Search results

This paper aims to explore the role of source credibility while purchasing environment-friendly products using Ajzen’s (1991) theory of planned behavior as underpinning model.

The proposed theoretical model was empirically tested with the data collected from 334 respondents using structural equation modeling.

The results gave empirical support to the addition of source credibility to the original theory of planned. Moreover, consumer attitude was found mediating the effect of corporations’ credibility on purchase intention. Also, attitude and perceived behavioral control were found as the most important predictors of consumer’s intention to purchase environment-friendly products.

This study provides valuable insights for the marketers engaged in sustainable business practices. Amid, ever-increasing carbon emission, promoting the use of environment-friendly products has become the need of the time. Credibility plays a crucial role while promoting and communicating an organization’s sustainable practices among its stakeholders including consumers. Therefore, the marketer should formulate appropriate marketing communication strategy to communicate the consumer about the green practices and environment-friendly products they produce. The results suggest that corporation’s credibility shapes consumer attitude and influences intention to purchase environment-friendly products. Earning trust of the consumer is pivotal to achieve success in the market. Therefore, results may help the marketers to better understand consumer’s response toward their marketing strategies and further convince and persuade them to buy their products.

The findings of this study may be useful for marketers, strategists, policymakers and government while formulating promotional strategies to make consumer aware, educate and persuade them to purchase products which do not cause harm to the environment.

The study is novel in terms of exploring role of source credibility and extending theory of planned behavior in the context of sustainable consumption.

Electricity plays an essential role in nations' economic development. However, coal and renewables currently play an important part in electricity production in major world economies. The current study aims to forecast the electricity production from coal and renewables in the USA, China and Japan.

Two intelligent grey forecasting models – optimized discrete grey forecasting model DGM (1,1,α), and optimized even grey forecasting model EGM (1,1,α,θ) – are used to forecast electricity production. Also, the accuracy of the forecasts is measured through the mean absolute percentage error (MAPE).

Coal-powered electricity production is decreasing, while renewable energy production is increasing in the major economies (MEs). China's coal-fired electricity production continues to grow. The forecasts generated by the two grey models are more accurate than that by the classical models EGM (1,1) and DGM (1,1) and the exponential triple smoothing (ETS).

The study confirms the reliability and validity of grey forecasting models to predict electricity production in the MEs.

The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.

This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.

It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.

The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.

This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

This paper aims to explore how parasocial relationships with Korean celebrity endorsers on social media result in brand credibility and loyalty.

The participants were identified through a purposive sampling approach, and they were composed of consumers who purchased Korean-celebrity-endorsed products and services of a telecommunications company. The hypothesized relationships were gauged using a predictive approach as a research design via partial least squares (PLS) path modeling.

The findings show that all hypothesized relationships are supported. In particular, social media interaction was found to have a substantial, positive and significant effect on self-disclosure. Moreover, self-disclosure has a considerably significant and direct effect on parasocial relationships and was found to indirectly affect the link between social media interactions and parasocial relationships. The results further reveal that social media interactions and parasocial relationships predict source trustworthiness, leading to brand credibility and loyalty.

To the best of the authors’ knowledge, the present undertaking is the only study that examined how parasocial relationships on social media are built when foreign celebrities, in this case, the well-known Korean popular group BTS, endorse telecommunications products and services.

La presente investigación explora cómo las relaciones parasociales con celebridades coreanas en las redes sociales generan credibilidad de marca y lealtad.

Los participantes se identificaron mediante un muestreo intencional y estaban compuestos por consumidores que compraban productos y servicios de una empresa de telecomunicaciones avalados por famosos coreanos. Las relaciones hipotetizadas se midieron utilizando un enfoque predictivo como diseño de investigación mediante un modelo de mínimos cuadrados parciales (PLS).

Los resultados muestran que todas las relaciones hipotetizadas se confirman. En particular, la interacción con los medios sociales tiene un efecto sustancial, positivo y significativo en la autodivulgación. Además, la autodivulgación tiene un efecto considerablemente significativo y directo en las relaciones parasociales y se descubrió que afecta indirectamente al vínculo entre las interacciones en los medios sociales y las relaciones parasociales. Los resultados revelan además que las interacciones en los medios sociales y las relaciones parasociales predicen la fiabilidad de la fuente, lo que conduce a la credibilidad de la marca y a la lealtad.

El presente trabajo es el único estudio que examina cómo se construyen las relaciones parasociales en los medios sociales cuando celebridades extranjeras, en este caso, el conocido grupo popular coreano BTS, promocionan productos y servicios de telecomunicaciones.

本研究探讨了在社交媒体上与韩国名人的寄生关系如何建立品牌可信度和忠诚度。

通过目的性抽样确定参与者, 包括购买韩国名人代言的电信公司产品和服务的消费者。研究设计使用偏最小二乘法（PLS）模型对假设关系进行预测测量。

研究结果表明, 所有假设关系都得到了证实。特别是, 社交媒体互动对自我披露具有实质性的、积极的和显著的影响。此外, 自我披露对寄生关系也有明显的直接影响, 并被发现间接影响社交媒体互动与寄生关系之间的联系。研究结果进一步揭示了社会化媒体互动和寄生关系能够预测来源的可信度, 从而提高品牌可信度和忠诚度。

研究结果表明, 所有假设的关系都得到了证实。特别是, 社交媒体互动对自我披露具有实质性的、积极的和显著的影响。此外, 自我披露对寄生关系也有明显的直接影响, 并被发现间接影响社交媒体互动和寄生关系之间的联系。研究结果进一步揭示了社会化媒体互动和寄生关系能够预测来源的可信度, 从而提高品牌可信度和忠诚度。

本文是唯一一篇研究外国名人在社交媒体上推广电信产品和服务时如何建立寄生社会关系的研究。

In view of the influences of online videos on the cultural tourism industry, this study aims to explore the mechanisms triggered by watching online video behaviors of fans. This study examines how fans who have watched celebrities’ online videos become tourists who attend concerts held at destinations based on celebrity and destination endorsements.

This study builds for celebrity and destination endorsements on online videos by combining media richness and source model theory. This research adopts partial least squares structural equation modeling to analyze the mechanism triggered by online media.

Watching online media influences celebrity and destination endorsements, which, in turn, affects the concert experience and intention to return to the destination. Results reveal less intertwined relationships between celebrity and destination endorsements and the complex mechanisms between the two endorsements.

With the rise in popularity of online media, online content has become a major source of information in the tourism industry and a means of enjoying travel seamlessly. This study highlights not only the role of “watching online videos” as one of the richest media but also the role of live concerts in cultural tourism for understanding complex cultural tourism.

为了探讨在线视频对文化旅游业的影响, 本研究探讨了观看在线视频所触发旅游行为的机制。本研究考察了在名人和景区的宣传下, 观看名人在线视频的粉丝如何成为参加目的地举办的演唱会的旅游者。

基于媒体丰富度和来源模型理论, 本研究调查了名人与景区的宣传视频。本研究采用partial least squares（PLS）结构方程建模来分析在线媒体所引发的机制。

观看在线媒体影响了名人和目的地宣传效应, 进而影响了演唱会的体验和到访目的地的意愿。研究结果揭示了名人和目的地宣传之间的关系并不紧密, 这两种宣传之间存在着复杂的机制。

随着在线媒体的普及, 在线内容已成为旅游业中信息的主要来源和享受旅游的手段。本研究不仅突出了“观看在线视频”作为最丰富的媒体之一的作用, 也突出了演唱会在文化旅游中的功能, 以更全面地理解复杂的文化旅游。

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

Consumers are increasingly spending more time on social media platforms to cope with anxiety and loneliness resulting from the recent pandemic. The extraordinary times, characterised by isolation and lockdown, has placed increasing dependence on online technology as a coping mechanism in which social media influencers (SMIs) served as the human face of brands, helping both consumers and brands maintain human connection. How should practitioners navigate their social media campaigns in this changing environment?

To answer this question, the Source Connectedness Pyramid is proposed to help explain and compare the interactions between consumers and SMIs in ordinary and extraordinary times.

In their interactions with influencers during ordinary times, consumers are satiated with influencer source characteristics of attractiveness, trustworthiness and expertise. However, during extraordinary times, consumers substitute their usual preference to focus on connectedness, characterised by relatedness, belongingness and attachment.

The empirical study within this paper lends support to this proposition and offers additional insights. The proposed Source Connectedness Pyramid contributes to influencer communication theoretically and has strategic implications for practitioners when navigating their social media campaigns in these extraordinary times.