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In a typical problem of allocating a fixed resource across a set of decision-making units (DMUs), the decision maker will decide how much resource to allocate to each DMU. Real-world decision-making contexts frequently pose challenges in precisely assessing each DMU’s performance on distinct parameters and in ascertaining the relative importance of these parameters, since resource allocation often precedes the decision-making process. Conventional decision-making approaches fall short in effectively addressing such complexities. In this paper, the authors propose a stochastic multi-attribute acceptability analysis (SMAA)-based model that can deal with the resource allocation problem with uncertain attribute values and unknown attribute weights.

The SMAA-based model first regards the resource to be allocated as an evaluation attribute and then proposes a fair resource allocation solution that minimizes the gap between the holistic acceptability of each DMU. To address the optimization of the SMAA-based model, two heuristic strategies were devised: a modified particle swarm optimization (PSO) approach and an adaptive algorithm, both designed to yield acceptable outcomes within a constrained time frame.

The resource allocation model based on SMAA can provide an effective analytical framework for the resource allocation problem of different DMUs in uncertain environments. Meanwhile, the PSO algorithm and adjustment algorithm proposed in this paper can help decision-makers quickly find a fair resource allocation plan.

Primarily, this study introduces a novel approach rooted in SMAA for tackling resource allocation dilemmas where attribute values are uncertain and weight information is absent. Secondly, a fairness-centric strategy that minimizes the disparity in overall acceptability among various DMUs is presented. Thirdly, two heuristic algorithms, namely, PSO algorithm and adjustment algorithm, are developed to solve the problem.

This study investigates the impact and role of digital twin technology in building automation (DTBA) from a sustainability viewpoint. It aims to enhance the understanding of how DTBA can boost efficiency, optimize quality and support sustainable practices in contemporary construction. By exploring the integration of DTBA with sustainable practices, the study seeks to demonstrate how DT can revolutionize building management and operations, leading to significant improvements in resource efficiency, environmental impact and overall operational excellence.

This research employs a bibliographic analysis and systematic review of 176 publications from the past five years (January 1, 2019 to December 31, 2023), focusing on the application and development of DTBA. The study methodically analyzes current trends, identifies research gaps and suggests future directions by synthesizing data from various studies, offering a comprehensive overview of the current state of DTBA research. The approach combines quantitative and qualitative analyses to provide robust insights into the advancements and challenges in the field.

The review identifies key development areas in DTBA, such as energy and environmental management, resource utilization within a circular economy and technology integration and interoperability. It highlights the necessity for further research to maximize DTBA’s potential in sustainable building automation. The findings suggest that while significant progress has been made, there is a critical need for innovations in data interoperability, predictive analytics and the integration of renewable energy sources to fully realize the benefits of DTBA in enhancing building sustainability.

This paper provides a thorough review of DTBA from a sustainability perspective, offering valuable insights into its current applications and future development potential. It serves as a crucial resource for researchers and practitioners looking to advance sustainable practices in the construction sector using DT technology. By bridging the gap between theoretical research and practical applications, the paper underscores the transformative potential of DTBA in driving sustainable development and provides a roadmap for future research and innovation in the field.

The purpose of this systematic literature review (SLR) is to examine the theoretical landscape of knowledge hiding (KH) research, identifies prevailing theories, the different ways KH is understood within these theories and the underlying assumptions that shape these views. Based on this, ideas for further research are derived to advance the theoretical basis of KH studies.

Using a theory-based SLR, the authors analysed 170 scientific papers from Scopus and Web of Science. This involved thematic analysis to categorise theories frequently applied in KH research and a detailed examination to link core assumptions to these theoretical perspectives.

The analysis revealed a reliance on 86 distinct theories, with a notable emphasis on social exchange theory and conservation of resources theory. KH is predominantly conceptualised as a negative, objective, reactive and relational behaviour rooted in social reciprocity and resource conservation. The review uncovers the multifaceted nature of KH, challenging the field to incorporate broader theoretical views that encompass positive aspects, subjective experiences, strategic intentions and non-relational determinants of KH.

To the best of the authors’ knowledge, this is the first study to systematically map and analyse the theoretical underpinnings of KH research. It offers a unique contribution by categorising the diverse theories applied in KH studies and explicitly linking these theories to their inherent assumptions about KH. This approach provides a comprehensive overview that not only identifies gaps in the current research landscape but also proposes alternative theoretical perspectives for exploring KH, thereby setting a new direction for future studies in this field.

This paper investigates the impact of strategic alliance network centrality and structural holes on firm innovation efficiency. In addition, the paper aims to explore the moderating effects of government R&D subsidies and firm technology standardization.

Based upon the literature on strategic alliance networks, this paper proposes a conceptual model with several hypotheses. The empirical analysis is based on a sample of 736 observations from 92 mechanical manufacturing firms in China from 2010 to 2017. We measured firm innovation efficiency via the DEA model and performed quantitative analysis with GMM estimation.

The results indicate that strategic alliance network centrality is positively related to firm innovation efficiency, and structural holes have a U-shaped relationship with firm innovation efficiency. Government R&D subsidies positively moderate the relationship between centrality and firm innovation efficiency. Firm technology standardization positively moderates the relationship between centrality and firm innovation efficiency and the U-shaped relationship between structural holes and firm innovation efficiency.

Firms should focus on improving innovation efficiency and maximizing innovation output under limited resources. Furthermore, managers ought to strengthen cooperation between firms and external alliances while promoting the utilization of strategic alliance network position resources to benefit innovation efficiency.

This paper considers that innovation efficiency, including input and output processes, is more representative than innovation performance, and few studies have focused on the relationship between strategic alliance networks and innovation efficiency. To fill this research gap, this paper explores the impact of strategic alliance network position embedding on innovation efficiency.

As organisations rely more on systems to protect their accounting data, it is critical to investigate whether cutting-edge “Accounting Information Systems (AIS)” technologies increase “Accounting Information Quality (AIQ)” in post-Covid-19 era.

The current study examined how Covid-19 modified the expected links between AIS implementation and AIQ factors. To make the “structural model”, a “web-based survey” was used. A “Multi-group Analysis” (MGA) with “AMOS.26” was employed to test the regulating effect. To look at their regression values, the 412 participants from “Family Small- and Medium-sized Enterprises FSMEs” in Jordan who took part in the MGA were split into two groups: those who had a high experience of Covid-19 impact (N = 143) and those who had a low experience of Covid-19 impact (N = 269). The study of 412 completed surveys shows that MGA uses a modified t-test to determine the significant difference between regression coefficients.

The results of the moderation effect confirm that Covid-19 has a substantial effect on AIS adoption and AIQ. All hypotheses about how AIS implementation affects AIQ in post-Covid-19 era were approved. The study finds that adopting AIS is critical for enterprises to maintain successful operations and efficiency.

This model may assist businesses in comprehending the relationship between AIS and AIQ, how it affects it in the current technological adoption phase under extremely unpredictable economic conditions and what characteristics support its widespread usage. This research offered the theoretical underpinning for examining AIS’s ability to improve AIQ among Jordanian family firms. The results can also be applied to other Middle Eastern countries and AIQ, how it affects this relationship during this period of technological adoption under extraordinarily unpredictable economic conditions and what factors support its widespread use. Practitioners and scholars must recognise how AIS can develop organisational values and impacts as the AIS environment grows. This study recommends a holistic AIS-implementation methodology and evaluates organisational implications to address this empirical challenge.

This study investigates whether adopting AIS results in significantly higher AIQ in the post-Covid-19 era. To the best of the author’s knowledge, there have been a few attempts to investigate how AIS influences data quality. Still, there is little evidence of how this relationship has evolved in the aftermath of the Covid-19 tragedy. As a result, much work remains to be done, particularly in analytical decision-making culture, which is critical to the development of the growing economies of the Middle East and Jordan. To address this knowledge gap, the Covid-19 and AIQ criteria were incorporated into the model utilised in this study. This research adds to existing knowledge and may be valuable in areas with low AIS framework frequency.

Robotic restaurants are very novel, and service robots in these restaurants are identified as offering unique advantages in terms of efficiency, tireless service and potentially lower operational costs. However, studying customer engagement with the robots can reveal aspects of robotic service that resonate with diners. Understanding how diners interact with robots can help create a more engaging and enjoyable atmosphere, bringing more business to restaurants. Building on the stimulus-organism-response (SOR) theory and place attachment theory, the purpose of this paper is to study the impact of the robotic service quality (RSQ) on the customer attachment to the robotic restaurant with the mediating role of the different dimensions of the customer engagement, like the Absorptive Attention, Enthusiastic Participation and Social Connection. Subsequently, the impact of the customer attachment to the robotic restaurant on different dimensions of customer engagement behaviors like augmenting, co-developing, influencing and mobilizing behaviors was also studied.

The cross-sectional data from 786 robotic restaurant diners in India who answered the self-administered structured questionnaires is utilized for this descriptive study. The study employed a purposive sampling strategy. The SMART-PLS 4.0 program was used to run structural equation modeling and analyze the data.

The results indicate that customer engagement dimensions like Absorptive Attention, Enthusiastic Participation and Social Connection differentially mediate the relationship between RSQ and customer attachment with the robotic restaurant. Customer attachment to the restaurant and the robotic services subsequently positively impact customer engagement behaviors.

The study relied upon cross-sectional data from the Indian population above 18 years to test the proposed model. Further studies could test the model across different populations to generalize the study results.

This study addresses the need to investigate robotic restaurant diners’ engagement behaviors. By testing place attachment theory and the SOR framework, this study is the first to show that RSQ will impact the customer attachment with the robotic restaurant and that different dimensions of customer engagement mediate the relationship. It also validates the previous research findings that customer engagement is not a single global construct, and different sub-dimensions are to be explored. This study is also the first to show customer attachment to the robotic restaurant will impact customer engagement behaviors differently.

This study aims to examine whether rising air pollution impacts cryptocurrency returns across different categories.

This study uses panel regression to investigate the impact of air pollution on cryptocurrencies between January 2014 and June 2023. Cryptocurrency prices are sourced from www.coinmarketcap.com. Air quality is measured using the air quality index (AQI) values provided by the World Air Quality Index Project. Generalized method of moments (GMM) estimators for dynamic panel regression have also been used to control for endogeneity concerns.

High AQI levels are observed to negatively affect cryptocurrency returns. This impact remains absent during good air quality and for cryptocurrencies with lower energy consumption like stablecoins, clean energy and health cryptocurrencies, supporting the argument that rising air pollution leads to lower returns for cryptocurrencies more prone to damaging the environment.

The findings of this study could offer investors valuable insights in formulating more efficient cryptocurrency trading strategies. It also demonstrates how environmental variables influence the performance of volatile assets like cryptocurrencies. The presence of lower returns for currencies perceived as damaging to the environment could put the focus on promoting sustainability in the production of such digital currencies.

No prior study has investigated the influence of AQI on cryptocurrency returns. This study aims to focus on the behavioral aspect of financial decision-making. As cryptocurrency adoption rates rise across the globe, the findings of this study can provide useful insights to cryptocurrency traders.

The paper aims to contribute to the advancement of the debate on digital innovation and entrepreneurship from a cross-generational perspective in the context of family businesses. Specifically, the paper explores the contribution provided by the young generations of entrepreneurs to the digital transformation and resilience of family businesses during the pandemic emergence of COVID-19. Focusing on the need for a major understanding of digital resilience in the context of family businesses and small and medium-sized enterprises, the paper aims to provide theoretical and empirical contributions in replying to the following research question: How did young entrepreneurs contribute to the resilience and the digital transformation of their family businesses during COVID-19?

The methodology consisted of a two-stage qualitative investigation including a focus group managed in presence with the involvement of 24 young entrepreneurs and a self-administered online survey involving the family businesses belonging to the Association of Young Entrepreneurs of Confindustria Puglia (South Italy) that has allowed to collect 47 replies, with a 32% response rate.

The paper presents evidence about the resilience of family businesses during the COVID-19 supported by the disruptive role of emerging digital technologies. Our analysis demonstrates that young entrepreneurs adopted different patterns of digital transformations, depending on the firm’s industry and the family firm’s digital maturity. Digital resilience in the context of the observed family businesses produced different benefits in terms of competitiveness, effectiveness of decision-making, visibility and communication and new opportunities for value creation; it occurred in several business areas, including production, logistics, sales and human resources management.

The study highlights the relevant role that younger generations can play in exploring innovation opportunities associated with digitalisation as well as in contributing to reinforce innovation and resilience capability of their family businesses through collaboration with external stakeholders and ecosystems.

The value of the research consists in the attempt to analyse the meaning and implication of digital innovation in the context of family business as a driver for their resilience capability. The Family Businesses Digital Readiness and Young Entrepreneurs Contributions Matrix presented into the study as an original contribution of synthesis of the evidence collected.

Recent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.

We introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.

Experimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.

This study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).

This study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.

Amidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.

We find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.

In the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.