Search results

Electronic word-of-mouth (eWOM) has become one of the most influential information sources for consumers' purchase decision-making. Based on construal-level theory and from the perspective of cognitive effort, this study investigated the effects of eWOM social media types and conflicting eWOM on consumers' purchase intentions and validated the mediation role of social psychological distance, perceived value, and perceived cognitive effort.

Two scenario-based experiments were conducted to validate the research model. Specifically, a 2 (eWOM social media type: strong-tie vs weak-tie) × 2 (conflicting eWOM: with vs without) between-subjects design was used. ANOVA, multiple regression analysis with PROCESS, and partial least squares (PLS) were employed to test the hypotheses.

The results showed that eWOM social media types had significant effects on both social psychological distance and perceived value. The significant chain mediating effects of social psychological distance and perceived value between eWOM social media types and consumers' purchase intentions were found. In addition, conflicting eWOM had significant effects on both perceived value and perceived cognitive effort. Indirect effects of conflicting eWOM on purchase intention through perceived cognitive effort were discovered.

These results contribute to the eWOM literature by investigating the influences of eWOM social media types and conflicting eWOM on consumers' purchase intentions. They also contribute to construal-level theory by extending its applicability to the field of eWOM.

Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction.

Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes.

It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios.

This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.

In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM.

The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τ_c method and P_d method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations.

At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

This study investigates the relationship between the Chief Executive Officer's (CEO) overconfidence and financial reporting complexity in Iran, a context characterized by weak corporate governance and heightened managerial discretion.

The sample consists of 1,445 firm-year observations from 2010 to 2021. CEO overconfidence (CEOOC) is evaluated using an investment-based index, specifically capital expenditures. Financial reporting complexity (Complexity) is measured through textual features, particularly three readability measures (Fog, SMOG and ARI) extracted from annual financial statements. The ordinary least squares (OLS) regression is employed to test the research hypothesis.

Results suggest that CEOOC is positively related to Complexity, leading to reduced readability. Additionally, robustness analyses demonstrate that the relationship between CEOOC and Complexity is more distinct and significant for firms with lower profitability than those with higher profitability. This implies that overconfident CEOs in underperforming firms tend to increase complexity. Also, firms with better financial performance present a more positive tone in their annual financial statements, reflecting their superior performance. The findings remain robust to alternative measures of CEOOC and Complexity and are consistent after accounting for endogeneity issues using firm fixed-effects, propensity score matching (PSM), entropy balancing approach and instrumental variables method.

This study adds to the literature by delving into the effect of CEOs' overconfidence on financial reporting complexity, a facet not thoroughly investigated in prior studies. The paper pioneers the use of textual analysis techniques on Persian texts, marking a unique approach in financial reporting and a first for the Persian language. However, due to the inherent challenges of text mining and feature extraction, the results should be approached with caution.

The insights from this study can guide investors in understanding the potential repercussions of CEOOC on financial reporting complexity. This will assist them in making informed investment decisions and monitoring the financial reporting practices of their invested companies. Policymakers and regulators can also reference this research when formulating policies to enhance financial reporting quality and ensure capital market transparency. The innovative application of textual analysis in this study might spur further research in other languages and contexts.

This research stands as the inaugural study to explore the relationship between CEOs' overconfidence and financial reporting complexity in both developed and developing capital markets. It thereby broadens the extant literature to include diverse capital market environments.

As quarterly business reviews (QBRs) remained unexplored in the scholarly community, this paper sheds light on what QBRs are, how they are being used across organisations and provides deep insights into the implementation of the QBR at an incumbent car manufacturer’s digital transformation business unit. Particular attention has been paid to decision processes, portfolio management, challenges and success factors.

Given the explorative nature of the research, a case study is well suited to explore the phenomenon in its real-world context, especially given the dynamic and volatile business environment. This article is based on insights from an incumbent car manufacturer undergoing a business-wide transformation.

The car manufacturer introduced the QBR process and themes to improve business effectiveness and efficiency through (1) focusing on the biggest issues, (2) concentrating efforts, (3) providing autonomy and stability, (4) building and maintaining strong relationships, and (5) building domain expertise. Through the QBR process, themes were (de)prioritised, resources allocated, financial value (estimates) agreed upon, and key performance indicators (e.g. £m/FTE; FTE, full-time equivalent employees) introduced. Digital product managers’ were assigned to the prioritised themes, and portfolio management structures were presented.

Managing short- and long-term objectives is challenging for most businesses but essential to perform well in uncertain environments. The QBR process can help organisations continuously (de)prioritise work and reallocate resources based on changing environments and aligned with strategic priorities.

Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels.

An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling.

The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive.

Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated.

This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels.

This study aims to investigate the intricate relationship between intellectual capital and environmental innovations among manufacturing medium and large firms in Uganda, utilizing the SmartPLS methodology.

This research adopts a cross-sectional and quantitative approach, collecting data through a questionnaire survey from a sample of manufacturing medium and large (ML) firms in Uganda. The collected data underwent analysis to identify patterns and relationships using the SmartPLS structural equation modeling (SEM) technique.

The findings highlight a distinct pattern: structural capital is the strongest predictor of environmental innovations, with human capital being the next most significant factor. However, the positive relationship with relational capital did not attain statistical significance, suggesting the need for further exploration into inter-firm relationships.

For managers, investing in robust organizational structures and human capital development programs can enhance firms’ capacity to drive eco-friendly initiatives, aligning with global sustainability agendas. Policymakers are encouraged to create an enabling environment that nurtures IC and incentivizes environmental innovation through supportive policies such as tax incentives and funding mechanisms for green technologies.

This study enriches the intellectual discourse on IC and environmental innovation by employing SmartPLS methodology to highlight the nuanced impact of its components, emphasizing the multifaceted nature of IC and its role in driving EI.

During the planting process, agricultural products produce large amounts of greenhouse gas (GHG) emissions. This has placed tremendous pressure on sustainable global development. Many countries and regions in the world have adopted intensive subsistence cultivation methods when planting maize; however, limited studies exist on these methods. The main purpose of this research is to show the impact of climate change on maize yields and carbon footprint (CF) in South Korea over 10 years, find the proper operating method and promote the advanced combination of inputs for the sustainable development of maize farmers.

This study used survey data from the South Korea Rural Development Administration of 2010, 2014 and 2019 to estimate the CF of maize planting under intensive subsistence cultivation. Life-cycle assessment was used to determine the CF. Farmers were grouped according to significant differences in yield and GHG emissions. Linear regression was used to measure the dependence of the main contributors on the CF production and carbon efficiency.

In South Korean maize planting, N in chemical fertiliser was the most significant contributor to the CF and organic fertiliser was the most significant input. The use of chemical and organic fertilisers significantly affects the production of the CF and carbon efficiency. Households in the high-yield and low-GHG emission groups are more sustainable because they generate the least GHG when producing and earning through maize cultivation. Globally, maize production in South Korea has a relatively low CF and maize production produces fewer GHG.

This study provides information for policymakers to determine key operational options for reducing GHG emissions using intensive subsistence cultivation of maize production in South Korea and other countries.

This paper aims to examine the mediating role of knowledge sharing (KS) on the relationship between the transformational leadership (TL) and innovation in banks.

Quantitative analysis was conducted by using the structural equations modeling with AMOS 24 to examine the influence of the mediating role of KS on the TL–innovation relationship. Data were collected from 310 employees at 27 banks in Lebanon.

The research highlights that leaders exhibiting transformational behavior were able to promote knowledge-sharing culture that enhances the generation of new ideas, products and processes. The findings confirmed that KS mediates the association of TL and innovation.

The findings point to how TL mobilizes employees to engage in innovative products and processes by encouraging a knowledge-sharing culture.

The research findings advance the understanding of how TL stimulates innovation and highlights the benefits gained by cultivating KS to generate more innovative outcomes.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.