Search results

Despite the growing importance of digital transformation, few studies have investigated its precise effects on firm efficiency. This research explores the differential effects of digital transformation on the profitability and marketability of manufacturing firms.

We analyze the relationship between digital transformation and firm efficiency using a dataset of Chinese-listed manufacturing firms from 2011 to 2023.

The results indicate that digital transformation improves marketability and has a U-shaped relationship with profitability. Moreover, industry competition amplifies the positive effect of digital transformation on marketability but attenuates its U-shaped effect on profitability. In contrast, media coverage attenuates the positive effect of digital transformation on marketability and amplifies its U-shaped effect on profitability.

While the existing conclusion about the efficiency of digital transformation is inconsistent, this research enriches the literature on digital transformation and provides insights for improving firm efficiency in terms of both profitability and marketability.

The main purpose of this study is to examine the effect of corporate digital transformation on bond credit spreads. Additionally, it also explores the two potential channels, information asymmetry and default risk, through which digital transformation can influence bond credit spreads.

We use the bond issuance data of Chinese listed companies over the period 2008–2020. Corporate digital transformation of these companies is measured with textual analysis of the management discussion and analysis part of annual reports. We employ a panel regression model to estimate the effect of digital transformation on bond credit spreads.

We find robust evidence that companies with higher digital transformation experience lower bond credit spreads. We further observe that credit spread reduction is higher for firms that are smaller, non-state-owned, have lower credit ratings and have less analyst coverage. We also find evidence that digital transformation reduces credit spreads by reducing the information asymmetry between firms and investors with enhanced information transformation mechanisms and lowering corporate default risk by strengthening operating efficiency.

To the best of our knowledge, this study is the first attempt to understand the impact of corporate digital transformation on bond credit spreads. Our findings help to understand the effect of digital transformation on firms’ credit worthiness and access to capital.

This study explores the impact of digital technologies and digitalization management on digitalization performance in Lithuanian manufacturing firms, aiming to unravel the dynamics between digital technology adoption and managerial capabilities in enhancing digitalization performance.

Employing partial least squares structural equation modeling (PLS-SEM), the research analyzes data from a survey of 506 Lithuanian manufacturing firms, focusing on their digitalization strategies and outcomes.

The findings reveal that while digital technologies alone do not directly influence digitalization performance, digitalization management significantly mediates this relationship, highlighting the pivotal role of managerial practices in maximizing the benefits of digital technologies.

The study acknowledges limitations in its scope, primarily focusing on Lithuanian manufacturing firms, which may affect the generalizability of its findings to other sectors or geographical contexts.

The study offers valuable insights for practitioners and managers, underscoring the importance of strategic management in leveraging digital technologies for enhanced digitalization performance and providing a roadmap for more effective digital transformation practices.

This research elucidates the intricate dynamics between digital technologies, digitalization management and digitalization performance, revealing a pivotal mediating role of digitalization management. It notably demonstrates that digital technologies, contrary to expectations, do not directly influence digitalization performance, underscoring the essential function of digitalization management in harnessing digital technologies for enhanced performance.

The purpose of this study aims to shorten the running-in time and improve the running-in quality of cylinder liner piston rings (CLPRs), the running-in tests were carried out and running-in parameters of CLPRs were designed based on running-in attractor theory, which can guide the choice of optimal working conditions for other friction pairs.

The running-in state and time under different working conditions are identified by the evolution law of the running-in attractor phase trajectory and fractal and chaotic characteristic quantities. The CLPRs running-in tests under different conditions were conducted and the friction signals were collected. The constructed phase trajectories and calculated chaotic parameters of the running-in attractor are obtained and the running-in state and time are identified by the evolution law of phase trajectories and chaotic characteristic quantities. The running-in quality is obtained by the surface morphology fractal dimension and characteristic roughness parameters.

The running-in parameters for short running-in time and good running-in quality are designed based on the fractal and chaotic theory and the optimal solution method are used to verify the results through the single objective or multi-objective optimization, and the corresponding optimal running-in parameters are obtained.

The optimal working condition parameters obtained from the design have guiding significance for the selection of CLPR running-in parameters, and this work can provide ideas for the other friction pairs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0179/

This study aims to address the issues of limited pipe diameter adaptability and low inspection efficiency of current pipeline inspection robots, a new type of pipeline inspection robot capable of adapting to various pipe diameters was designed.

The diameter-changing mechanism uses a multilink elastic telescopic structure consisting of telescopic rods, connecting rods and wheel frames, driven by a single motor with a helical drive scheme. A geometric model of the position relationships of the hinge points was established based on the two extreme positions of the diameter-changing mechanism.

A pipeline inspection robot was designed using a simple linkage agency, which significantly reduced the weight of the robot and enhanced its adaptive pipe diameter ability. The analysis determined that the robot could accommodate pipe diameters ranging from 332 mm to 438 mm. A static equilibrium equation was established for the robot in the hovering state, and the minimum pressing force of the wheels against the pipe wall was determined to be 36.68 N. After experimental testing, the robots could successfully pass a height of 15 mm, demonstrating the good obstacle capacity of the robot.

This paper explores and proposes a new type of multilink elastic telescopic variable diameter pipeline inspection robot, which has the characteristics of strong adaptability and flexible operation, which makes it more competitive in the field of pipeline inspection robots and has great potential market value.

The robot is characterized by the innovative design of a multilink elastic telescopic structure and the use of a single motor to drive the wheel for spiral motion. On the basis of reducing the weight of the robot, it has good pipeline adaptability, climbing ability and obstacle-crossing ability.

This study aims to investigate how two different types of brand-to-brand dialogues – “roasting” versus “toasting” – impact consumers’ brand perceptions, particularly perceived entertainment, and influence brand attitudes.

The research design comprises four studies. The preliminary study involves Web scraping to gauge consumer perception about the two communication approaches followed by two well-known brands. Study 1 involves an online experiment to compare these communication types within each brand tested in the pilot study and examines the mediation effect of perceived entertainment. Study 2, also an online experiment, investigates the role of message neutralization, demonstrating that “roasting” can be acceptable when the humor is neutralized. Study 3 further tests the effects of neutralized “roasting” at different levels of brand familiarity and personality.

Roasting can lead to more favorable consumer perceptions than toasting. The effect can be explained by roasting’s higher level of perceived entertainment. However, this positive outcome is contingent on the successful neutralization of the aggressive humor in the “roasting” messages. When it comes to brand familiarity and personality, familiar brands benefit more from neutralized “roasting,” whereas brand personality does not have a strong influence.

The findings suggest that “roasting” can be effective when messages are neutralized, and “toasting” works best when spontaneous and genuine. It highlights how brand familiarity and personality influence consumer reactions, thus, offering strategic insights for both established and lesser-known brands. The study also prompts further research to examine other brand traits, cultural factors and behavioral dimensions in brand-to-brand dialogue, signifying the complexity and richness of this growing research area.

This study advises lesser-known brands to adopt “toasting” strategies to build a positive image, while established brands can try “roasting,” ensuring message neutrality to avoid negativity. The research emphasizes the role of brand familiarity and personality in shaping brand dialogues. Marketers must consider these to make humor strategies effective and bolster positive brand image.

This research uniquely examines message neutralization through contextual cues as a strategy brands can use to aid their sensitive dialogues with others on social media. The findings provide new insights into how brands can use different types of messages in digital communications to attract consumers and ensure positive reception, offering valuable guidance for academics and practitioners in brand-to-brand dialogue.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The primary aim of this field research study is to fill the void about the long-run performance of the crosslisting event of companies listed on NYSE-Euronext Paris.

Our sample consisted of an overall sample of 138 listed companies officially listed on the French Stock Exchange over the period 1994–2019 using three empirical methods, including the Time Abnormal Return (CTAR) calendar, the three-factor model of Fama and French (1993), and the method of Fama and Macbeth (1973).

We find significant long-term underperformance. Over the long term, the returns of cross-listed companies are lower than the returns of control companies. Also, we find that cross-listed companies' performance deteriorates over the long term.

This study can help investors and financial analysts make informed decisions about the timing and effectiveness of investment strategies. In addition, it contributes to academic research to assess the efficiency of capital markets and provide evidence on the effectiveness of market regulations.

This study differs from previous studies in terms of applying a variety of different statistical methods to test the existence of abnormal long-term performance after the crosslisting announcement and the first study that analyses the long-term performance of cross-listed firms in the French context.

This research study aims to delve into the enduring relationship between housing property prices and economic policy uncertainty across eight major Indian cities.

Using the panel non-linear autoregressive distributed lag model, this study meticulously investigates the asymmetric impact of economic policy uncertainty on apartment and house (unit) prices in India during the period from 2000 to 2022.

The findings of this study indicate that economic policy uncertainty exerts a negative influence on property prices, but noteworthy asymmetry is observed, with positive changes in effect having a more pronounced impact than negative changes. This asymmetrical effect is particularly prominent in the case of unit prices.

This research reveals that long-run price trends are also influenced by factors such as interest rates, building costs and housing loans. Through a comprehensive analysis of these factors and their interplay with property prices, this research paper contributes valuable insights to the understanding of the real estate market dynamics in Indian cities.

Purpose: In Greece, large companies have started to focus more and more on corporate social responsibility (CSR) and ESG (environmental, social, and governance) activities, realising the importance of sustainability and social responsibility beyond traditional profits. Using machine-learning (ML) methods and artificial neural networks (ANNs) can enhance the process of measuring performance in these areas in several ways, including data analytics. This paper investigates and explores the correlation between CSR and ESG actions with financial and non-financial factors for the 100 largest companies operating in Greece.

Methodology: The study runs from January 2019 until December 2021, and ANNs and ML techniques are employed. The comparison concerns both the control variables and the predictability of the methods.

Findings: The main findings that emerged are the confirmation of the correlation between CSR and ESG actions and the financial performance and determinants of corporate responsibility of the companies in the sample. Moreover, good results were obtained for almost all of the techniques examined, but the superiority of deep learning models and gradient-boosted trees (GBTs) was found for the selected variables.

Significance/Implications/Conclusions: The findings suggest that using ML techniques and neural networks to measure CSR actions can help companies evaluate their performance and make effective decisions to improve their sustainability. It can also be a valuable tool for institutional investors, banks, and regulators.

Future Research: We believe that future research should focus on improving these models, exploring hybrid approaches that combine the strengths of different techniques, and expanding the range of variables considered.