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This study aims to propose a longitudinal analysis of motivations for Integrated Reporting (IR) adoption, internal changes, the benefits of IR implementation and compliance challenges.

The authors analyse a longitudinal case study of an Italian-listed company (Eni) participating in the IR-Pilot Programme (PP) and covering 10 years of IR adoption. The analysis was based on a mixed-method approach that included semi-structured interviews, content analysis of annual reports and triangulation with other data sources. Results are discussed regarding institutional theory, legitimacy theory and diffusion of innovation theory.

The study suggests that motivations for adopting IR change over time and participation in the IR-PP helps Eni acquire a comprehensive and substantial integrated view of value creation over time, makes integrated culture a key factor for strategic business sustainability and confirms the readiness of early adopters to comply with the non-financial Directive (NFD).

This study, among the few longitudinal case studies, provides organisations, regulators and academics with insights into the motivations driving the successful adoption and implementation of IR and the NFD. The results may help companies consider one of the tools currently deemed to bring sustainability into action and participation in pilot groups.

The purpose of this paper is to investigate the role of performance measurement systems (PMS) within the context of digital servitization (DS), especially in collaborations involving multiple actors. The paper adopts a bottom-up ecosystem perspective to gain insights into how companies can effectively manage the complexities of digital transformation in the servitization domain.

This research draws upon a longitudinal case study within the wine and spirit supply chain. It focuses on a logistics company, “GR”, which has promoted a DS strategy to offer advanced services and enhance the competitiveness of the entire ecosystem.

The study offers valuable insights into the evolution of PMS roles throughout the DS journey, promoting cooperation, coordination, collaboration and control among ecosystem actors, thereby facilitating the development of a DS strategy.

The study is focused on a logistics company with unique capabilities and networks. Future research should include a broader range of contexts. Furthermore, our analysis focuses on the initial stages of ecosystem emergence, particularly the initiation and momentum phases. Further research should explore how DS impacts organizations in the following ecosystem phases.

This research offers valuable insights for managers, particularly in the development of DS strategy. It underscores the significance of PMS as key facilitators in crafting DS strategy and in the broader ecosystem evolution. The findings demonstrate that PMS is instrumental across different phases of the servitization process, improving aspects such as performance monitoring, resource allocation, collaboration and communication. Moreover, this study emphasizes the importance for small and medium-sized enterprises manufacturers and logistics firms to build and nurture collaborative relationships with various supply chain stakeholders to successfully implement a servitization strategy. In the wine industry, embracing a multiactor perspective is crucial. The delivery of advanced services necessitates a wide spectrum of knowledge and skills on one hand, and adaptability and flexibility in developing relationships on the other.

The study contributes to the literature on management accounting by exploring the role of PMS in DS. It reveals that PMS acts as a fundamental enabler, promoting seamless coordination and collaboration among various actors involved in DS. This sheds light on the transformative potential of PMS in creating a collaborative environment, where multiple organizations work together to offer value-added services.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

The study aims to critically evaluate the Kochi Water Metro as a model for sustainable urban transportation and extract valuable lessons for policymakers and cities seeking similar initiatives. The study employs a qualitative data-driven approach, utilising a longitudinal case study design, open-ended interview questions, direct observations and qualitative content analysis. The Kochi Water Metro has emerged as a transformative urban mobility initiative, enhancing connectivity while simultaneously reducing congestion on roads. The project's sustainable practices have minimised its carbon footprint, contributing to environmental sustainability. Economic benefits have accrued through increased tourism, employment opportunities and improved local livelihoods. The project's adaptability to local conditions and changing needs further underscores its sustainability. Limited data from the project's early years necessitate further longitudinal analysis. The focus on benefits may overlook potential challenges, warranting broader investigation. Reliance on qualitative data may restrict generalisability. The Kochi Water Metro serves as a beacon for sustainable and equitable transportation solutions. It serves as a blueprint for policymakers, providing practical lessons in project implementation, especially its integration with existing modes enhances overall urban mobility. Policymakers should consider replicating elements such as electric boats and integrated networks, while emphasising environmental sustainability, accessibility and community engagement. The comprehensive evaluation provides valuable insights into the project's tangible impacts and replicable elements. The innovative approach blends sustainable practices, operational resilience and community engagement, serving as a model for cities seeking sustainable, equitable and efficient transportation solutions.

This study aims to develop a modelling framework of housing supply dynamics within the context of urban microsimulation systems. Housing markets have witnessed substantial investigation over recent decades, predominantly concerning residential demand. However, comparatively limited attention has been directed towards comprehending the housing supply dynamics. Housing policy disconnects with the developers’ market behaviours, which leads to significant mismatch between the housing construction and affordable housing needs of the population. Research attention should be made in comprehending the residential construction market activities. To address this gap, this study developed an autoregressive distributed lag (ARDL) model and analyzed the temporal evolution of housing construction.

An ARDL model was developed to address the issue of temporal modelling of the housing supply. An empirical study was conducted in the Greater Toronto and Hamilton Area (GTHA) based on a longitudinal housing starts data set from 1998 to 2020. The model integrates diverse variables, including macroeconomic conditions, property development costs, dwelling prices and opportunity costs. Notably, the model captures both the path-dependent effects stemming from supply market fluctuations and the temporal lag effect of influential factors.

The findings reveal that the supply-side’s responsiveness to market condition alterations may span up to 18 months. The model has reasonable and satisfying performance in fitting the observed starts. The methodological foundations laid will facilitate future modelling of housing supply dynamics.

This study innovatively separated the modelling of housing supply within the context of urban microsimulation, into two parts, the modelling of housing starts and completion. The housing starts are determined in a complex and regressive process influenced by both the micro-economic environment and the construction cost and housing market trends. Through the temporal modelling method, this study captures how long it would take for the housing supply to respond to multiple factors and provides insight for urban planners in regulating the housing market and leveraging various policies to influence the housing supply.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing social commerce research frameworks (e.g. the information model).

In the era of Industrial Revolution 4.0 technologies and new social commerce (s-commerce) models, the authors believe that there is an immediate need for a new research framework. The authors analysed the progress of the s-commerce paradigm between 2003 and 2023 by applying longitudinal science mapping. The authors then developed a research framework based on the themes in the strategic diagrams and evolution map.

From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, virtual communities, social shopping and analytic approaches. From 2011 to 2015, it shifted to s-commerce, consumer behaviour, Web 2.0, artificial intelligence, social technologies, online shopping, user studies, data gathering methods, applications, service-based social commerce constructs, e-commerce and cognitive factors. Social commerce remained the primary research paradigm from 2017 to 2023.

The SC framework may be analogous to popular research frameworks such as technology-organisation-environment (T-O-E) and stimulus-organism-response (S-O-R). Based on this SC framework, researchers may gain a better understanding by determining the factors of the social, commercial, technological and behavioural dimensions.

The authors redefined s-commerce and developed an SC framework. Practical guidelines for the SC framework and an exemplary research model are presented. Overall, this study offers a new research agenda for the extant understanding of s-commerce, with the SC framework as the next frontier of the theoretical advancements and applications of s-commerce.

This study examines the interaction between sales control systems and firm level strategic orientations and their joint effects on company performance in B2B context. Independent and joint effects of market orientation (MO), innovation orientation (IO) and sales control systems (SCS) on firm performance were analyzed.

A quantitative research methodology is adopted to compile firm-level primary data from manufacturing companies located in an organized industrial zone. Research data were collected by face-to-face surveys from 302 sales professionals. The research model and hypotheses were tested by using partial least squares (PLS) structural equation modeling (SEM) with Smart PLS 3.0.

In addition to confirming the positive effects of MO and IO on performance, data analyses revealed that SCS exert an indirect effect on company performance which is fully mediated by MO.

The research was limited to a developing country context and research data was collected from a convenient sample of B2B companies by a cross-sectional study. Cross-cultural and longitudinal studies may provide additional insights. Firm level strategic orientations and sales control systems must be examined together in an integrated way to explore their effects on company performance. The individual effects of these structures on business performance may manifest differently when they come together.

Results indicate that the sales control system setup is critical for the implementation of a market-oriented strategy. This study highlights the importance of setting a compatible sales control system to achieve organizational goals in accordance with the strategic orientations which affect the success of particular organizational strategies.

Although the current literature identifies the independent and joint effects of market orientation and innovation orientation on company performance, empirical studies probing the interaction of sales control systems with these constructs is very scarce. Understanding how sales control systems relate to strategic orientations will help design a more effective sales organization and improve company performance. This study contributes to the literature by promoting additional insights by linking sales control systems with market orientation, innovation orientation and company performance.

The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.

The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.

The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.

The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.

The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.

The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.

This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.