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This study aims at exploring how small and medium enterprises (SMEs) can implement a more open and co-creational business model by actively collaborating with startups.

Because of the novelty of the SME–startup collaboration phenomenon and to the depth of the investigation required to grasp the mechanisms and logic of an open and co-creational business model, a single-case study has been performed related to investigating a collaboration between an SME and a startup.

The authors provide detailed empirical evidence on how SMEs may structure a “systematic” approach to design and execute an open business model enabled by startup collaboration. Moreover, this study suggests that the business model innovation process represents a necessary forerunner of an open business model. Finally, the authors contend that research on open business models should entail a broader perspective beyond the innovation process, to include business model validation through testing approaches like the lean startup.

This study takes as the locus of investigation the original perspective of the external partner of a focal firm willing to innovate. This study offers a unique contribution because, to date, few studies adopted such view within a relevant and under-remarked empirical setting linking SMEs and innovative startups.

The study aimsto analyze the main elements associated with the evolution of Brazilian agtechs from the initial conception of the business model to becoming companies in the scale-up stage.

The exploratory research was conducted based on data collected through in-depth interviews. The answers were analyzed quantitatively using descending hierarchical classification (DHC) and correspondence factor analysis (CFA) and qualitatively using content analysis.

Five main elements were identified as responsible for the evolution of the companies up to their entering the scale-up phase: (1) governance, (2) decisions inherent to resource allocation, (3) monitoring of strategic, tactical and operational activities, (4) fostering human capital development and (5) business model validation. Each element presents a set of performance indicators that show the scalability of these companies.

The model developed can help companies that have not yet advanced from the conception of the business model to the scalability of different sectors, in addition to agribusiness.

Proposal of a model that presents the main elements that impact on scalability and respective indicators that contributed to the scalability process of Brazilian agtechs.

This study contributed to advancing the knowledge on the organizational life cycle (OLC) of agricultural startups, particularly regarding the factors responsible for their scalability.

While the disruptive potential of artificial intelligence (AI) has been receiving growing consensus with regards to its positive influence on entrepreneurship, there is a clear lack of systematization in academic literature pertaining to this correlation. The current research seeks to explore the impact of AI on entrepreneurship as an enabler for entrepreneurs, taking into account the crucial application of AI within all Industry 4.0 technological paradigms, such as smart factory, the Internet of things (IoT), augmented reality (AR) and blockchain.

A systematic literature review was used to analyze all relevant studies forging connections between AI and entrepreneurship. The cluster interpretation follows a structure that we called the “AI-enabled entrepreneurial process.”

This study proves that AI has profound implications when it comes to entrepreneurship and, in particular, positively impacts entrepreneurs in four ways: through opportunity, decision-making, performance, and education and research.

The framework's practical value is linked to its applications for researchers, entrepreneurs and aspiring entrepreneurs (as well as those acting entrepreneurially within established organizations) who want to unleash the power of AI in an entrepreneurial setting.

This research offers a model through which to interpret the impact of AI on entrepreneurship, systematizing disconnected studies on the topic and arranging contributions into paradigms of entrepreneurial and managerial literature.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

This study aims to introduce the original idea of competitive empathy, to go beyond competitive advantage and help managers and entrepreneurs strategize with a shared purpose.

This study builds on and originally combines seminal works on empathy in the fields of psychology and management, which are extended to embrace the notion of empathy toward competitors. Empirical research leveraged different methods, including “class as a lab” research; field studies; and collaborative research.

To support managers’ and entrepreneurs’ effort to be more empathic and emotionally intelligent when dealing with competitors, the study introduces the “Competitive Empathy Catalyst” tool, which identifies three layers – namely, orientation, execution and foundation – where to look for common ground between your company’s and your competitors’ strategy. A set of principles that should inspire managers’ strategic behavior and action to enable competitive empathy are also proposed: search for a non-conflicting identification with competitors and avoid “egotism”; adopt “perspective-taking”; practice “mirroring”; aim at the “greater good”; leverage “vicarious learning” and apply “cautionary trust.”

Looking at competitors from a different angle and applying competitive empathy as a strategic device can uncover a plethora of opportunities benefiting the company’s strategy and ability to create, deliver and capture value.

Empathy in management theory and practice has been traditionally associated with interaction with customers, employees and stakeholders. Competitive empathy counterintuitively applies empathy to a category of players that were largely left out from the discussion, that is, competitors.

The goal of this research is to investigate the relationship between two different sets of practices, lean startup and business planning, and their relation to entrepreneurial performance.

The authors collected data from 120 entrepreneurs across the US about a variety of new venture formation activities within the categories of lean startup or business planning. They use hierarchical regression to examine the relationship between these activities and new venture performance using both a subjective and objective measure of performance.

The results show that talking to customers, collecting preorders and pivoting based on customer feedback are lean startup activities correlated with performance; writing a business plan is the sole business planning activity correlated with performance.

This research lays the foundation for understanding the components of both lean startup and business planning. Moreover, these results demonstrate that the separation of lean startup and business planning represents a false dichotomy.

These findings suggest that entrepreneurs should engage in some lean startup activities and still write a business plan.

This article offers the first quantitative, empirical comparison of lean startup activities and business planning. Furthermore, it provides support for the relationship between specific lean startup activities and firm performance.

This study focuses on extra-curricular start-up programs for students at higher educational institutions. It explores the social and situated learning experiences of students who participate in start-up programs, as well as how the processes and outcomes of entrepreneurial learning are potentially shaped by this context.

The study follows multiple cohorts of students who have participated in an extra-curricular start-up program managed by three collaborating universities in Greater Copenhagen. The data have been inductively analyzed using semi-structured interviews with students and project managers during and after the start-up program, complemented with project progress reports, observation notes and survey data.

The analysis generates a grounded, theoretically informed process model of entrepreneurial learning situated in extra-curricular start-up programs. The model depicts how the immersion, comprehension and co-participation in entrepreneurship as social practice subsequently enables students to expand knowledge structures and develop greater self-confidence in performing entrepreneurship. The model identifies three interconnected components that trigger entrepreneurial learning among students, which allow them to acquire two set of competencies: venture creation competencies and enterprising competencies.

The findings offer unique insights into how the social and relational environment influence and shape the learning experience of students, hence filling the research void on entrepreneurial learning in the situated context of extra-curricular enterprise activities. The findings also elucidate how individual learning experiences of students are potentially shaped by the immersion, comprehension and co-participation in entrepreneurship as social practice.

As a relatively new computing paradigm, crowdsourcing has gained enormous attention in the recent decade. Its compliance with the Web 2.0 principles, also, puts forward unprecedented opportunities to empower the related services and mechanisms by leveraging humans’ intelligence and problem solving abilities. With respect to the pivotal role of search engines in the Web and information community, this paper aims to investigate the advantages and challenges of incorporating people – as intelligent agents – into search engines’ workflow.

To emphasize the role of the human in computational processes, some specific and related areas are studied. Then, through studying the current trends in the field of crowd-powered search engines and analyzing the actual needs and requirements, the perspectives and challenges are discussed.

As the research on this topic is still in its infancy, it is believed that this study can be considered as a roadmap for future works in the field. In this regard, current status and development trends are delineated through providing a general overview of the literature. Moreover, several recommendations for extending the applicability and efficiency of next generation of crowd-powered search engines are presented. In fact, becoming aware of different aspects and challenges of constructing search engines of this kind can shed light on the way of developing working systems with respect to essential considerations.

The present study was aimed to portrait the big picture of crowd-powered search engines and possible challenges and issues. As one of the early works that provided a comprehensive report on different aspects of the topic, it can be regarded as a reference point.

This paper aims at proposing knowledge translation as an element of business model design that can support entrepreneurs in achieving alignment and collaboration between entrepreneurial teams and external stakeholders.

The conceptual model presented in the paper is developed from the literature review and draws mainly on two streams of work as follows: first, the contributions related to the lean start-up methodology initially developed by Blank and Ries and second, the work of Osterwalder and Pigneur on business models and its subsequent developments. In addition, we draw on key insights from the entrepreneurship and organizational learning literature, such as discovery-driven planning and disciplined entrepreneurship.

The continuous validation framework (CVF) is introduced, posing the attention on underlining knowledge-translation mechanisms to decode complex concepts related to new venture creation.

The authors propose a new framework (the CVF) as an effective translational tool because it is a visual diagram that allows entrepreneurs to translate complex and technical ideas into a format that is more understandable for external audiences. Additionally, for each step of the CVF, specific translational mechanisms are defined and discussed, as each stage of the CVF presents specific translational challenges that result in outcomes that differ from stage to stage.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.