Citation
Lombardi, R., Paoloni, P., Belyaeva, Z. and Shams, S.M.R. (2020), "Guest Editorial", Management Decision, Vol. 58 No. 8, pp. 1517-1524. https://doi.org/10.1108/MD-08-2020-036
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited
1. Smart technologies for sustainable business model: adaptation challenges and prospects in economic and cultural drift
1.1 Introduction and core focus
In information and communication technology (ICT) research and practice, smart technology is characterized as “self-monitoring, analysis and reporting technology (SMART)” (Mashhadi et al., 2018, p. 1108). Such SMART technologies, generally, are able to process information in order to take decision to adapt with the changing environment instantly, based on analysing the preceding and new information that the technology collects and stores in its memory, in support of its own artificial intelligence. The purpose of smart technology is to create, deliver and manage intelligent products/services and/or experiences, which is characterized by intensive information sharing for optimal and sustainable value creation or co-creation (Gretzel et al., 2015; Shams, 2016). In this context, smart technologies have received greater attention in recent years in business and management practice (Lombardi, 2019; Lombardi and Secundo, 2020; Akter et al., 2020), to offer products or services more competitively, sustainably and with optimized value for the involved stakeholders. For example, in the tourism and culinary industry, entrepreneurs are experimenting with robots (i.e. artificial intelligence) to not only replace the waiters/waitresses but also the chefs (Holley, 2018). In the contemporary broadcasting industry, the mega events such as Olympic, World Cup Football and other events are broadcasted, based on a broadcasting system that is supported by the Internet of Things (IoT) (Trequattrini et al., 2016), which is defined as “the interconnection via the Internet of (several) computing devices…, enabling them to send and receive data (instantly)” (Fletcher, 2015, p. 20). Universities co-create impactful knowledge enabling research and development for augmented reality and smart cities applications (Ferraris et al., 2018). On the other hand, digitalization and technologies lead to new challenges fostering rebound effects in job insecurity and robotic responsibility (Osburg and Heinecke, 2018) Regrettably, “the concept of smart technology itself has however been scarcely defined and conceptualized beyond technological fields and perspectives” (Lee, 2012; as cited in Neuhofer et al., 2015, p. 243).
Consequently, such lack of understanding and inadequate definition of smart technology beyond the ICT field brings a critical challenge to comprehensively understand the extent that should be followed to adapt smart technologies in particular business management context, in order to fully capitalize with the smart technology to sustainably design, implement and monitor smart technology-centred business and management models (Palmaccio et al., 2020). A common challenge for sustainably adapting smart technology in business and management is, it extends the risk of increased unemployment in the long-run. For example, the artificial intelligence, such as robots replace the waiters, waitresses and chefs. The blockchain technology-centred ATMs take the jobs of banks' customer service attendants and/or cashiers. In general, effective data management is instrumental for sustainably designing, implementing and monitoring any smart technology. However, in terms of data management, storage and stakeholder privacy, the giant corporations, such as “Facebook is at the centre of a huge privacy controversy” (Newman and Farrell, 2015, np). Denoting to the recent indignity of Facebook's data mismanagement, it is reported that “it was not just the user's profile data that was harvested, but also that of their friends” (McNamee and Parakilas, 2018, np). There are also many other examples of inappropriate management of data in business practice in recent years, such as in Tesco (McDonald, 2017), Thomson Reuters and LexisNexis (O'Grady, 2017). As a result, in business management, the big data-centred smart technology implementation often leads to wrong decision-making (Barth, 2017). In support of this view, Mikalef et al. (2017) also argue that “big data and analytics are also challenging existing modes of business and well-established companies” (np).
Since IoT, big data analytics, artificial intelligence and blockchains generally have implications to design and implement self-monitored analytical reporting system also in the light of corporate governance and accounting system; this special issue refers to these four technological innovations in particular, as the core area of investigation on “the challenges and prospects of smart technology to plan, implement and monitor sustainable business model in economic and cultural drift”. However, on the one hand, “there is limited understanding of how organizations need to change to embrace these technological innovations, and the business shifts they entail. Even more, the business value and strategic relevance of…technologies still remain largely underexplored” (Mikalef et al., 2017, np). On the other hand, the prospective implication of smart technologies for sustainable business model (SBM) development is also largely an unexplored area. For example, SBM incorporates a triple bottom line approach to contribute to a wide range of stakeholders, including economy, environment and society (Bocken et al., 2014; Wu et al., 2018; Battisti et al., 2019). However, indicating to the significant research gaps on the antecedent role of smart technology for SBM, Wu et al. (2018) argue that “there are essential and urgent needs to raise the awareness and call for attentions on how to innovate and energize (smart) ICTs (information and communication technologies) in order to best assist all nations to achieve the SDGs (sustainable development goals)” (p. 1), related to economic, environmental and societal welfare as the triple bottom lines of a SBM.
In the contemporary network economy (Asanuma, 2013; Kollmann and Christofor, 2014), cultural and/or economic awareness is another crucial factor to reflect on, while sustainably adapting smart technology, in order to plan, implement and evaluate SBM. Since, culture is the blend of beliefs, values and assumptions that can be inherited by an individual from her/his early childhood (Hofstede et al., 2010). Such cultural/social viewpoints can be embedded in individual's mind since their childhood. As a result, such embedded cultural viewpoints could impact on an individual's decision-making process during their adulthood, which appears as a dimension of distinctiveness in human behaviour across different culture (Steenkamp, 2001; Steenkamp and Kumar, 1999). Based on this individual cultural background based on a particular cultural viewpoint, different stakeholders, e.g. customers or suppliers estimate the substitute value propositions (Kotler, 2003) that would be most relevant to their (the stakeholders') own individual culture-specific value anticipation. Cross-cultural background appears to be decisive in choosing different aspects of SBMs in food and drink industry, focussed on green technologies in Western countries versus education technologies integration in Eastern Europe (Belyaeva et al., 2020). In the contemporary ICT age, stakeholders are overwhelmed with information about the different competitive value propositions (Berner and Tonder, 2003). Referring to such ICT-centred enormous inflow and outflow of information as ongoing basis, Murphie and Potts (2003) argue that “the saturation of contemporary culture with technological speed: we live…in a ‘dromosphere’, or speed-space. The same applies to the mind (e.g. this speed-space impact on our attitudes)” (p. 37). However, we have significant lack of understanding on how cultural issues (e.g. attitudes, values, norms, ways of thinking, etc.) would impact on sustainable adaptation of smart technology to plan, implement and monitor SBM, in addition to some recent studies of its peripheral area (Hasgall and Ahituv, 2018). Hasgall and Ahituv argue that “to the best of the authors' knowledge, this (their study) is the first empirical research to study how functioning as a CAS (complex adapting system)…translates into attitudes (culture) towards technology innovation and to technology usage (adaptation) within organizations” (2018, p. 35).
In addition, it is argued that “the volatile scenario of technological innovation” (Tebrga et al., 2018, p. 64) mainly considers technological diversification and adaptation as an organizational explorative activity; however, the exploitative prospect of technological diversification and adaptation is an overlooked area, which could limit the application of diversification and adaptation of technology (Pan et al., 2018). Since the organizations and the society are culturally broaden its horizons (Ray, 2014), and there are “important differences between assimilation (just incorporation) and adaptation (i.e. user friendliness of technology) of technology innovation” (Hasgall and Ahituv, 2018, p. 35); a scorching area with unexplored phenomena is the “impact of cultural issues on smart technology adaption for sustainable SBM”.
In this context, this special issue aimed to develop our understanding on the adaptation challenges and prospects of smart technologies to plan, implement and evaluate SBM in economic and cultural drift. The goal of this special issue is to attract rigorous research studies from scholars all over the world, contributing to the evolution of new thought in research and practice of the under-researched areas that are discussed in section 1 (introduction and core focus) of this management decision special issue. The next section presents the synopsis of the papers included in this special issue, in order to demonstrate how the special issue has pursued its aim.
2. Synopsis of the special issue papers
The paper by Maiti et al. (2020) “resource-based model for small innovative enterprises” presents a new resource-based linear programming model for resource optimization directed to small innovative enterprises (SIE). This paper moves from the resource-based view (RBV) and dynamic capabilities strategy suggesting “a distinct sight regarding resource fungibility. Study classifies resources into multipliable, rentable and expendable resources to increase adequacy of the model…The survival rate of SIE in the early stages of life cycle is very low due to the competition among SIEs”. Maiti et al. (2020) contribute to literature also by suggesting “a distinct sight regarding resource fungibility and that classifies resources into three categories, such as: multipliable, rentable and expendable resources to increase adequacy of the model”.
The paper by Garzoni et al. (2020) “fostering digital transformation of SMEs: a four levels approach” aims to investigate “how digital technologies trigger changes in the business process of manufacturing small and medium-sized enterprises (SMEs)”. The study presents the case of Smart District 4.0 in the Apulia region (South Italy). The authors analyse the “enablers of Industry 4.0 in a regional context characterized by delay in research and development and innovation performances where the companies' competitiveness is based on limited knowledge and technological assets”. Garzoni et al. (2020) provide an interesting research defining a roadmap for the “digital transformation of SMEs in a region where the profile of companies' digital maturity is still low”.
In their study titled “cross-country differences in European firms' digitalisation: the role of national culture” Rubino et al. (2020) analyse the relationship between national culture and the country level dynamics of firms' digitalization highlighting how a country's various cultural dimensions help or hinder the level of firms' digitalisation. Through their results, Rubino et al. (2020) propose “the existence of a negative, significant, relationship between both masculinity and uncertainty avoidance, and the country level of firms' digitalisation. Indulgence is found to positively and significantly influence a country's level of digitalisation, as a negative, significant, relationship between individualism and the degree of digitalisation. Power distance is found to have no significant impact.”
Rialti et al. (2020) in their paper “achieving strategic flexibility in the era of big data. The importance of knowledge management and ambidexterity” proposes the analysis of 215 survey responses from managers of organisations in continental Europe. They aim to unpack “the micro-mechanisms that exist between an organisation's ability to conduct big data analytics (BDA) and its influence on strategic flexibility. Knowledge management capabilities and organisational ambidexterity have long been considered factors influencing the aforementioned relationship”. Rialti et al. (2020) show that BDA capabilities are a relevant antecedent of an organisation's strategic flexibility.
Dal Mas et al. (2020) in their paper “smart contracts to enable sustainable business models. a case study” develop an analysis of how blockchain technology using smart contracts support the creation of SBMs. Dal Mas et al. (2020) aims at showing key elements enabling SBMs in the application of smart contracts. Through a case study, the authors “show how smart contracts can reduce the costs of transactions, increase social trust and foster social proof behaviours that sustain the development of new SBMs”.
Tiscini et al. (2020) in their paper titled “the blockchain as a sustainable business model innovation” aims to “to investigate the blockchain (BC) potentiality to drive business models towards sustainability, exploring the application of the BC technology in the agri-food industry and providing the analysis of a best practice, the Placido Volpone winery (the company), which is the first Italian winery company to introduce the BC.” Using the Value Triangle framework, the authors analyse the potential innovation aspects of BC for SBMs proposing evidence for company leaders and managers.
Biloslavo et al. (2020) in their paper “business model transformation towards sustainability: the impact of legitimation” are directed to explore “the legitimacy issues raised during a sustainable business model innovation process, deployed by an Italian company, which was analysed through the lens of the legitimation theory and the business model innovation theory.” The authors adopt a case study to show how the potentiality of digital technologies permits the sustainable business models' development. The last one needs to obtain legitimation through stakeholder relationship management.
The paper titled “analysing pro-poor innovation acceptance by income segments” by Hasan et al. (2020) aims to show “the moderating influence of different bottom of the pyramid (BOP) income segments on the antecedents of pro-poor innovation acceptance.” The authors propose a direction that have implications for “companies and government designing or selling products for millions of poor people in developing and emerging economies. Understanding the adoption behaviour based on BOP segments will enable the corporations and governments to ensure successful adoption of pro-poor innovation”.
Sigh et al. (2020) in their paper “what drives FinTech adoption? A multi-method evaluation using adapted technology acceptance model” explore the “FinTech adoption and use from the technology acceptance perspective by adding sub-constructs of the technology acceptance model (TAM), unified Theory of acceptance and use of technology (UTAUT), ServPerf, and WebQual 4.0.” The authors draft the sub-contracts into adoption, behaviour and technological contributing to the technology acceptance literature.
The study by Hack-Polay et al. (2020) titled “big data analytics and sustainable textile manufacturing decision-making about the applications of biotechnologies in developing countries” propose “issues associated with the application of big data analytics for decision-making about the introduction of new technologies in the textile industry in the developing world.” The authors assert that “the limited use of this valuable technological resource is linked to several factors, mainly cultural, generational and educational factors”.
Fiorentino et al. (2020) in their paper “how smart technologies can support sustainable business models: insights from an air navigation service provider” aim to “unravel and address the challenges of smart technologies to build and maintain a sustainable business model for organisations.” The authors present an empirical analysis showing the business value and the strategic relevance of smart technologies is underestimated in SBM adoption. Thus, they show as smart technologies are a need within business activities.
Annosi et al. (2020) in their study “digitalization in the agri-food industry: the relationship between technology and sustainable development” propose the investigation of digital technology adoption and its dominant challenges faced by firms in the agri-food industry. Using a structured literature review, they analyse “how these challenges impact on the sustainable development of digital technology for firms in the industry and provide avenues for future research” creating a reference framework in this field.
Lardo et al. (2020) in their study “the perspective of capability providers in creating a sustainable I4.0 environment” propose the analysis of the “capability providers to a sustainable I4.0 environment as an additional perspective regarding the management decisions of a smart and sustainable business model (SSBM) transformation of big corporations.” Through a case study, the authors present a framework of perspective contribution to the sustainable I4.0 environment.
Sannino et al. (2020) in their study “CEO characteristics and sustainability business model in financial technologies firms. Primary evidence from the utilization of innovative platforms” analyse the “characteristics of corporate leaders (CEOs) in Fintech sector firms representing the implementation of the sustainable business model”. This study proposes a specific benchmark profile of CEOs. Authors also identify the key features that enable opportunity exploration, development and evaluation of sustainable business models using innovative platforms.
3. Conclusions
In this special issue, the analysis of smart technologies for sustainable business model has been presented using a renewed approach directed to discover challenges and prospects in economic and cultural drift. However, research and practice in this field has been progressing; the field is yet at its emerging stage. As a consequence, many issues need to be further investigated also in the light of the smart technologies' adoption from the diverse contexts of economic and cultural issues across the world. All kind of organizations (e.g. private and public companies) are deputed to discover the impact of artificial intelligence, blockchain, big data and other smart technologies in their business models, in order to underpin the business and/or social value of contemporary technologies (Shams and Solima, 2019). The effects of such novel exploration and application of the contemporary technologies will be instrumental for fostering sustainable business models and safeguarding stakeholders.
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Further reading
Lombardi, R., Trequattrini, R., Cuozzo, B. and Minzari, A. (2020), “Big data, artificial intelligence and epidemic disasters. A primary structured literature review”, International Journal of Applied Decision Sciences, forthcoming.