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In the digital age, personalized services and accurate recommendations enhance the customer experience and streamline shopping. However, increasing concerns about personal privacy have led to resistance from consumers, necessitating a balance between providing high-quality online services and safeguarding personal data. The aim of this paper is to offer a comprehensive review of the fragmented literature on consumer privacy decision-making and to identify key issues worth exploring in future research.

Although previous studies have analyzed the antecedents and outcomes of privacy decisions, they have often been conducted in a fragmented manner. There remains a lack of a holistic understanding of the factors influencing privacy decisions, including their boundaries. Therefore, we build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We attempt to dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, ultimately providing a comprehensive framework for understanding these dynamics.

Based on the Theory of Planned Behavior, we analyze the entire process of consumer privacy decision-making in terms of antecedent, moderating and outcome variables. The results indicate that consumer privacy decision-making is not an isolated behavior or a single choice but a complex, multi-level dynamic process. The factors influencing consumer privacy decisions primarily encompass five aspects: individual characteristics, information, organization, platform and interaction management, leading to various outcomes in both behavioral and perceptual dimensions. Furthermore, the process is constrained by multiple moderating variables, such as information sensitivity, platform knowledge and prior experience.

We build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, aiming to provide a comprehensive framework for understanding these processes.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

Driven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.

This paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.

Several interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.

This study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.

The formation of unmanned aerial vehicle (UAV) swarm plays a critical role in numerous applications, such as unmanned agriculture, environmental monitoring and cooperative fencing. Meanwhile, the self-organized swarm model exhibits excellent performance in amorphous formation flight, and its collective motion pattern displays great potential in dense obstacle avoidance. The paper aims to realize the formation maintenance of UAVs while combining the advantage of the self-organized swarm model in avoiding dense obstacles. Thereby enhancing the flexibility, adaptability and safety of UAV swarms in dense and unpredictable scenarios.

In this paper, a self-organized formation (SOF) swarm model with a constrained coordination mechanism is proposed. A global information-based formation rule is designed to flexibly maintain the formation. A constraint coordination mechanism is designed to resolve the problem of constraint conflicts between formation rules and self-organized behavior rules. The model introduces a new obstacle avoidance rule to prevent deadlocks. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the model.

The simulation results show that SOF swarm enables the formation elastically to dense obstacles. Compared to the Vasarhelyi model, swarm performance metrics are improved. For example, the task completion time of SOF swarm is reduced by 16%, 28% and 39% across the three obstacle densities, and the order of SOF swarm is improved by 4%, 13% and 18%, respectively. The proposed model is also validated with a swarm of seven quadcopters that can successfully navigate and maintain formation in a real-world indoor environment with dense obstacles. Video at: https://youtu.be/V8hYgOHxWls.

The proposed formation rule is based on global information construction, which presents challenges in terms of communication overhead in distributed systems.

An SOF swarm model is proposed, which achieves formation maintenance by incorporating formation rule and constraint coordination mechanism and improves obstacle avoidance performance by introducing a new obstacle avoidance rule. After real UAVs verification, the model is feasible for practical deployment and provides a new solution to the formation flight and formation maintenance problems encountered in dense environments.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

The purpose of this paper is to analyze the mechanism of nanofluid enhanced heat transfer in microchannels and promote the application of nanofluids in industrial processes such as solar collectors, electronic cooling and automotive batteries.

The two-phase lattice Boltzmann method was used to calculate the flow and heat transfer characteristics of Al₂O₃ nanofluids in a microchannel at Re = 50. By comparing the simulation results of pure water, nanofluids without calculated nanoparticle-fluid interaction forces and nanofluids with calculated nanoparticle-fluid interaction forces, the effects of physical properties improvement and interaction forces on flow and heat transfer are quantified.

The findings show that the nanofluid (φ = 3%, R = 10 nm) increases the average Nusselt number by 22.40% at Re = 50. In particular, 16.16% of the improvement relates to nanoparticles optimizing the thermophysical parameters of the base fluid. The remaining 6.24% relates to the disturbance of the thermal boundary layer caused by the interaction between nanoparticles and the base fluid. Moreover, the nanoparticle has a negligible effect on the average Fanning friction factor. Ultimately, we conclude that the nanofluid is an excellent heat transfer working medium based on its performance evaluation criterion, PEC = 1.225.

To the best of the authors' knowledge, this research quantifies for the first time the contribution of nanoparticle-liquid interactions and nanofluids physical properties to enhanced heat transfer, advancing the knowledge of the nanoparticle's behavior in liquid systems.

Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy method.

This paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element method.

The study’s findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length growth.

The improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.