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The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

This is an acceptable fact that firms put efforts to maximize shareholders wealth but there is growing demand that firms are also accountable to various stakeholders associated directly or indirectly with the firms' business activities. Investors now evaluate firm's performance not only from financial perspective but also consider environment, social, and governance (ESG) factors when taking investment decision. ESG is not visible in firm's annual financial reports but investors do not deny its significance when valuing firms. There are increasing interests in ESG by communities, professionals, and government bodies, and all are interested to keep it as part of firms' regular activity and have to relate it with firm performance and efficiency that affects firm value. Still, there are difficulties in integration of ESG factors into investment decision-making, but efforts are being put to overcome all the issues. Firms which consider ESG are in a good position to achieve their long-term financial goals as they are likely to attract capital, lower borrowing costs, mitigate risks, and maximize shareholders value.

A digital servitization ecosystem (DSE) is a cooperation model based on the concept of value cocreation. However, capability asymmetry among enterprises can lead to unfair benefit distribution and hinder value cocreation and digital service transformation. This paper aims to investigate the impact of the varying capabilities of enterprises (manufacturers, service providers and digital technology providers) on revenue distribution when these enterprises collaborate on digital servitization transformation. This analysis is performed from an ecosystem perspective to facilitate the stable development of DSEs.

The rise of DSEs has engendered extensive literature, and the distribution of benefits within DSEs is in dire need of new mechanisms to adapt to the new competitive environment. The importance of investment contribution, digital servitization level, digitalization level, risk-taking ability, digital servitization effort level and brand awareness is determined by combining the expert scoring method and the entropy value method to determine different weights for manufacturers, service providers and digital technology providers. The Shapley value is used to design the benefit distribution mechanism for stable cooperation among DSE enterprises, thus providing a more scientific basis for the development of cooperative relationships.

Digital servitization is a collaborative process that involves multienterprise activities, and it is significantly affected by digital servitization level and digitalization level. Moreover, constructing the modified Shapley value benefit distribution mechanism according to the relevant capabilities of digital servitization can promote the stable development of DSEs and value cocreation among members.

The main contributions of this study are as follows: First, it summarizes the stability-influencing factors of DSEs on the basis of empirical and literature research on the demand for enterprise digital servitization capabilities and transformation difficulties, delves deeper into the capability composition and cooperative relationship of DSE members and combines the expert scoring method and the entropy value method to determine the weighting to design the benefit distribution mechanism. Second, it reflects system stability and development by studying the revenue distribution of DSE members, thereby expanding the ecosystem construction and business model transformation of digital servitization in the existing research.

This study aims to contribute to the darker side of consumer–brand interactions by examining the relationship between consumer-related antecedents, particularly consumer personality traits, in triggering brand-hate emotions. Additionally, the link between brand hate and brand forgiveness was also taken into account, as well as the moderating impact of personality attributes. The impact of brand forgiveness on consumer coping behavior was investigated, particularly for brand switching (flight) and negative word-of-mouth (NWOM) (fight) on Indian e-commerce shopping websites/apps.

Using a structured questionnaire survey and a nonprobability purposive sampling approach, data were obtained from 438 online shoppers who had experienced hate directed at a particular shopping website or app. The hypotheses were tested statistically using partial least squares (PLS) structural equation modeling with SmartPLS 4 software.

First, the findings demonstrate that agreeableness, extraversion and neuroticism significantly affected brand hate. Second, the results indicate that personality traits, particularly extraversion and conscientiousness from the Big-Five model, play a substantial role in moderating the relationship between brand hate and brand forgiveness. Third, the study also reveals the significance of brand forgiveness in mitigating the adverse consequences of NWOM and brand switching in the context of e-commerce platforms.

Practical steps such as complaint-management processes and prompt resolutions through an appropriate means of active interaction and understanding the consumer’s personality when their concerns are heard and handled can help brand managers earn customers’ forgiveness and reduce brand hate toward e-commerce websites/apps.

Based on the authors’ understanding, this study is the initial one to incorporate brand hate, brand forgiveness and coping strategies into the model in a service context with the interaction effect of consumer personality traits.

This research employs a behavioural approach to investigate the determinants of CEO disclosure tone inconsistency. By examining CEO characteristics and psychological attributes, the study aims to unravel the complexities underlying tone variations in Management Discussion and Analysis (MD&A) reports. Through this exploration, the research seeks to contribute to understanding ethical considerations in corporate communications and provide insights into the nuanced interplay between personal, job-related and psychological factors influencing CEO disclosure tone.

The study utilises a dataset comprising 1,411 MD&A reports from 143 companies listed on the Tehran Stock Exchange between 2012 and 2021. Multiple regression analyses with year- and industry-fixed effects are employed to examine the relationships between CEO gender, tenure, duality, ability and psychological attributes such as narcissism, myopia, overconfidence and tone inconsistency. Data analysis involves MAXQDA software for analysing MD&A reports and Rahavard Novin software for document analysis, supplemented by audited financial statements.

The findings reveal significant relationships between CEO characteristics, psychological attributes and tone inconsistency. Female CEOs exhibit reduced tone inconsistency, contrasting with previous research trends. CEO tenure correlates negatively with tone inconsistency, whereas CEO ability shows a positive correlation, indicating a nuanced relationship with performance. However, CEO duality does not exhibit a significant association. Psychological attributes such as narcissism and myopia are positively associated with tone inconsistency, while no substantial connection is found with managerial overconfidence.

This research contributes to the inaugural exploration of CEO disclosure tone inconsistency through a behavioural lens, advancing measurement precision in the field. By delving into CEO characteristics and psychological attributes, the study offers unique insights into the roots of tone inconsistency. Applying comprehensive lexicon and phraseology enriches the methodological approach, fostering dialogue among diverse stakeholders and adding distinct perspectives to the discourse on ethical issues in business. Through its meticulous examination of behavioural underpinnings, this study becomes a catalyst for reflection, dialogue and progress in corporate communications and ethical considerations.

This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.

This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.

The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.

This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.

本研究通过分析酒店评论文本和图像之间情感特征的不一致性来检测消费者的讽刺。

本文提出了一种基于多模态深度学习的讽刺检测模型, 使用从两个旅行平台收集的三个酒店品牌的评论, 该模型能够识别模态内部和模态之间的情感不一致性。利用图神经网络（GNN）探索文本-图像交互信息, 以检测讽刺情感中的关键线索。

研究结果显示, 多模态深度学习模型优于其他基线模型, 这有助于理解酒店服务评估, 并为酒店经理提供决策建议。

该研究可以在两方面帮助酒店业者：检测服务质量和制定策略。通过选择参考酒店品牌, 酒店业者可以更好地评估其服务质量水平（随之而来的是最佳资源分配）, 因此, 讽刺检测研究不仅有助于寻求提高服务质量的酒店经理。本研究介绍的多模态深度学习方法可以在其他行业复制, 帮助旅行平台优化其产品和服务。

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.