3]. By examining internal controls and risk management processes, audits help identify weaknesses and areas for improvement, enhancing organizational transparency and accountability. Audits protect the interests of various stakeholders, including employees, customers, suppliers, and lenders [4]. By assuring the accuracy and reliability of financial statements, audits help safeguard the investments, jobs, and contractual relationships associated with a company. This promotes trust and stability in the business environment.

Auditing quality is an interesting research problem and is influenced by many factors such as the blockchain, Artificial Intelligence, professional skepticism, auditor independence, audit firm size, regulatory environment, technological advancements, and the adoption of innovative audit tools. Professional skepticism plays a pivotal role in audit quality [5]. Auditor independence, ensuring auditors maintain an unbiased stance, is fundamental for objective and reliable audit outcomes [6]. The audit firm size has been identified as a factor, with larger firms often having more resources and expertise to deliver higher-quality audits [7]. The regulatory environment and oversight mechanisms significantly impact audit quality, as stringent regulations and effective oversight contribute to a more robust auditing process [8]. Technological advancements and the adoption of innovative audit tools have emerged as crucial factors shaping audit quality in the contemporary business landscape [9]. Understanding the intricate interplay of these factors is essential for advancing research and enhancing the overall quality and effectiveness of auditing practices. Therefore, these factors are widely discussed in the previous literature in different demographic contexts but lack the investigation on Artificial Intelligence (AI) and blockchain technology in relation to audit quality in the modern world.

Conclusion and recommendations

Our findings show that AI improves audit quality by providing assistance and timely fraud detection. AI’s data analysis capabilities, ability to identify complex fraud patterns, and real-time monitoring contribute to more effective and efficient audits. As AI advances, it holds tremendous potential to enhance auditing practices’ overall quality and effectiveness. This study provides specific insights into the implications of these technologies within Turkey and also other varieties of implications for investors, management, stakeholders, and policymakers. The study assessed the level of adoption and readiness of Turkish auditing firms to embrace blockchain and AI technologies. It identified the factors influencing the adoption process, including organizational culture, infrastructure, resources, and regulatory environment. Understanding the current state of technology adoption in Turkey sheds light on the potential implications for audit quality.

The implications of blockchain and AI on fraud detection and risk assessment practices in Turkish audits can be explored. The study assessed how these technologies improve the ability to detect and prevent fraud, including identifying unusual patterns, real-time monitoring of transactions, and using predictive analytics. It can also examine the impact of risk assessment methodologies and the ability to assess risks accurately. The investor can invest by seeing the audit quality of the firms within Turkey to protect their investment, and the government can make the legislation in the weakness and delayed audit without enjoying the blockchain technologies and the other factors. Future research should be conducted on the efficiency of blockchain technology that is effective in the accounting system and preparation of the financial system that makes it easier for the technologies to improve the audit quality. Some more Blockchain units should be included in further research. Due to the limitation, we included one economy in our investigation; future research should include more than one and compare each economy, the use, and the efficiency of the artificial intelligence system.

Abstract

With the continuous development of technology, traditional marketing methods no longer meet the needs of the main forces of social consumption, and people urgently need more innovative and personalized marketing strategies. E-commerce companies must develop a comprehensive customer-oriented marketing strategy based on big data and multi-channel to achieve their long-term healthy development. This paper first investigated the impact of the digital economy on e-commerce enterprises, focused on the transformation of the digital economy on the marketing model, expounded the development analysis of e-commerce in the digital economy era, and described the development trend of e-commerce marketing in the digital economy era. Then, this paper expounded the current problems faced by e-commerce enterprises, and discussed the lack of integrity, homogeneity, large-scale marketing strategies, and the lack of analysis and application of big data. After that, this paper put forward the marketing strategy of e-commerce enterprises in the digital economy era, and studied it from three aspects, namely, building a reasonable product management structure, marketing strategy based on customized marketing content, and social media marketing strategy based on information sharing. Then this paper proposed to use genetic algorithm to strengthen the marketing strategy of e-commerce enterprises. Finally, based on experiments and surveys, this paper used genetic algorithms to strengthen the construction of e-commerce enterprise marketing strategy in the digital economy, and concluded that the new e-commerce enterprise marketing strategy was 21% more satisfactory than the traditional new e-commerce enterprise marketing strategy. Through comparison, it can see that the integrity of the marketing plan of the new e-commerce enterprise’s marketing strategy was 0.33 higher than that of the traditional e-commerce enterprise’s marketing strategy, and the integrity of the promotion strategy was 0.34 higher than that of the traditional e-commerce enterprise’s marketing strategy. After using the new e-commerce enterprise marketing strategy, the improved management structure was 0.29 higher than that of the traditional monitoring system, and the high quality of products was 0.18 higher than that of the traditional system.

Introduction

Electronic commerce is the combination of Internet information technology and traditional offline business activities. With the development and maturity of Internet technology, e-commerce continues to grow. Its concept, connotation and meaning evolve gradually with the evolution of technology and actual e-commerce activities. From a historical perspective and combined with the development of information technology, the evolution of the concept of e-commerce can provide a new perspective for observing the development of new forms of e-commerce. In 2023, the Ministry of Commerce implemented the decisions and deployments of the Party Central Committee and the State Council to accelerate the development of the digital economy, promote the important role of e-commerce in restoring and expanding consumption, promoting the integration of data and reality, and deepening international cooperation, and achieve positive results in high-quality development. With the rapid development of the digital economy, e-commerce enterprises have also experienced a golden age of development. Through the analysis and research of relevant industry data, it is not difficult to find that e-commerce business cannot be separated from a large number of consumers. E-commerce enterprises can develop normally with enough consumers. In order to survive in the highly competitive market, e-commerce enterprises must make full use of advanced big data analysis technology and formulate marketing strategies suitable for the development of e-commerce enterprises. Main contribution: This article mainly focuses on the research of marketing strategies related to e-commerce enterprises in the context of big data, and proposes corresponding improvement strategies. Objective: This article aims to promote the healthy and long-term development of e-commerce enterprises in the context of big data through improved marketing strategies based on this foundation.

Conclusions

Currently, how to promote their own development through effective marketing strategies has become an important issue that e-commerce enterprises need to pay attention to in the context of big data. This article mainly studies the marketing strategy related issues of e-commerce enterprises with big data as the background, and proposes corresponding improvement strategies. This article aims to promote the healthy and long-term development of e-commerce enterprises in the context of big data through improved marketing strategies based on this foundation. To sum up, in the new economic era, the marketing strategy of enterprises is very important. The fierce competition between enterprises requires enterprises to constantly innovate, change marketing strategies and maximize market space. Therefore, every enterprise should strive to implement marketing strategies in an innovative spirit. Only by constantly changing marketing strategies and actively adapting to the needs of economic development can enterprises stand out from the competition. E-commerce companies are shifting from single and large-scale marketing strategies to homogeneous and diversified marketing strategies. E-commerce companies need to establish accurate integrated marketing based on big data, customer-oriented and multi-channel to survive and develop in the future competition. The development of genetic algorithms has led to exponential growth in the direct to consumer personal genetic testing industry (such as 23andMe, Ancestry, Microgenes, 23Rubik’s Cube, etc.), resulting in the formation of a vast private genetic database. The author explores the potential impact of genetic algorithms on the field of e-commerce. Based on the results of behavioral GA research, the author proposes to incorporate the influence of genetic factors into the existing theoretical research framework of e-commerce, and use this to examine the potential market applications of genetic algorithms. Genetic research design can be used to test causal relationships, and consumer theory can be improved by revealing the biological mechanisms of behavior. In the future, the author will further evaluate the moral and ethical challenges related to corporate autonomy, privacy, and discrimination, and propose a future research agenda.