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Companies must no longer just report on corporate sustainability (CS) performance but also demonstrate that they are aligning their strategies with sustainability. However, suitable management control systems (MCS) are required to implement a sustainability strategy. Thereby, sustainability reporting (SR) can also be employed for control purposes. On the other hand, existing MCS can be used to develop SR that goes beyond accountability. Accordingly, this paper explores how this interplay can be designed.

For the study, 20 semi-structured interviews were conducted with persons from ATX and DAX companies. Since the interplay should be examined from a holistic control perspective, the authors used the MCS package of Malmi and Brown as an analysis framework.

Nowadays, merely focusing on reporting is too narrow a view. It is therefore not surprising that the investigation was able to reveal various possible linkages between MCS and SR that span the full range of the MCS package of Malmi and Brown.

Future research should also consider non-listed companies to investigate potential differences and take a closer look at the proposed reciprocal nature of the interplay.

The findings expand the knowledge of how companies can use SR for control purposes and how existing MCS can help develop a reporting that goes beyond accountability.

The study contributes by highlighting the potential of SR to control CS performance from a holistic MCS perspective and likewise the impact of existing MCS on reporting. In addition, different theoretical perspectives are used to explain why the interplay can be designed differently in practice.

Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

This paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.

Our paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.

AI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.

This paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.

Our study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.

The purpose of this paper is to investigate the most recurrent variables characterizing the collaborative relationships of industrial symbiosis (IS) (hereinafter also referred to as “anatomic” variables) established in the attempt to adopt circular economy (CE) by collecting evidence from a rich empirical set of implementation cases in Italy.

The current literature on IS was reviewed, and a content analysis was performed to identify and define the “anatomic” variables affecting its adoption in the circular economy. We followed a multiple-case study methodology investigating 50 cases of IS in Italy and performed a content analysis of the “anatomic” variables characterizing each case.

This research proposes the “anatomic” variables (i.e. industrial sectors involved, public actors involvement, governmental support, facilitator involvement and geographical proximity) explaining the cases of IS in the circular economy. Each “anatomic” variable is discussed at length based on the empirical evidence collected, with a particular reference to the impact on the different development strategies (i.e. “bottom-up” and “top-down”) in the cases observed.

Current literature on IS focuses on a sub-set of variables characterizing collaboration in IS. This research builds on extant literature to define a new framework of five purposeful “anatomic” variables defining IS in the circular economy. Moreover, we also collect and discuss a broad variety of empirical evidence in what is a still under-investigated context (i.e. Italy).

Following the lead of neighboring fields such as strategy and organization studies, entrepreneurship is gradually joining in the adoption of a practice perspective. Entrepreneurship as practice (EaP) is thus a nascent domain of investigation where the methodological debate is still unsettled and very fluid. In this paper, the authors contribute to this debate with a focus on family entrepreneurship.

The authors develop a conceptual paper to discuss what it entails to look at family entrepreneurship through a practice lens and why it is fruitful. Moreover, the authors propose a research strategy novel to the field through which such investigation can be pursued, namely process tracing, and examine its inferential logic.

Process tracing is a strategy of data analysis underpinned by an ontology of causal mechanisms. The authors argue that it complements other practice methods by inferring social mechanisms from empirical evidence and thereby establishing a connection between praxis, practices and practitioners.

Process tracing helps the articulation of an “integrated model” of practice that relates praxis, practices and practitioners to the outcome they jointly produce. By enabling the assessment of impact, process tracing helps providing prima facie evidentiary grounds for policy action and intervention.

Process tracing affinity with the practice perspective has been so far acknowledged only to a limited extent in the social sciences, and it is, in fact, a novel research strategy for the family entrepreneurship field.

We explore the impact of equity liquidity on a firm’s dynamic leverage adjustments and the moderating impacts of leverage deviation and target instability on the link between equity liquidity and dynamic leverage in the UK market.

In applying the two-step system GMM, we estimate our model by exploring suitable instruments for the dynamic variable(s), i.e. lagged values of the dynamic term(s).

Our analyses document that a firm’s equity liquidity has a positive impact on the speed of adjustment (SOA) of its leverage ratio back to the target ratio in the UK market. We also demonstrate that the positive relationship between liquidity and SOA is more pronounced for firms whose current position is relatively close to their target leverage ratio and whose target ratio is relatively stable.

This study provides important implications for both firms’ managers and investors. Particularly, firms’ managers who wish to increase the leverage SOA to enhance firms’ value need to give great attention to their equity liquidity. Investors who want to evaluate firms’ performance could also consider their equity liquidity and leverage SOA.

We are the first to enrich the literature on leverage adjustments by identifying equity liquidity as a new determinant of SOA in a single developed country with many differences in the structure and development of capital markets, ownership concentration and institutional characteristics. We also provide new empirical evidence of the joint effect of equity liquidity, leverage deviation and target instability on leverage SOA.

The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns.

The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology).

The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based.

The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

The purpose of the paper is to explore how the effects of the COVID-19 pandemic will influence the development of revenue management practice in the visitor attractions sector.

This viewpoint paper builds on the argument that tracking previous patterns of behaviour and trends can be used to predict future actions and developments.

The paper identifies how historically the development of revenue management practice has been driven by major external trigger points often linked to sudden increases in competitive pressures, such as the deregulation of the airline industry, and expands on this to argue that the pandemic is one such trigger point that has fundamentally changed the approach to revenue management through a refocusing on key principles to manage demand and that this could potentially accelerate its development within the visitor attraction sector.

Pre-COVID, the practice of revenue management in the visitor attraction sector was underdeveloped, and the opportunities to develop revenue management had not been discussed in the academic literature. This paper suggests that the challenges of the pandemic that forced visitor attractions to focus on visitor demand management can now begin to be extended to incorporate the management of revenue and will consequently be of value for academics and practitioners.

The purpose of the study is to perform a scientific mapping and detect the evolution pattern of two emerging fields, organizational capabilities and sustainable supply chain management (SSCM), to detect and visualize the existing conceptual domains and identify less-explored areas.

This study uses a methodological combination involving systematic literature review and bibliometric analysis. The methodology was implemented in the following order: definition and selection of the material using an electronic database, descriptive analysis of the material, category selection using bibliographic coupling analysis by VOSviewer (clusterization), material evaluation and content analysis.

The research results clarify the intellectual structure within the academic field. The authors’ identified three main clusters: (1) sustainable capabilities and practices in supply chain management (SCM), (2) green SCM and performance and (3) information technology and innovation. The findings reveal that there is a rich field to be explored, especially regarding issues involving sustainable technological capabilities, sustainable initiatives and key resource development.

This study facilitates researchers’ and practitioners’ understanding and their ability to map the different paths and evolution of SSCM and organizational capabilities. It can encourage managers and policymakers alike to conceive new approaches to engage in the adoption of SSCM.

This work employs a singular approach to identify the intellectual knowledge and topics related to the implementation of SSCM by adopting the theoretical approach of sustainable organizational capacity. It contributes to the debate on distinguishing specific sustainable organizational capabilities from traditional capabilities.

The purpose of this article is to develop a configurational approach based on the TOE framework (technology, organization and environment) to understand the degree of implementation of I4.0 technologies in manufacturing small- and medium-sized enterprises (SMEs). Specifically, the study considers technological infrastructure and competence, I4.0 integration capabilities, organizational agility and strategic flexibility, environmental dynamism and industry-specific forces as simultaneous pre-conditions for achieving an effective implementation of I4.0 technologies.

This study uses the fuzzy-set qualitative comparative analysis (fsQCA) methodology as it allows for asymmetric and configurational-focused testing of proposition and sound theoretical development. In total, 305 responses were collected through a survey administered to SME managers in Europe and the United Kingdom (UK).

The study examines the influence of technology, organizational and environmental aspects on I4.0 technologies implementation in SMEs. High I4.0 degree of implementation is structured around 5 configurations, while other 4 configurations are related to low levels of I4.0 implementation.

This study proposes a configurational approach for SMEs to become I4.0 ready and how they may successfully implement I4.0 technologies. Such findings represent an original and novel contribution to existing research, offering a broad view on the I4.0 implementation by manufacturing SMEs.