Abstract

The circular economy (CE) has emerged as a sustainable alternative to the linear model of production and consumption of products and services. Consumers are key actors in the circular economy loop, yet consumer behaviour and decision-making remain at the periphery of research in this area. To close this gap and to promote related academic research we perform a systematic literature review, analysing the main areas of research in the CE relating to consumer behaviour and decision-making. The results show that there are six main areas that link consumer and CE: consumer behaviour, purchase intention and sustainable consumption; lifetime and reparability; recycled plastics, upcycling, e-waste and innovation. These areas seem incomplete and focused on a relatively small number of sectors. Further, while attitude and knowledge are the most influential elements in the consumer’s buying decision-making process, our analysis shows significant gaps in current research in this regard. A key element of consumer behaviour, the post-purchase phase relating to “use”, “recycle” and “upcycle” was found to still be obscure. We provide recommendations on how this gap can be filled.

Introduction

The circular economy (CE) has been gaining increasing attention in recent years from society, companies and public bodies (Korhonen et al., 2017). The best-known economic model until now was the linear model, based on continuous growth and the use of many resources (Kirchherr et al., 2017; Ness, 2008). The CE has emerged as an alternative to this model, due to the fundamental positive role it plays in the environment, its functions and its interactions with the economic system (Ghisellini et al., 2016). The concept of CE has been reformulated and analysed several times in recent years (Geissdoerfer et al., 2017; Ghisellini et al., 2016; Kirchherr et al., 2017; MacArthur et al., 2013; Prieto-Sandoval et al., 2018), with researchers agreeing that it is a regenerative production and consumption model. However, it cannot be regarded as an individual process, as the CE involves a change in companies, industries and the economy through shifts in values, norms, behaviours and attitudes in society (Chizaryfard et al., 2021).

To this effect, the concept of CE is being analysed from different fields such as economics, strategy, engineering and resource consumption (Clube and Tennant, 2020; Demirel and Danisman, 2019; Machado et al., 2019; Perez-Castillo and Vera-Martinez, 2021), although there are still few studies concerned with the impact of consumers on the CE loop. Consumers must be involved in the whole process and be aware of the value of CE-based products and services. However, as stated by (Kirchherr et al., 2017), the reasons why consumers do or do not take part in CE processes have not been sufficiently analysed (Camacho-Otero et al., 2018). established that although there is increasing research on consumption, the contributions often refer to possible solutions relating to the nature or meaning of CE or the consumption progress, but do not focus on the relationship between the CE and the consumer. In turn (Wastling et al., 2018), argued that the role of the consumer in the CE needs to be questioned and has not yet been fully explored. To this end, there is a need to analyse the role of consumers to resolve the existing gaps in the current literature. However, not only is the relationship between CE and consumer essential to understand, but so are the behaviours and attitudes that consumer may have regarding this process, considered a hot topic due to the wealth of information it brings to both companies and society (Stankevich, 2017). The consumer can be analysed effectively through the decision-making process, wherein the consumer is at the centre (Zhang and Benyoucef, 2016).

Discussion and conclusions

Based on this literature review, it was found that interest in the relationship between the CE and the consumer has increased in the last two and a half years, when 75% of the documents analysed were published. This reflects the fact that it is a new topic that has a long way to go, and one that is important not only at European but also at global level. This literature review provided an overview of and revealed the deep research in the existing literature regarding the CE and the consumer, in terms of consumer behaviour and consumers’ PI.

To answer the first research question – What are the main areas of research in the CE relating to consumer behaviour? – this paper summarises and interrelates the different main areas of the CE with the consumer. Notably, seven different main areas were detected, all of them interrelated, as shown in Fig. 7. A circular economic model is presented, composed of “take, make, distribute, purchase, use and dispose”, adding “recycle, upcycle and innovate”. The CE was the main concept analysed, from which six different main areas were obtained that can be connected to the circular chain in terms of consumer behaviour. They were (1) the elements that affect consumers’ behaviour, PI and sustainable consumption; (2) lifetime extension and reparability; (3) packaging and recycled plastic; (4) e-waste; and (5) upcycling. Moreover, it was identified that process improvement and involvement depend on (6) innovation, as a way to reduce barriers to CE adoption and to contribute to a more sustainable economy, and so is considered a key factor within the CE loop. Each of the areas mentioned, except for the “purchase” area, which deals with the key concepts of consumer behaviour and attitudes, analyse consumers in a superficial manner, focusing more on the product or service than on consumer reactions or behaviours. To this effect, an important gap is detected in the literature, and that is the lack of knowledge of consumers in actions such as decisions to extend the life of a product, how consumers perceive the changes in new consumption models, and even how they can be re-educated in relation to concepts based on the CE.

Abstract

The Internet of Things (IoT) is becoming pervasive and with each new installation of IoT platforms new and legacy brokers have to be exploited. New internal brokers are those under the control of the platform, while legacy external brokers are those in place managed by third parties. The solution proposed addressed problems of (a) interoperability to reduce set up time to cope with unknown data structures (devices, entities) distributed via brokers; (b) performance by dimensioning both front-end and back-end processes to reach high rates in a broker-based platform, while preserving full capability features of the data warehouse. Interoperability aspects have been addressed by introducing our concepts and a reasoner into an IoT Directory tool to manage Internal and External brokers, automate device discovery and registration from both standard and customized data models. Despite the managed complexity, a broker-based solution turned out to provide high performance. To this end, a specific assessment and architecture tuning have been performed and reported in the paper to give evidence and validation. The proposed integrated IoT Directory has been developed in the context of the Herit-Data Project, and it is currently used in the whole Snap4City network of 18 tenants and billions of data. Snap4City is an open-source IoT platform for Smart Cities and Industry 4.0, which is an official FIWARE platform and solution, EOSC service and libs of Node-RED.

Introduction

The Internet of Things (IoT) defined a paradigm for the computation and communication amongst things, which is becoming every day more and more pervasive and adopted in many different domains [1,2]. It is partially due to the worldwide intense deployment campaign about Low-Power Wide Area Network technologies [3], as well as to many approaches and protocols for communications amongst devices (e.g., Message Queue Telemetry Transport or MQTT, Next Generation Service Interfaces or NGSI, Advanced Message Queuing Protocol or AMQP, Constrained Application Protocol or COAP). The approach is also covering the cloud and fog infrastructures [4,5]. Thus, IoT network infrastructures are becoming every day more complex to be managed due to the networks’ structure and existence of several protocols, formats, and concepts [1,6]. Hence, the complexity is growing in terms of data management, not only for huge amount of data but also for interoperability and abstraction levels needed to data managing. Relevant aspects to be considered are security and privacy [7] on specific data models and entities. To increase the complexity, there is a range of different owners and managers who may control different parts of such IoT networks [8].

In this context, the concept of Gateway is relevant for any segments of IoT networks connecting, as well as for the Web of Things, WoT [9]. Gateways may be integrated with one or more Brokers to send/receive data to/from Devices/entities. The Gateways and Brokers are typically compliant with a single protocol and may be managed by third parties with respect to data management platform. In some cases, they are provided as public services for several interested customers in the same area (for example: Proximus for LoraWAN services [10]). A Gateway may abstract from the IoT Broker level managing multiple brokers for multiple organizations/tenants (which can be regarded as customers of Gateway services to manage several Devices), via some API and/or Web user interface. Typical IoT Brokers can only manage one organization, and thus are single tenant, meaning they broker messages using topic/entity concepts (which can be regarded as the key for subscription on that specific device/entity) without any internal partition of services, but as a sort of family of devices and subscriptions. Some IoT Brokers can be multi-tenant, such as the FIWARE Orion Broker [11,12], which provides support for partitioning the served devices/entities/topics in groups, and each of them may have a dedicated service/path for a specific scope (or a specific customer). Furthermore, devices of different tenants could exist physically in different places (even having identical identifiers, IDs), and the subscription to the broker’s tenant may imply receiving all messages/services in the partition in push. That is feasible only if the subscriber knows the service/path identifier and, in the event of access control, the subscriber has the grant to access the broker’s services. Different IoT Devices connected to the same broker adopt the same protocol, may use different data structures/models and have the same semantic information.

Conclusions

The proliferation of IoT devices, brokers, networks, data models, operators and tenants, makes the harmonization and management of IoT Platform a hard goal. This paper offers an analysis and a comparison amongst relevant existing platforms, and it points out the basic requirements to achieve such aims. These identified requirements are in most cases not addressed by main platforms which prefer to stay on their own end-to-end solutions with limited interoperability and capacity of exploiting legacy IoT networks in place, in terms of performance. The proposed solution addressed problems of (a) interoperability by reducing set up time to efficiently detect and learn how to process unknown data structures (devices, entities) distributed via brokers; (b) performance by dimensioning the front-end and back-end processes to reach high rates in a broker-based platform, while preserving full capabilities features of data warehouse.

As to interoperability, the main identified and solved problems are those related to a large variety of Data Models coming from non-controllable External Brokers. The issue has been solved by designing and implementing a harvester and reasoner that is capable to automatically recognize/understand and map the new data models/types into those already known by Knowledge Base. This approach, together with the definition of a comprehensive meta model and dictionary, has allowed to speed up the process more than 800 times. The harvesting and comprehension process can be periodically performed to keep the platform updated with any newly defined data models by third party brokers. Furthermore, the process is helped by Km4City ontology and Data Dictionary to recognize the new data types and models according to the semantic domain.

Abstract

Keywords

1. Global migrants, the context of work, and IB/IM research

2. Individual-level research on migration in IB and IM

3. The future of migration research: breaking away from disciplinary closure and integrating different levels of analysis

4. Concluding remarks and further implications for future research

References

Highlights

Abstract

Keywords

1. Introduction

2. Designing and calibrating public transport models

3. Optimal supply policies

4. Emerging technologies and related policies

5. Conclusions

CRediT authorship contribution statement

Acknowledgement

Appendix.

References

Abstract

Public transport provision requires substantial organisational efforts, careful planning, financial contributions from the public, and coordination between millions of passengers and staff members in large systems. Efficient resource allocation is critical in its daily operations. Therefore, public transport has been among the most popular subjects in transport economics since the infancy of this discipline. This paper presents an overview of the literature developed over the past half century, including more than 300 important contributions. With a strong methodological orientation, it collects, classifies, and compares the frequently used analytical modelling techniques, thus providing a cookbook for future research and learning efforts. We discuss key findings on optimal capacity provision, pricing, cost recovery and subsidies, externalities, private operations, public service regulation, and cross-cutting subjects, such as interlinks with urban economics, political economy, and emerging mobility technologies.

1. Introduction

Public transport, defined in this paper as high-capacity vehicle sharing with fixed routes and schedules, is the backbone of urban transport systems in global cities, especially in densely populated metropolitan areas. It is unlikely that mobility will become completely private in the near future, simply because of the inevitable traffic congestion and the difficulties of storing individual vehicles when they are not in use. In other words, even though technological development may transform the appearance of public transport, the fundamental challenge of coordinating between individual travellers who share vehicles of high capacity will remain. The purpose of public transport economics is to make this coordination more efficient, ensuring optimal resource allocation to unlock all societal benefits of mass mobility.

This work reviews more than 300 papers, including the most influential contributions that shaped our understanding of the economics of public transport over recent decades. The earliest studies date back to the 1960s and the 1970s when advanced quantitative methods were not available to calibrate disaggregate supply models, estimate sophisticated demand models, and simulate policy interventions’ impact on large urban networks. Did public transport economics significantly change over half a century? Interestingly, the main messages and policy recommendations of the economists in this field are still the same. Scale (density) economies, road pricing, substitution with underpriced car use, socially optimal subsidies, and the peak load problem are still on the research agenda in various forms, just like decades ago. However, the prevalence of popular subjects does not imply that theoretical and empirical results have achieved maximum impact on policymaking. Despite the surrounding consensus among members of the scientific community, the links between scale economies and subsidisation or the limitations of public transport pricing in congestion mitigation are not obvious in the wider transport industry, to mention only two examples. One of the challenges of public transport economics as a sub-discipline will emerge in knowledge dissemination and cross-fertilisation with related disciplines and professions. We believe that a critical overview of past research efforts is crucial in making impactful discoveries in the future.

Highlights

Abstract

Keywords

1. Introduction

2. Methodology

3. Barriers and strategies for NBS finance in different ecological domains

4. General barriers and strategies for financing urban NBS

5. Barrier 1: coordination across public and private financiers for urban NBS

6. Barrier 2: valuation and accounting for the multiple benefits of urban NBS

7. Conclusion: a framework for enabling urban NBS finance

8. Limitations and avenues for future research

Declaration of competing interest

Acknowledgements

Appendix A. Supplementary data

References

Abstract

Obtaining public and/or private finance for upscaling urban nature-based solutions (NBS) is a key barrier for reaching urban sustainability goals, including climate mitigation and adaptation. We carry out a systematic review of the academic literature to understand the key barriers and corresponding strategies for financing urban NBS. First, we report on specific financing challenges and strategies found for NBS uptake in four urban ecological domains: buildings, facades and roofs; urban green space (parks, trees); allotment gardens (including urban agriculture); and green-blue infrastructure. Across domains, we identify two overarching barriers of NBS finance: (1) coordination between private and public financiers and (2) integration of NBS benefits into valuation and accounting methods. We discuss strategies found in the literature that address these barriers; here, two things stand out. One, there is a large variety of valuation strategies that does not yet allow for an integrated accounting and valuation framework for NBS. Two, strategies aimed at coordinating public/private finance generally look for ways to encourage specific actors (real estate developers, residents) that benefit privately from an NBS to provide co-financing. We visualize our findings into a framework for enabling (public and/or private) finance for upscaling urban NBS.

1. Introduction

Nature-based solutions (NBS) are a form of eco-innovation that specifically promote nature as a means for providing solutions to climate change (mitigation and adaptation), bad air quality, loss of biodiversity, vulnerable coastlines and other threatened ecosystems, food insecurity and health, social and economic deterioration/injustice (Kabisch et al., 2016, p. 2; Nesshöver et al., 2017, p. 1216–1217). The European Commission additionally emphasizes NBS as a way to realise socially inclusive green growth (European Commission 2015). In general, NBS are recognized for their ability to simultaneously deliver multiple benefits (‘solutions’) to urban sustainability goals, such as biodiversity, climate change mitigation and adaptation, as well as social wellbeing (Kabisch et al., 2016).

NBS are realised in different urban ecological domains such as green buildings, facades and roofs, green space connected to grey infrastructure (playgrounds, street trees), parks and urban forests, allotments and community gardens as well as of different types of green-blue spaces such as lakes, urban drainage systems, permeable surfaces and wetlands. Derelict and former industrial areas and brownfield sites can potentially be re-developed as urban NBS. This diversity of NBS illustrates that NBS are adapted to place-based conditions (Dorst et al., 2019); each ecological domain or context can provide a unique set of services, benefits and values for different urban stakeholders, ranging from ecological services such as climate mitigation and water management to social and economic benefits, such as social cohesion and economic development. Urban NBS often represent local public goods (Besley and Coate 2003)—benefitting primarily those citizens in the area where they are located.

Abstract

Keywords

JEL classification

1. Introduction

2. Data and descriptive statistics

3. Crowd support and Players’ Performance: An empirical analysis

3.1. The home advantage in Points with and without the crowd

3.2. Different impact in the first or in the second half?

3.3. Impact on home and away Teams’ goals

3.4. Other measures of performance: Shots, shots on target and corners

4. Referees’ decisions with and without spectators

5. Concluding remarks

Appendix

References

Abstract

Social pressure may have relevant consequences in many contexts but it is hard to evaluate it empirically. In this paper we exploit a natural experiment in soccer to provide clear evidence of its effects. We aim to study how social pressure from the crowd in a stadium affects both players and referees. While in normal matches crowd support may be correlated to a host of variables affecting the outcome of interest, we exploit the fact that after the health emergency for the Covid-19 pandemic in 2020, soccer matches in top European Leagues have been allowed only behind closed doors, that is, without spectators in the stadiums. We use data of first and second division of five major European Leagues (Germany, Spain, England, Italy and Portugal) for the last 10 seasons and compare – using a differences-in-differences analysis – several outcomes (determined by players’ performance and referees’ decisions) of matches played with crowd support to the same outcomes when matches were played without crowd. We find considerable effects of the pressure from the crowd: while with the support of the crowd a considerable home advantage emerges in various measures of performance (points, goals, shots, etc.), this advantage is almost halved when matches are played behind closed doors.

1. Introduction

Sociologists and psychologists have long recognized the influence of social factors on the decisions and behaviors of agents.1 Economists only recently have begun to show that social pressure may be an important factor affecting individual preferences and behavior (Akerlof, 1980; Bernheim, 1994; Becker and Murphy, 2000; Benabou and Tirole, 2006).

In Economics it is now usual to assume that individuals are motivated not only by extrinsic or monetary incentives: they are also affected by social considerations, by the need to be approved socially or by the desire to please others or to avoid displeasures for them and people tend to conform to social pressure to receive social recognition or to avoid social sanctions.

Social pressure may have relevant consequences in many contexts but it is hard to evaluate it empirically, since it is hard to observe exogenous changes in the social groups or networks and it can be hard to disentangle social pressure from other factors, since variations in social pressure could be related to a number of other unobserved factors.

The COVID-19 pandemic has resulted in over 1.4 million confirmed cases and over 83,000 deaths globally. It has also sparked fears of an impending economic crisis and recession. Social distancing, self-isolation and travel restrictions forced a decrease in the workforce across all economic sectors and caused many jobs to be lost. Schools have closed down, and the need of commodities and manufactured products has decreased. In contrast, the need for medical supplies has significantly increased. The food sector has also seen a great demand due to panic-buying and stockpiling of food products. In response to this global outbreak, we summarise the socio-economic effects of COVID-19 on individual aspects of the world economy.

Introduction

Labelled as a black swan event (1) and likened to the economic scene of World War Two (2), the outbreak of COVID-19 (the disease caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-COV-2)) has had a detrimental effect on global healthcare systems with a ripple effect on every aspect of human life as we know it. Sohrabi et al. highlighted the extent of the outbreak with the World Health Organization (WHO) declaring the COVID-19 outbreak as a global emergency on 30th January 2020. (3) In a response to ‘flatten the curve’ (4), governments have enforced border shutdowns, travel restrictions and quarantine (5,6) in countries which constitute the world’s largest economies, sparking fears of an impending economic crisis and recession. (7) In an attempt to understand the turmoil effect on the economy, we summarize the effect of COVID-19 on individual aspects of the world economy, focusing on primary sectors which include industries involved in the extraction of raw materials, secondary sectors involved in the production of finished products and tertiary sectors to include all service provision industries.

1- Introduction

2- Research design

3- Literature review

4- Comprehensive conceptual framework of the sharing economy

5- Discussion

6- Implications for theory and practice on sustainability

7- Conclusion

References

Abstract

The sharing economy is seen as an important building block for transitions to sustainability. Although the sharing economy concept is widely used, comprehension varies about what makes a sharing economy business model. This study aims to review and categorize the field of sharing economy business model research, by reviewing the fundamentals of the concept of sharing and feeding them back into the business model literature. A comprehensive framework of business model categories is proposed, distinguishing four market segments of the sharing economy: Singular Transaction Models, Subscription-Based Models, Commission-Based Platforms and Unlimited Platforms. The study’s framework is grounded in an analysis of sharing economy fundamentals and business model literature, contributing to a better understanding of the potentials of sharing economy for sustainability transitions and the implementation of business model innovations.

Introduction

In recent years the sharing economy is seen as a disruptive innovation, made possible by new technologies – mainly the Internet, which is transforming economies and the way business is done (Ferrell et al. 2017; Heinrichs 2013). Enthusiasts of the sharing economy aggregate different environmental, social and economic promises, each corresponding to different framings, values and debates (Acquier et al. 2017; Cherry and Pidgeon 2018). The environmental promises of sharing centers around better resource utilization, such as reducing idle capacity by favoring access over ownership, as well as using goods to the end of their lifespan (Botsman and Rogers 2010; Demaily and Novel 2014). A typical example are cars, which are idle 95% of the time and if were accessible to non-owners, would significantly reduce the number of cars required (Frenken and Schor 2017). Furthermore it is forecasted that sharing as a dominating consumption pattern leads to business models with a selfinterest in using more durable products and recycling more actively (Demaily and Novel 2014). Some even see sharing as an opportunity to combine efficiency, consistency and sufficiency strategies and to move these into the mainstream to transform how business is done (Heinrichs 2013; Mason 2015). Others put forward social promises of sharing, viewing the sharing economy as a way to offer cheaper access to services, which will especially help the socially deprived (Hira and Reilly 2017; Schor and Fitzmaurice 2015). Additionally, sharing is seen as a possibility to promote non-reciprocal exchange, thereby fostering community building, solidarity and social bonding among individuals, leading to more social cohesion in society (Belk 2010; Gansky 2010; Stampfl 2015). The economic promises of the sharing economy are reflected in the aim to overcome centralized economic and political institutions and to replace them with well organized (peer-to-peer) networks — composed of economically and politically emancipated individuals (Acquier et al. 2017; Hira and Reilly 2017). Other commentators remark on the new business opportunities and potential arising for start-ups (Matzler et al. 2016; PwC UK 2016), necessitating established businesses pivot their business models to avoid creative destruction (Habibi et al. 2017).

1- Introduction

2- Background

3- Research methodology

4- Results

5- Discussion

6- Conclusion

References

Abstract

As circular economy and sustainability gain greater attention of governments, industry and academia, business model innovation for circularity and/or sustainability is becoming fundamental to sustain companies’ competitive advantage. A variety of business model innovation approaches have been proposed to suit circular economy or sustainability principles. Although they largely have been addressed independently as two separate knowledge areas, there is an opportunity to seize synergies from the intersection of both streams. This paper provides a review of approaches for business model innovation for circular economy and/or sustainability, based on a systematic review of academic literature and practitioner-based methodologies. The systematic literature review identified 94 publications and 92 approaches (including conceptual models, methods or tools). The different approaches were categorized according to the business model innovation process, following a three stage dynamic capability view. Subsequently they were compared based on five characteristics (nature of data, boundaries of analysis, level of abstraction, time-based view, and representation style), to allow for a better understanding of how to use the approaches in research and practice. Based on the review, key findings outlining trends and a reflection about the interface of the scopes of circular economy-oriented and sustainability-oriented business model innovation are presented. Moreover, a number of gaps are identified and a framework that maps a future research agenda to simultaneously advance both streams is outlined.

Introduction

Sustainability and circular economy (CE) are of growing interest for governments, investors, companies and the civil society. Sustainability envisions a balanced integration of economic performance, social inclusiveness, and environmental resilience, to the benefit of current and future generations (Geissdoerfer et al., 2017a). CE emerged as an umbrella concept in the 20100 s (Blomsma and Brennan, 2017), and envisions the achievement of a more resource effective and efficient economic system by intentionally narrowing, slowing and closing materials and energy flows (Bocken et al., 2016; EMF, 2015). CE is often seen as a means to achieving sustainability, but with a narrower focus on the economic and environmental dimensions (Geissdoerfer et al., 2017a). Nevertheless, not all systems (e.g. businesses, value chains) incorporating circular principles are intrinsically more sustainable (Geissdoerfer et al., 2018b). Enhanced sustainability or circularity requires changes in the way companies generate value, understand and do business. Companies are compelled to interact within an ecosystem of actors, moving from a firm-centric to a network-centric operational logic. This transition requires rethinking their incumbent business models (BM), in order to enable a decoupling of value creation and resource consumption (Bocken et al., 2016). Hence, business model innovation (BMI) towards sustainability and circularity is a fundamental capability for companies. Research related to the BM concept within the boundaries of sustainability and CE is still recent, with just 10 and 5 years of activities, respectively (Diaz Lopez et al., 2019; Foss and Saebi, 2017; Lüdeke-Freund and Dembek, 2017). As expected, these bodies of knowledge or potential ‘emerging’ research fields are still in a conceptualization stage and literature is fragmented (LüdekeFreund and Dembek, 2017; Merli et al., 2018; Nubholz, 2017). Likewise, the boundaries and synergies between circular and sustainable BMI are not clearly explored (Geissdoerfer et al., 2017a). While researchers are focusing on understanding and describing these fields, practitioners are already being ‘pushed’ for the transformation of their BMs as a means of embedding circular or sustainability thinking (European Commission, 2018, 2014). Therefore, sustainable and circular BMI approaches have also been proposed in the gray literature by companies, consultancies, governments or NGOs. Despite the excitement, a shared framework does not yet exist to support researchers/practitioners in need of understanding how to conceptualize, design and implement circular or sustainable BM as a means to solve or avoid environmental or social issues, whilst aiming for economic benefits. In addition, the way in which general BM-related research has evolved might generate confusion in regards to the interpretation of the BM construct (Lüdeke-Freund and Dembek, 2017). Research about conventional BM concepts and BMI has progressed into a more robust body of knowledge over the past 15 years. However, it is still in a consolidation phase, with inconsistencies and conceptual ambiguity associated with multidisciplinary contributions from different research fields and the elusive nature of the BM construct, which allows for interpretative flexibility (Foss and Saebi, 2017; Massa and Tucci, 2014; Wirtz et al., 2016). The BM construct is applied widely and sometimes promiscuously. Many of the proposed approaches for circular or sustainable BMI are inspired by conventional BM theory and consider circular/sustainable BMI as sub-fields of conventional BMI (LüdekeFreund and Dembek, 2017), which to a certain extent inherits the aforementioned lack of consistency. Furthermore, many authors proposing approaches for sustainable and circular BMI try to highlight their differentiation from the conventional stream and assume a positioning of individual ‘silos’, instead of promoting complementarity or integration of approaches for synergistic gains. Additionally, there is a lack of clarification of where existing tools for conventional BM are sufficient and where new tools are required for embedding circularity or sustainability in BMI (Lüdeke-Freund and Dembek, 2017; Nubholz, 2017). Not only have they assumed a ‘silo’ positioning regarding conventional stream, but also in relation to each other. Several approaches have been proposed either for circular or sustainable BMI, however few approaches try to address the integrated vision of both concepts (Antikainen and Valkokari, 2016; Geissdoerfer et al., 2018a; Vogtlander et al., 2017). In this dynamic scenario, characterized by an intensive boost of heterogeneous intellectual content in a short period, it is difficult for researchers/practitioners to decide on where to start or which approach to follow.

Since 1990s Malaysia aspired to make biotechnology and bioeconomy as her engines of economic growth to utlise the abundance of natural resources and biodiversity. The public sector plays an integral role in developing the sector and various incentives are in place for the private sector to be actively involved and to forge collaboration with the public sector. The country launched its National Biotechnology Policy in 2005 and later launched its National Bioeconomy Programme in 2010 to become the first country in South East Asia and second in Asia after China to have such an initiative. Malaysia is also very proactive in its biosafety law and regulations and has most of the related legal instrument in place. A lot of success has been recorded since the inception of the National Biotechnology Policy in terms of job creation, contribution to GDP through biobusinesses and investment from foreign companies, but the sector is not spared from challenges too. Due to the nature of the discipline that is multidisciplinary and that requires huge amount of investment, expertise and political will, there are a lot of barriers before the country emerges as a bioeconomy player. This paper discusses the public policies, initiatives and funding mechanisms in place in Malaysia that drive its research, development and commercialisation in the area of biotechnology and bioeconomy. The authors also discuss the challenges faced in Malaysia in implementing the policies.

The government’s biotechnology agenda: a historical background

The biotechnology initiatives in Malaysia commenced in mid 1990s where the government committed significant amount of investment, time and energy to make the country an international biotechnology hub. The biotechnology agenda was developed to capitalise Malaysia’s rich natural resources and biodiversity and turn them into biobusiness and wealth. Historically, Malaysia has been actively pursuing agricultural research through a number of world recognised research institutes to promote higher yield and better quality of rubber, oil palm and agricultural commodities [1]. The country also boasts a strong manufacturing sector with the presence of multinational companies. These were seen as a pre-requisite to build the biotechnology sector. The modus operandi to make the country a biotechnology hub might have changed due to change in leadership in early 2000, but the commitments remained strong which is seeing various initiatives being rolled out by the government to strengthened biotechnology and bioeconomy to make these the pillars of economic engine. In Malaysia, government institutes and agencies continue to play a central role in accelerating the biotechnology and bioeconomy agenda.

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings. © 2017 Elsevier Ltd. All rights reserved.

Introduction

The effect of high rate of business failure can be devastating to firm owner, partners, society and the country’s economy at large (Alaka et al., 2015; Edum-Fotwe, Price, & Thorpe, 1996; Hafiz et al., 2015; Xu & Zhang, 2009). The consequent extensive research into developing bankruptcy prediction models (BPM) for firms is undoubtedly justified. The performance of such models is largely dependent on, among other factors, the choice of tool selected to build it. Apart from a few studies (e.g. Altman, 1968; Ohlson, 1980), tool selection in many BPM studies is not based on capabilities of the tool; rather it is either chosen based on popularity (e.g. Abidali & Harris, 1995; Koyuncugil and Ozgulbas, 2012; Langford, Iyagba, & Komba, 1993) or based on professional background (e.g. Altman, Marco, & Varetto, 1994; Beaver, McNichols, & Rhie, 2005; Hillegeist, Keating, Cram, & Lundstedt, 2004; Lin & Mcclean, 2001; Nasir, John, Bennett, Russell, & Patel, 2000). This is because there is no evaluation material which shows and compares the relative performance of major tools in relation to the many important criteria a BPM should satisfy. Such material can provide a guideline and subsequently aid an informed and justified tool selection for BPM developers. Most prediction tools are either statistical or artificial intelligence (AI) based (Balcaen & Ooghe, 2006; Jo & Han, 1996). The most common statistical tool is the multiple discriminant analysis (MDA) which was first used by Altman (1968) to develop a BPM popularly known as Z model, based on Beaver’s (1966) recommendation in his univariate work. MDA, normally used with financial ratios (quantitative variables), subsequently became popular with accounting and finance literature (Taffler, 1982) and many subsequent studies by finance professionals simply adopted MDA without considering the assumptions that are to be satisfied for MDA’s model to be valid. This resulted in inappropriate application, causing developed models to be un-generalizable (Joy & Tollefson, 1975; Richardson & Davidson, 1984; Zavgren, 1985). Abidali and Harris (1995), for example, unscholarly employed A-score alongside Z-score (i.e. MDA) in order to involve qualitative managerial variables, alongside quantitative variables, in their analysis when logistic regression (LR) [or logit analysis] can handle both types of variables singularly.