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The paper argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”. Big data platforms are shaping the processes of production, labour, the price of products and market conditions. “Digital platforms” and “big data” have become an integral part of the processes of production, distribution and exchange relations. These twin pillars are central to the capitalist accumulation processes. The article argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”.

As a conceptual paper, this paper follows critical methodological lineages and traditions based on non-linear historical narratives around the conceptualisation, construction and transition of the “Marxist theory of capital accumulation” in the age of platform economy. This paper follows a discourse analysis (Fairclough, 2003) to locate the way in which an artificial intelligence (AI)-led platform economy helps identify and conceptualise new forms of capitalist accumulation. It engages with Jørgensen and Phillips' (2002) contextual and empirical discursive traditions to undertake a qualitative comparative analysis by exploring a broad range of complex factors with case studies and examples from leading firms within the platform economy. Finally, it adopts two steps of “Theory Synthesis and Theory Adaptation” as outlined by Jaakkola (2020) to synthesise, adopt and expand the Marxist theory of capital accumulation under platform capitalism.

This article identifies new trends and forms of data driven capitalist accumulation processes within the platform capitalism. The findings suggest that an AI led platform economy creates new forms of capitalist accumulation. The article helps to develop theoretical understanding and conceptual frameworks to understand and explain these new forms of capital accumulation.

This study builds upon the limited theorisation on the AI and new capitalist accumulation processes. This article identifies new trends and forms of data driven capitalist accumulation processes within platform capitalism. The article helps to understand digital and platform capitalisms in the lens of digital labour and expands the theory of capitalist accumulation and its new forms in the age of datafication. While critiquing the Marxist theory of capitalist accumulation, the article offers alternative approaches for the future.

The non-fungible token (NFT) market has been multiplying in recent years. NFTs are tokens stored on a blockchain network based on smart contract technology that can be used to represent ownership of digital assets and cannot be changed like-for-like. With NFTs, all recorded digital properties can be freely traded and stored with values, making them possible to increase content transactions' privacy and security. In addition, NFTs engender new ways to organize, consume, share and store digital content. Despite the rapid growth of the NFT market, related consumer behaviors have yet to be well-known and relevant academic research results are very scarce. This study aims to explain how NFT fits with blockchain and cryptocurrency and how consumers accept it. This paper also develops a structured causal model with multiple paths to explain the antecedents and attitude variables for NFT acceptance.

The data collection was conducted from 542 young consumers in Korea via an online survey. The structural equation modeling method was used to analyze the hypotheses.

Attitudes toward technology and assets positively affect NFT purchase behavioral intentions. Additionally, symbolic driver affects behavioral intention directly.

The results expanded the understanding of the NFT market and consumers, which are still in their early stages. They also provide valuable insights for establishing future market strategies for NFT.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

This paper aims to (1) identify the different performance drivers (lead indicators) and outcome measures (lag indicators) investigated in the literature concerning the four balanced scorecard (BSC) perspectives in operations management (OM) contexts and (2) understand how performance drivers and outcome measures (and substantiated perspectives) are related.

We undertake a systematic literature review of the BSC literature in OM journals. From the final sample of 40 articles, performance drivers and outcome measures have been identified, and the relationships amongst them have been synthesised according to the system dynamics approach.

Findings show (1) the most relevant performance drivers and outcome measures within each BSC perspective, (2) their relationships, (3) how the perspectives are linked through the performance drivers and outcome measures and (4) how the different measures relate systemically. Accordingly, four causal loops amongst identified measures have been built, which – jointly considered – allowed for the creation of a dynamic strategy map for OM.

This study is the first one that provides a comprehensive and holistic view of how the different performance drivers and outcome measures within and between the four BSC perspectives in OM relate systemically, increasing the knowledge and understanding of scholars and practitioners.

This study aims to illustrate how collaborative platforms may leverage active community for climate change adaptation to implement biodiversity preservation policies.

This study adopts the Dynamic Performance Governance methodological framework to analyze the causal relationships affecting biodiversity preservation policy outcomes.

Active community reduces harmful factors for biodiversity (i.e. biological threats and anthropogenic pressure), limiting the risk of extinction of perennial plants. Stakeholders’ prior knowledge is an enabling condition of climate adaptation processes as it triggers the adoption of prescriptions and cultural changes in a community.

The study provides methodological guidance to define measures to deliver material information to support environmental performance governance. It elaborates an inventory of short- and long-term performance indicators integrating natural-science targets into accounting measures that can support policymakers operating in other contexts to implement climate change adaptation policies.

As a response to the study findings, social implications provide insights into how active community in collaborative platforms for climate change may support stakeholders to address natural resources imbalances, define strategies to share the burden among them and intervene on multiple policy domains (e.g. financial, environmental and social).

Climate change adaptation challenges are conceptualized as “super wicked problems,” and the collaborative platforms designed to address them are rendered as complex adaptive systems. This makes the paper go beyond traditional environmental governance, demonstrating that stakeholders’ interactions within collaborative platforms harness active community specialized knowledge.

Social enterprises (SEs), part of the third sector, are hybrid organizations combining the pursuit of social scopes with commercial business solutions. In seeking for social value, they pair for-profit and non-profit features, thereby compensating for shortcomings of both the public sector and the commercial market. Therefore, the performance management of such organizations assumes a crucial relevance. Among the available tools, the balanced scorecard (BSC) aims to capture performance multidimensionality, at the same time fostering legitimacy towards stakeholders.

In general terms, the BSC has the limit to follow a linear and static logic of construction and functioning. For this reason, scholars combine it with system dynamics (SD) to create dynamic balanced scorecards (DBSCs). However, literature seems to devote scarce attention to the adoption of such analytic tools in the third sector, particularly in SEs. This chapter wants to contribute to bridging this gap by proposing a tailored application in the context of a social cooperative, active in the clothing recycle and in the re-integration of disadvantaged social categories. By referring to previous literature about DBSC, two modelling strategies are identified: the BSC-driven and the SD-driven. The latter, based on inductive reasoning, is the one privileged for the study because of its wider flexibility. The modelling outputs consider different perspectives than the ones within traditional BSCs, contain elements of circular causality and show how financial and non-financial performances interplay and co-determine each other. Insights from the proposed model can be useful to support both decision-making and stakeholder engagement.

(Digital) servitization, referring to service-driven strategies and their increasing implementation in manufacturing, is one of the most rapidly growing areas in industrial service research. However, the cultural change involved in successful servitization is a phenomenon that is widely observed but poorly understood. This research aims to clarify the processes of social construction as manufacturers change their organizational culture to transform into industrial service providers.

This research takes a systematic approach to integrate disparate literature on servitization into a cohesive framework for cultural change, which is purposefully augmented by rationale culled from organizational learning and sensemaking literature.

The organizational learning framework for cultural change in servitization introduces a dynamic perspective on servitizing organizations by explaining social processes between organizational and member-level cultural properties. It identifies three major cultural orientations toward service, digital and learning that govern successful servitization.

This research contributes to the servitization literature by presenting a new approach to reframe and explore cultural change processes across multiple levels, thus providing a concrete starting point for further research in this area.

This chapter explores a pathway towards achieving a sustainable and inclusive future through the implementation of systems innovation, systems leadership and systems change. The author highlights the importance of understanding complex systems and identifies several models that can be employed to drive systems change, including the iceberg model and multi-level perspective. The author stresses the significance of systems leadership and innovation in creating a sustainable and inclusive future. This means that leaders and managers need to shift their mindset from reductionism to systems thinking. Reductionism views complex systems as a collection of separate parts that can be studied independently. In contrast, systems thinking acknowledges the interconnectedness of all parts and how they influence each other. When they embrace systems thinking, leaders and managers can make systems innovation and drive systems change to achieve sustainable and inclusive growth. Achieving a sustainable and inclusive future requires a collective effort from individuals, organizations and governments. It demands a comprehensive understanding of the interdependencies and interactions within complex systems, as well as a willingness to adopt new ways of thinking and leading. This chapter presents a compelling case for adopting systems innovation, systems leadership and systems change as critical components in building a sustainable and inclusive future.

The aim of this paper is to theoretically explore the concept of leadership in a Total Quality Management (TQM) context by developing a new theoretical framework of understanding Total Quality Leadership (TQL) as well as by opening the dialogue in researching further key elements of TQL.

The approach that the paper adopts is conceptual. Based on exploration of the wider management and leadership literature of empirical and theoretical studies, this paper develops a framework of TQL.

The suggested TQL framework is composed by three main pillars, namely the proactive, adaptive and the relational. The former consists of elements like anticipation of current business environment complexities and filtering of information that enhances practice decision making. The second pillar refers to adaptation, autonomy and feedback while the last one emphasises on the importance of aspects like social interactions, engagement and empathy. The paper explains why the specific pillars with the additional elements are critical for TQM success.

Given the tremendous challenges that organisations face due to increased complexity and demanding competition of the business environments globally, the role of leadership as the major “soft” aspect of TQM approach, seems to be vital more than ever. But the type of TQL appropriate to enhance total quality success nowadays, is still (and should be) under continuous exploration. This conceptual study attempts to provide new theoretical insights of TQL as well as to open the dialogue around the main elements consisting of TQL and how the future research agenda is formulated.