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The purpose of this study is to identify important strategies and practices supporting inter-organizational learning (IOL) in integrated care. The two research questions ask how organizational network architectures can help involved organizations overcome the barriers of IOL in integrated care (RQ1) and what design recommendations can strengthen the processes of IOL in integrated care (RQ2).

This study applies a qualitative design to analyze an improvement initiative in a regional, integrated care service for elderly patients with multiple illnesses in Norway. An inductive thematic analysis for the triangulating of qualitative data from different sources was applied. Patterns within the data were organized into themes, categories and subcategories. No software was applied.

The identified characteristics of the organizational network architectures supporting IOL in integrated care in the case under study were: equality of the involved parties, shared goals, recognition of expertise and the abilities to coordinate, design IOL processes and make joint decisions (RQ1). The categories of practices supporting the process of IOL were: insight into complex realities, contradictions, iteration, motivation and prototypes (RQ2).

This study offers much-needed insight into a successful approach for IOL in integrated care. The results offer strategies to be considered when building organizational networks for the improvement of integrated care and relevant practices useful when designing IOL processes in such care services. We believe such knowledge has important implications for policymakers, frontline personnel, education, research and leaders.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

This paper aimed to study business model innovation by a work-integration social enterprise (WISE). Specifically, the study investigated how the organization developed novel value propositions and created and delivered value for multiple stakeholders.

An in-depth qualitative study was conducted at Foreword, a for-profit organization that uses persons with disabilities, mental health conditions and special needs. Data was drawn from semi-structured interviews with stakeholders of the organization and several secondary information sources.

The authors’ inductive analysis revealed the existence of an innovative and powerful business model that is integrated by the organization’s overarching social mission and anchors its ability to deal with multiple conflicting logics such as economic, social, ecological sustainability and community development, to co-create value with and for multiple stakeholders.

The study underscores the need for business model innovation through enhancing value creation for multiple stakeholders for for-profit WISEs. Since the analysis and resulting model in the study are based on a single organization in a geographically small, affluent country with a hands-on government, they may need to be modified before applying in other contexts.

The study identifies several pointers for other social enterprises – specifically the need for managers to build business models appropriate for their organizational and environmental contexts.

The study’s originality stems from the adoption of a stakeholder lens to examine business model innovation. It also proposes an integrative conceptual model of the antecedents and outcomes of business model innovation.

The main purpose of this research study is to investigate and examine the factors that might influence the intention to adopt and use mobile payment and their relationships during the COVID-19 pandemic.

This research study used both mobile payment adoption literature, The Technology Adoption Model and Unified Theory of Acceptance and Use of Technology, to propose a conceptual framework for mobile payment adoption. Quantitative method is used in which 306 participants responded to an online survey to validate the proposed conceptual framework.

The introduced integrated model embraced perceived risk, transaction transparency, mobile payment usefulness, social influence, performance expectation as independent variables and usage continuation intention to adopt mobile payment as the dependent variable. The results from data analysis have statistically revealed significant relationships and a positive impact of perceived risk, mobile payment usefulness, social influence and performance expectation. Also, the results identified a negative impact for the transaction transparency factor. As this research study is conducted at a later stage of the COVID-19 pandemic, it adds value to the existing literature by providing insights to business managers on the factors influencing mobile payment usage and other implications related to increasing the market potential for businesses in the new normality of the coronavirus pandemic.

This paper offers a combination conceptual framework of mobile payment adoption based on a literature review on mobile payment adoption from information systems perspective. It adapts integrated model to establish a more comprehensive innovation adoption framework for mobile payment.

With the advancement of technology and more attention to environmental issues, building information modeling (BIM) and green building have become two new and growing trends in the construction industry. Therefore, this study proposes a new strategy that integrates BIM and green building rating assessments with an emphasis on Iran Green Building Rating System (IGBRS).

By creating a Revit-IGBRS project template that includes sheets related to all credits, the project compliance with the IGBRS credits and management of submittal documents for certification has been facilitated. Finally, a case study of the materials and resources category of the IGBRS system was performed to validate the BIM-IGBRS application model. All 8 criteria of this category were examined by using Dynamo programming for the Revit sample project.

A practical model for BIM and IGBRS integration is presented, which allows designers to be aware of the IGBRS scores obtained before the project’s construction phase and examine different scenarios for the highest scores. Overall, this study showed that integrating BIM and the Iranian rating system is possible with some constraints, and adding some features to BIM software can promote this integration.

Given that no study has been conducted on the integration of BIM with the Iran Green Building Rating System (IGBRS), the present research investigates utilizing building information modeling to meet the credits requirements of this rating system. The results of this research can be generalized and used in other green rating systems.

This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

Recognizing the importance of Robo-advisors in digital financial services, this paper aims to analyse the users’ perception and acceptability of artificial intelligence (AI) in digital investment solutions using an extended “Technology Acceptance Model” (TAM).

The model is tested using 454 online valid responses received from Indian Fintech users via direct path analysis, mediation and moderation.

The study’s findings show that trust, perceived usefulness and perceived risk all significantly impact users’ attitudes towards Robo-advisors. In contrast, ease of use and social influence did not impact users’ attitudes statistically. Furthermore, the results indicate that their attitudes and ease of use influence users’ intentions to adopt Robo-advisors. Moreover, the moderation effect of gender partly supports the overall model. Specifically, in the path between attitudes and their antecedents, gender plays a role in influencing the relationships among these variables. This aligns with preliminary research in the field, providing additional insight into how gender may moderate the factors influencing users’ attitudes and intentions regarding Robo-advisory services.

This research study also reveals that trust, perceived risk, ease of use and demographic factors influence the adoption of Robo-advisory services. It is functional, but its sample selection is not probabilistic and overly emphasizes gender. Future research should use probabilistic sampling, other demographic factors and experience and situational factors. Also, it is necessary to examine how convenient and satisfying it is to communicate with service providers. Filling these gaps will improve the knowledge of consumer behaviour in the context of Fintech adoption and develop the current research.

This study posits that perceived usefulness, trust, perceived risk and ease of use remain core determinants of adopting Robo-advisory services. So, to improve the level of trust of users, it is necessary to develop security measures, data clarity and quality and customer support. Enhancing ease of use by incorporating better interface gestures is always beneficial for increasing the number of users and their level of satisfaction. As identified in previous studies, practical solutions will be achieved by pursuing the increased use of technology while leveraging AI for personal services and minimizing perceived risks, which will strengthen more advanced security measures as well as sufficiently clear communication.

The paper aims to extend the TAM by incorporating measures of trust and social influence to identify the factors that drive the adoption of Robo-advisors. In doing so, the paper may contribute to developing a more comprehensive understanding of the factors that shape consumers’ attitudes and intentions towards these technologies. Moreover, the paper appears to examine the moderating effect of gender on attitude and its predictors, which could provide insights into how gender characteristics may impact the adoption of Robo-advisors.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.