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Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.

This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may challenge current scientific theories or lead research in new phenomena.

This paper describes a case study of theorizing by using a crowd-based method. The first section of the paper introduces what do the authors know about crowdsourcing, crowd science and the aggregation of non-expert views. The second section details the case study. The third section analyses the aggregation. Finally, the fourth section elaborates the conclusions, limitations and future research.

This document answers to what extent the crowd-based method produces similar results to theories tested and published by experts.

From a theoretical perspective, this study provides evidence to support the research agenda associated with crowd science. The main limitation of this study is that the crowded research models and the expert research models are compared in terms of the graph. Nevertheless, some academics may argue that theory building is about an academic heritage.

This paper exemplifies how to obtain an expert-level research model by aggregating the views of non-experts.

This study is particularly important for institutions with limited access to costly databases, labs and researchers.

Previous research suggested that a collective of individuals may help to conduct all the stages of a research endeavour. Nevertheless, a formal method for theorizing based on the aggregation of non-expert views does not exist. This paper provides the method and evidence of its practical implications.

This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).

Although management accounting tools and techniques are developed to solve practical problems in organizations, there is a lot of criticism of management accounting research for not having an impact on practice. In interventionist research, the “shaping” of an intervention to solve a practical problem is an important step. The purpose of this paper is to explore how the findings of management accounting research can be reviewed to make them practically applicable in shaping an intervention.

The paper is based on the author’s experiences with an interventionist research project.

Systematic literature reviews, which are common in engineering and medicine, bring together the academic knowledge that can contribute to solutions for a specific practical problem, including a definition of the ways in which this knowledge can be applied. Inspired by the methodology for conducting such reviews, this paper proposes how interventionist management accounting researchers can use existing theoretical knowledge in shaping interventions that aim to solve a practical problem. After an intervention, the analysis of the intervention’s unforeseen effects can provide a basis for the refinement of the theory identified in the literature review.

Such a literature review can be organized according to four approaches to taking theoretical knowledge into practice. Unforeseen effects of the intervention can guide the selection of additional theory that helps to interpret these effects and refine normative and academic theory.

In management accounting it is uncommon to review the literature with the aim of shaping a solution for a practical problem. This paper explores how literature reviews that focus on a specific practical problem can contribute to bridging the gap between theory and practice.

Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO₂ and PM_2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Results suggest that the carbon trading mechanism induces 2.7% of CO₂ emission reduction and 6.7% of PM_2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

Drawing from tripartite theory of attitude, this study examined whether interaction effect of psychological ownership (cognitive component) changes the nature of the relationship between job satisfaction (affect component) and job performance (behavioral component) toward a higher or weaker relationship. Furthermore, the study draws from psychological ownership theory to find support whether job satisfaction is nurtured by the feeling of psychological ownership.

Longitudinal data from 211 academic and non-academic employees was randomly collected and partial least square-structural equation model (PLS-SEM) was used for data analysis through SmartPLS version 3.3.2.

The study found a positive interaction effect of psychological ownership on the relationship between job satisfaction and job performance. Furthermore, the study found that feeling of psychological ownership nurtures employees' satisfaction with their job.

The findings of the study explicate to human resource managers and practitioners the mechanism through which job satisfaction affects job performance and how feelings of psychological ownership nurtures employees' satisfaction with their job.

The study provides new insight into the relationship between job satisfaction and job performance by drawing on the tripartite theory of attitude perspective, and concluded that job performance as overall employee attitude toward the organization is predicted by the interaction and interplay of job satisfaction, psychological ownership and job performance as components of attitude. To the authors’ best knowledge, none of the previous literatures on job satisfaction–job performance relationship draws its conclusions from the perspective of tripartite theory of attitude. Furthermore, the study found empirical evidences that psychological ownership nurtures employees' job satisfaction.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.