Abstract

Despite advancements in digital technology, outdoor advertising remains a robust communication tool with broad audience appeal. However, heightened environmental awareness necessitates proactive measures within the industry. This study examines the environmental impact of banners and proposes sustainable strategies. Market analysis underscores the widespread use of banners despite alternative advertising methods, prompting an investigation into their environmental implications and mitigation solutions. The research specifically focuses on prolonging banner lifespan and campaign duration through innovative recycling approaches aligned with circular economy principles. A comprehensive literature review informs these models and strategies, identifying opportunities for sustainable banner management. A case study approach is employed to present the transformation of banners into functional raincoats using Gemini X20 Pattern Designer software. The study emphasizes the optimization of resources and adherence to copyright regulations. This practical example demonstrates the feasibility and benefits of recycling in outdoor advertising. Collaborations with tailoring workshops facilitate community engagement and contribute to economic benefits. The implementation of these practices by companies serves to enhance their sustainability credentials and leadership in environmental responsibility within the outdoor advertising sector. In conclusion, this study provides valuable insights into sustainable practices that mitigate environmental impacts while fostering economic and social benefits.

Introduction

Outdoor advertising, also known as out-of-home advertising, stands as one of the earliest forms of mass communication known to humankind [1]. Despite the rise of online advertising platforms, which have revolutionized traditional advertising methodologies [2,3], outdoor advertising maintains its relevance and efficacy in the modern era. Its enduring value lies in its capacity to engage with a broad audience simultaneously [4]. Many enterprises continue to integrate outdoor advertising into their marketing strategies alongside digital campaigns, recognizing the distinct advantages each medium offers in reaching diverse demographics and fulfilling varied promotional objectives.

However, amidst a growing societal consciousness regarding environmental sustainability, it becomes imperative for the outdoor advertising industry to adopt a proactive approach toward environmental concerns. This article aims to highlight the critical role of environmental considerations in out-of-home advertising and to outline strategies aimed at promoting sustainability within this sector. The research objective is to offer an overview of the current landscape of outdoor advertising, with a specific focus on the environmental impact of banners used in this industry.

The primary aim is to investigate models and strategies that can extend the lifespan of banners while also prolonging the duration of advertising campaigns. This research objective aligns with market research findings [5], which indicate a significant increase in the use of banners for outdoor advertising, despite the availability of several alternative advertising methods. Consequently, the first research question arises: What are the advantages and disadvantages of banners, including their environmental impact, and what viable solutions exist to mitigate their negative effects? This area of study has been relatively underrepresented in academic discourse, as most research in the field has primarily focused on the marketing aspects of advertising campaigns [6,7,8], specifically the effectiveness of outdoor advertising with particular attention to recall and recognition [9]. As a result, there has been limited examination of potential methods and strategies that could be employed to extend the longevity of banners beyond their initial use and integrate them into a closed-loop system [10].

Conclusions

This study has critically examined outdoor advertising, with a specific focus on the environmental impact of banners, proposing strategies to enhance sustainability within the sector. Through an evaluation of both the advantages and disadvantages of banners in outdoor advertising, the research has highlighted environmental concerns such as material waste and pollution associated with their production and disposal. The findings underscore the potential benefits of adopting circular economy models, which emphasize eco-friendly materials and sustainable production practices not only during production but also throughout the post-production lifecycle. This approach aims to minimize waste generation, optimize resource use, and enhance the economic viability of materials beyond their initial use in outdoor advertising campaigns.

The literature review conducted in this study established a comprehensive foundation by scrutinizing existing research on banners in outdoor advertising, encompassing market reports, academic journals, and case studies. It analyzed environmental issues related to banner production and disposal, emphasizing the need for integrated solutions that promote circularity in the use of materials. Synthesizing these findings supports the proposition that sustainable banner management can benefit significantly from upcycling and repurposing strategies. The findings highlight that the role of street advertising companies can be expanded from polluters to advocates for promoting sustainable practices within the industry. By embedding circular economy principles into their operations, these companies mitigate their environmental impact and contribute to local economic development. The research highlights the importance of innovation and responsibility in achieving a more sustainable future for the outdoor advertising sector.

Abstract

Business-to-business (B2B) suppliers need to develop specific capabilities to successfully manage digitalized touchpoints in B2B customer journeys. As digitalization leads to shifts in touchpoints, new demands arise regarding a supplier’s ability to manage these touchpoints. Developing capabilities for managing digitalized touchpoints is becoming a key aspect for suppliers in designing and managing attractive B2B customer journeys. Although extant research has highlighted the importance of managing touchpoints in the form of capabilities, this area has received surprisingly little research attention. Similarly, the B2B context in customer journeys remains under-researched. The present study investigates supplier capabilities for the management of digitalized touchpoints in B2B customer journeys. Through an exploratory in-depth case study, this research identifies seven key supplier capabilities for the management of digitalized touchpoints in B2B customer journeys: preparing customer resources, integrating customer resources, enabling collective actions, supporting customer actions, balancing activation levels, hybridizing environments, and merging digital environments. These capabilities are structured around a theoretically derived conceptualization of touchpoints as consisting of resources, actions, and environments. From a capability perspective, this study also demonstrates the key role of touchpoint control, as well as approaching touchpoints as sequences to ensure consistency across, and seamlessness between transitions of, touchpoints.

Introduction

The concept of customer journeys is increasingly recognized as a central element in marketing ( Homburg & Tischer, 2023 ; Steward, Narus, Roehm, & Ritz, 2019 ; Witell et al., 2020 ). A key building block in a customer journey is the notion of touchpoints, considered here as the interactions, or engagements, between a supplier and a customer ( Homburg, Jozić, & Kuehnl, 2017 ; Lemon & Verhoef, 2016 ). Because touchpoints are pivotal for, and embedded in, customer journeys, it becomes important for a supplier to be able to manage these ( Durmusoglu, McNally, & Chen, 2022 ; Hodgkinson, Jackson, & West, 2021 ). Poorly managed touchpoints can, for example, lead to excessive resource usage as well as bad customer experiences. A supplier’s ability to manage touchpoints becomes especially challenging in business-to-business (B2B) contexts, where customer journeys are characterized by greater complexity, a broader array of stakeholders and touchpoints, as well as longer engagement timelines ( Meyer & Schwager, 2007 ; Witell et al., 2020 ; Zolkiewski et al., 2017 ). These characteristics put added demands on the management of touchpoints but also highlight the need for further insights into B2B customer journeys ( Purmonen, Jaakkola, & Terho, 2023 ; Rusthollkarhu, Toukola, Aarikka-Stenroos, & Mahlamäki, 2022 ).

While existing research underscores the significance of touchpoints within the customer journey, and the competitive edge that can be gained through their effective management in the form of capabilities (e.g., Aichner & Gruber, 2017 ; Dhebar, 2013 ; Gao, Fan, Li, & Wang, 2021 ; Homburg et al., 2017 ), there is a notable lack of studies delving into the specific supplier-oriented capabilities necessary for managing these touchpoints ( Homburg & Tischer, 2023 ; Lemon & Verhoef, 2016 ; Maechler, Sahni, & van Oostrum, 2016 ). This is especially so in the face of advancing digitalization. Digitalization, describing the process by which digital technologies are influencing business processes (see, e.g., Li, Kannan, Viswanathan, & Pani, 2016 ), not only introduces new opportunities but also compounds the complexity of managing touchpoints ( Marino & Lo Presti, 2018 ; McColl-Kennedy et al., 2015 ). For example, both the diversity and number of touchpoints in a customer journey typically tend to increase as firms introduce digital technologies ( De Keyser, Verleye, Lemon, Keiningham, & Klaus, 2020 ; Larivière et al., 2017 ). This leads to an additional emphasis on understanding suppliers’ touchpoint management capabilities related to a new digital reality ( Ritter & Pedersen, 2020 ; Volberda, Khanagha, Baden-Fuller, Mihalache, & Birkinshaw, 2021 ).

Implications and future research

This study provides insights into the management of digitalized touchpoints in B2B customer journeys by operationalizing it as a set of capabilities. We add to existing literature by taking a supplier perspective on touchpoints, identifying seven capabilities to manage digitalized resources, actions, and environments, which together frame the management of digitalized touchpoints. We also provide guidance as to future research in the field of B2B customer journeys as well as for B2B managers in strategizing for customer journeys.

Abstract

Global virtual teamwork has emerged as a cornerstone of collaborative teamwork in today’s work landscape, characterized by cross-border collaboration using digital technologies. Although research has started to investigate the mechanisms underlying effective teamwork through information and communication technologies (ICTs), an often-neglected dimension is the pivotal social context within which this collaboration unfolds. To address this research gap, this study adopts a social capital lens on teamwork in proposing social capital as a multidimensional mediator between the usage of ICTs and team effectiveness. The research model was tested using questionnaire data from 271 technologically sensitive and aware companies in the DACH region (Germany, Austria, Switzerland). The findings reveal that using multiple ICTs alone does not suffice for effective global virtual teamwork. Therefore, this study highlights the significance of social capital for the effectiveness of global virtual teamwork, which holds significant implications for theory and practice.

Introduction

Advances in information and communication technologies (ICTs), coupled with the increasing prevalence of globalization, have driven organizations to rely on digital technologies for globally distributed collaboration—so-called global virtual teamwork ( Tavoletti & Taras, 2023 ). Due to several benefits, such as time and cost savings, talent pooling, and increased team diversity, global virtual teams were already well established in today’s workplace before the COVID-19 pandemic ( Jimenez, Boehe, Taras, & Caprar, 2017 ; Kozlowski & Ilgen, 2006 ; Tavoletti & Taras, 2023 ). However, global virtual teamwork also presents significant challenges that can threaten team effectiveness. Reliance on ICTs reduces media richness and delays feedback, including an increased risk of miscommunication and misunderstanding ( Bilotta et al., 2021 ; Klitmøller & Lauring, 2013 ; Mangla, 2021 ).

Based on the premise that a single medium cannot support the two fundamental communication processes of conveyance (i.e., sharing new information) and convergence (i.e., reaching a common understanding), we introduce the concept of ICT usage, which refers to the frequent and strategic use of multiple ICTs ( Dennis, Fuller, & Valacich, 2008 ; Niinimäki, Piri, Lassenius, & Paasivaara, 2012 ). We propose that high levels of ICT usage will increase team effectiveness, which consists of performance, longevity, and satisfaction ( Dennis et al., 2008 ; Hackman, 1987 ).

Conclusion

The confluence of digitalization and globalization has had an enduring influence on utilizing virtual and global collaboration in the workplace. Due to their embeddedness in a social context, teams are a focal point of multiple levels, linking the individual team members and the organizational system holistically ( Kozlowski & Bell, 2013 ). This requires a profound comprehension of the social context, which can enhance the uptake of ICTs and the effectiveness of global virtual teamwork. To fill this research gap, we conceptualized social capital as a multidimensional mediator, encompassing structural, cognitive, and relational social capital ( Nahapiet & Ghoshal, 1998 ), between the adoption of ICTs and team effectiveness composed of performance, satisfaction, and longevity.

In summary, this study provides a solid foundation for integrating social capital in global virtual teamwork. The results of our study indicate that frequent use of multiple ICTs does not influence the effectiveness of global virtual teams. Instead, a significant positive relationship was found between ICT use and social capital. Regarding the individual dimensions of social capital, this relationship was only found for structural social capital. Similar results were obtained for the mediator hypotheses, with both total and structural social capital acting as strengthening mediators between ICT usage and team effectiveness. Conversely, cognitive and relational social capital did not show significant mediating effects. The present research context makes developing cognitive and relational social capital difficult, although their importance cannot be denied, as both social capital and its individual dimensions show a positive relationship with team effectiveness. Possible ways to build cognitive and relational social capital include allocating sufficient time for team interactions and creating shared physical spaces, such as face-to-face meetings, to balance them. Overall, our study provides insights into the extent to which the integration of social capital enhances team effectiveness in today’s digitized and globalized world.

Abstract

This study investigated the determinants of financing decisions of firms from the Middle East and North Africa (MENA) region, particularly the effect of public corruption on financing decisions and the effect of disorder following the Arab Spring on public corruption-financing structure relationship. The study encompasses a total of 800 business entities from 14 different countries, spanning the time frame of 2005–2018. Data is analyzed through the application of static fixed effects and dynamic GMM-System models. Results indicate that large, tangible companies prefer debt, whereas profitable companies with more room for growth choose equity. Less corruption is associated with increased corporate debt. The use of debt decreased because of the Arab Spring, and corruption’s effect on leverage became weaker. The speed of adjustment to the target leverage is comparatively slow for book leverage, while it is significantly greater for market leverage. These outcomes are consistent with the pecking order behavior resulted from increasing information asymmetry, but the tradeoff theory has some support as well. The implications of this study entail the need for enhancing investor protection, strengthening control measures, increasing transparency, and fostering the overall growth of the financial system to facilitate enterprises’ use of debt financing, particularly in the post-Arab Spring era. This paper provides fresh empirical evidence demonstrating the effect of the Arab Spring on capital structure and on the relationship between country corruption and capital structure in the MENA region. The paper also expands the body of research on capital structure and corruption across countries by providing empirical findings from a region that has been relatively overlooked in previous scholarly works.

Introduction

Lack of a perfect capital market necessitates the need for capital structure (CS) policy [1, 2, 3, 4, 5]. The choice of debt and equity is important not only for stable corporate financial health but also for the role it plays in economic growth [6,7]. Since Modigliani and Miller’s [8] slice-of-the-cake argument, the literature has identified many firm-specific CS determinants [6,9] as well as macroeconomic factors [7,10, 11, 12]. Country corruption facets, such as weak regulations and inconsistent policies at the macro level, may affect financing decisions as they influence agency conflicts of interest, the risk of financial fraud, and resource waste at the business level [10,13]. Such consequences are exacerbated if accompanied by weak disclosure policies that enhance the information asymmetry problem and increase the cost of capital [14]. Consequently, financial market activity reduces as a result of investors’ low confidence.

Public corruption is a potential determinant of CS that is largely ignored. A common link is through asymmetric information as a result of ineffective management transparency, monitoring, and law enforcement [15]. This information gap is also caused by manager-shareholder agency conflicts [16]. Because of poor ruling systems and regulations, emerging countries are deemed more corrupt than developed countries [17]. Non-financial enterprises are less regulated compared with financial firms, and thus they are more exposed to corruption [18].

Conclusions

This study investigates the CS practices of non-financial companies operating in the MENA region. Country-level corruption and the Arab Spring’s effect on CS is analyzed through the application of static and dynamic models. Country corruption has an effect on CS because of the significant agency problems and information asymmetry that are associated with corrupt practices. The developing countries in the MENA region are more susceptible to corruption because of inadequate governance systems and regulations, which make them a suitable choice for this study.

Our contribution to the literature on capital structure is threefold. The study expands the research on CS across countries in a geographic area that has been relatively overlooked in previous scholarly works. Our second contribution is emphasizing the significance of enhancing institutional environments in developing countries to facilitate firms’ access to external finance, particularly when the level of corruption in a country is found to have a significant effect on the decisions that corporations make about their CS. Finally, addressing the effect of the Arab Spring, a period of political instability, on the CS decisions is the first instance in which such an analysis has been conducted. The findings of this research are likely to be significant to other developing countries and regions and carry major implications for emerging economies that share similar attributes.

Abstract

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Introduction

The building and infrastructure industries are gradually transitioning towards building information modelling (BIM) as an object-based digital representation of an asset’s physical and functional characteristics [1], [2] and digital engineering (DE) practices to enable more productive methods of planning, designing, constructing, operating, and maintaining assets, using digital processes [3]. As detailed later in the paper, “digital” encompasses approaches, tools and technologies such as BIM and DE together with data science, the Internet of Things (IoT), and AI solutions integrated to digitise assets throughout the lifecycle. Asset owners recognise the importance of digital transformation (DT) in delivering and managing assets [4], as it offers many benefits throughout the asset lifecycle and can enhance efficiency, value for money, productivity, innovation, and safety.

1.1. DT of large infrastructure asset ownersGovernments and government-owned corporations (GOCs) are typically owners and operators of large assets, such as buildings, infrastructure, and other asset holdings across the spectrum of services. Governments and industries must transition from ‘digital by exception’ towards ‘digital by default’ [5], [6]. The infrastructure sector should adopt an evidence-based approach to infrastructure planning, delivery, and operations, including BIM, DE, embedded sensors, and digital asset management [7]. DT offers significant potential value and benefits to government agencies that manage assets across the whole-of-asset lifecycle. DT can assist in strategic investing and partnering with local governments and industry to deliver more benefits, including [8]:

Conclusion

The dynamic evolution of the building and infrastructure sectors towards digitalised practices underscores the growing importance of asset owners engaging in comprehensive DT. This research project has developed a multifaceted framework for assessing DT maturity tailored to the needs of large GOCs.

The existing assessment tools and methods have been reviewed for suitability in assessing the DT in whole-of-life asset owner organisations. The reviewed tools offer varying assessment scopes, including a capability assessment, maturity assessment, and benchmarking. The need and potential benefit of a new maturity assessment tool that would balance assessment accuracy and completion efficiency were established based on the shortcomings and limitations of existing DT maturity tools and methods. The key contributions of the new DT maturity assessment framework and tool developed by the research team include (i) a holistic view on the assessment of how well organisations use data and digital solutions, focusing on BIM capabilities, the value of information, data integration aspects, components of the smart city concept and critical information management competencies; (ii) an evidence-based scoring approach including assigning weight to various assessment indicators based on their importance to ensure the accuracy of the assessment; (iii) a multiperspective assessment allowing for more accurate organisation-wide analysis; (iv) short-term and long-term recommendations based on the current and target levels gap to facilitate performance improvement while realising the benefits and value of getting to each maturity level.

Abstract

Due to the industrial revolution and, thereby with, the emergence of Industry 4.0, there has been an impact on the marketing of business firms, enabling them to digitally transform and adopt Marketing 4.0 practices supported by smart technologies. One of these is Big Data Analytics (BDA), which enables a firm to improve its marketing practices. The main objective of this study is to evaluate how integrating big data technology improves the marketing strategies of Indian FMCG companies operating in a dynamic and highly competitive market distinguished by its size and diversity, constantly changing consumer behaviour, and Marketing 4.0 trends. This study adopts a systematic approach to coding an interview with seasoned Indian FMCG professionals to explain the capabilities of Big Data in molding marketing practices. A framework is constructed based on the same. The theoretical and practical implications are discussed to visualize its applicability in business. Further, the Resource-based View (RBV) theory is discussed for describing the insights into the role and adoption of Big Data by Indian FMCG firms for enhancing performance and achieving competitiveness, along with demonstrating its benefits for decision-making, customer loyalty and internal capability assessment.

Introduction

With technological advancements, industries now utilize innovative technologies that bolster a range of corporate activities and procedures, profoundly influencing the internal operations of these firms. (Cammarano et al., 2023, Singh et al., 2022b). The fourth industrial revolution, known as Industry 4.0, which is driving this technological change for business firms, serves as a bridge between intelligent products and adaptive processes and has a significant impact on both business models and production processes (Rosário & Dias, 2022; Salkin et al., 2018). Industry 4.0 enables the digital transformation of firms, which is further impacted by different internal and external factors related to the firm. The digital transformation is required to improve customer experiences and retain customers (Kitsios et al., 2021). Despite their implementation challenges and hazards, these advancements increase market analytics, cost, channel management, client connections, professional skills, and organizational procedures (Pascucci et al., 2023). With Industry 4.0, there is a steady trend towards understanding the consumers, which includes their needs, behaviours, storytelling, and anticipation (Lampropoulos, 2023; White & Pathak, 2023). These are the core components of marketing techniques that have evolved into marketing 4.0 (Yakut, 2022). As an outcome of the emergence of trends like Big Data (BD), social media, corporate social responsibility, and e-commerce, the shift to Marketing 4.0 is accelerating (Guven, 2020). Marketing 4.0 is driven by the convergence of offline and online marketing strategies and incorporates technical advancements to provide better flexibility (Rathod et al., 2020; Kotler et al., 2017). The improvement of the consumer experience, the induction of repurchase intention, and brand promotion are some of the significant achievements of Marketing 4.0 (Dash et al., 2023). Consumer usage patterns of social media, online platforms, search engines, contextual interactions, and community influences are some of the areas the companies are looking into. Companies use the information mainly for promotional and consumer engagement activities. The consumers are seen to be transformed into advocates through marketing 4.0, where a sense of community building takes place, resulting in a meaningful engagement of the marketers and consumers (Gau, 2019).

Conclusion

This study discusses and examines the role of BD and how an FMCG firm can adopt BD to improve its overall performance and attain a competitive position in the market. This research is the first of its kind to explore the implications of BD in the Indian FMCG sector. The relationship among BD, marketing practices and FMCG from in Indian perspective is a unique contribution to this paper. In addition to its usefulness, the study explores the crucial roles that automation and decision science play. This study discusses the benefits the firm achieves for accepting BD in the business. The study reveals some of how BD helps in decision-making for FMCG firms. For Indian FMCG firms, BD is crucial to value creation and achieving business operations’ scalability. FMCG organizations are using BD and analytics solutions more and more since they help create value, scale corporate operations, and increase output in today’s competitive market. BD makes it possible to analyze consumer preferences and behaviour at each transaction stage with a business. FMCG companies may now provide personalized experiences through targeted messaging, unique offers, and channel-specific communication optimization. BD also offers in-the-moment customer insights via sentiment, preference, and behaviour research, which aids FMCG companies in creating goods that enhance customer experiences and provide them with the necessary competitive edge.

Additionally, BD facilitates effective market campaigns through real-time predictions, data processing, and analysis. BD can also be strategically used through automation, new business model innovation, decision science, and risk management. Furthermore, customer segmentation based on BD makes marketing techniques more effective, and real-time customer insights, like sentiment analysis and tracking of consumer preferences and behavior, help organizations respond quickly to market changes.

Abstract

Purpose
This study proposes and tests the effectiveness of an integrated approach to community-based internal branding (CBIB) in a business-to-business setting. It integrates classical and contemporary views on internal branding, integrating community-building activities into the proposed internal branding framework ensuring a holistic model of co-creation of a corporate brand identity. It also examines the moderation effect of employee’s personal, job, and community-related characteristics on the relationships in the proposed model.

Design/methodology/approach
This paper presents a detailed narrative review to propose a conceptual model and uses a quantitative research design to test a set of hypotheses using structural equation modeling on 400 responses collected through a survey.

Findings
This study finds that integrated CBIB is a viable approach for implementing internal branding. The effectiveness of building employee brand commitment is demonstrated through the testing of the model. We also find that employee’s age, gender, organizational tenure, membership duration (in the brand community), education level, customer interaction, leadership status, and participation in brand fests moderate the proposed relationships in the CBIB model.

Research limitations/implications
One key limitation of this paper is that it lacks the multi-cultural and multi-industry perspective, which, given promising results in the current context, may be investigated in future studies.

Practical implications
The paper proposes a community mode of implementing internal branding in a B2B setting and suggests a way to create brand champions across the organization.

Originality/value
As per the authors’ knowledge, this paper is the first quantitative investigation of integrated community-based internal branding.

Introduction

A brand must deliver its promise (Burmann & Zeplin, 2005) or the effort that goes into market research, positioning, and creation of brand identity is likely to be lost (Baumgarth & Schmidt, 2010). No doubt, the internal branding philosophy of a firm directly influences its brand image (Mäläskä, Saraniemi, & Tähtinen, 2011). In business-to-business (B2B) and service firms, front-line sales employees, and support staff are key to delivering brand experience to the customer. Further, large B2B firms, due to their extensive employee base, complex structure, and multi-national spread, are at a greater risk of presenting mixed brand messages (Lee, 2021; Martin, 2021).

Research in the areas of internal branding, and organizational behavior reveals that a strong brand identity may be built by treating employees as internal customers (Baumgarth & Schmidt, 2010; Berry, Hensel, & Burke, 1976; Grönroos, 1985, Grönroos, 1997). Despite this assertion, when it comes to the implementation of internal branding in the B2B context, the classical view of internal branding recommends a top-down approach (Iglesias, Landgraf, Ind, Markovic, & Koporcic, 2020). The brand identity is fixed and strongly controlled by the firm, and is unidirectionally communicated to external stakeholders (Iglesias et al., 2020; Kapferer, 2012). On the other hand, the contemporary school of thought is shaped by the proliferation of digitalization and the internet, and it highlights the limited efficacy of a control-driven, top-down internal branding in the current B2B landscape (Iglesias et al., 2020; Iglesias, Ind, & Schultz, 2022; Saraniemi, 2022). At the core of this school of thought is the idea that brand identity is fluid, and is constantly co-created and developed through active conversation with all stakeholder groups across corporate brand interfaces (Iglesias et al., 2020; Iglesias et al., 2022).

Results

4.1. Measurement model
Assessment of the conceptual model (Fig. 2) includes testing the measurement model, structural model, mediation, and moderation analyses, all of which are undertaken using SPSS AMOS 26. All fit indices for the measurement model, viz., χ2/df = 2.160, CFI = 0.950, TLI = 0.942, and RMSEA = 0.054 are acceptable (Hair et al., 2010). We also performed the equivalence testing of the measurement model recommended by Marcoulides and Yuan (2016) by calculating the T-size RMSEA and CFI values. The T-size calculates the minimum population CFI and maximum size of misspecification (T-size RMSEA). We find that T-size RMSEA and CFI in equivalence testing are 0.059 and 0.935 respectively (against the conventionally reported value of 0.054 and 0.950). The cut-offs for T-size for the RMSEA and CFI based on the degrees of freedom (349), sample size (400) and number of observed variables (29) are:

Abstract
This paper examines the relation between bank profit performance and business models, using a machine learning–based approach. The analysis contributes to the literature on this relation by considering the bank portfolio’s ability to yield profits as the identification criterion of strategic profiles and by including all the components of the business model simultaneously in the identification process. Our research strategy is applied to the European Union banking system from 1997 to 2021. The paper’s primary finding indicates that specialization seems to be a strategy that results in banks adopting business profiles with better profit performance, particularly if the banks specialize in the standard retail-oriented model.

Introduction

      What characteristics of bank business models enhance profitability? The search for answers has been among supervisory authorities’ priorities since the end of the Great Recession and is likely to maintain its relevance in the aftermath of the COVID-19 pandemic. The reasons for such interest lie in the fact that the relation between business models and profitability can reveal the viability of banks’ strategic plans and, hence, the viability of banks themselves. In this regard, the Anon (2016) links poorly adapted business models to low bank profitability in advanced economies, identifying the former as an eroding agent of bank resilience over time. However, concern about the profitability of banks’ business profiles has been especially strong in the European Union (EU), as reflected by the fact that the European Central Bank launched a thematic review in 2016 to assess the relation between profitability and business models (European Central Bank, 2018).

      Banking research has examined this relation in two broad and different ways, using regression analysis to explore the effects of the characteristics of the bank business profile on profitability or grouping banks into business models by means of clustering techniques.

Conclusion

     The ability of business models to generate profits is an ongoing concern for banking authorities, especially in the EU. Closely related to this concern, we propose a new methodological strategy to identify business models and to assess their relation to profit performance. Previous works on business models have assumed the business models as a given or have identified them using similarities in balance sheet components. In either of these cases, profit performance is a byproduct of the business model. By contrast, in our identification strategy, profitability is at the core of the definitions of banks’ strategic choices. In particular, business models are obtained using the contributions of the bank portfolio components to profitability as the instruments of clustering analysis.

      The empirical exercise focuses on the EU banking system in 1997–2021. According to the classification of business profiles based on their overall contributions to profit performance, the best profile is a standard retail-oriented model, the second-best one is a standard wholesale model, and the worst profile, which has only equity as a characteristic component, does not match any standard model. Therefore, specialization seems to be a strategy that results in banks adopting business profiles that perform better in terms of their contributions to profitability, particularly if the banks specialize in the standard retail-oriented model.

Abstract

     Green innovation is an essential and burning topic for environmental and organizational performance. Therefore, this research aims to examine the effect of green innovation on environmental performance, which leads to organizational performance. Another objective is to measure the impact of two dimensions of green innovation, such as green process & green product measures, on green innovation. The second prime aim of this research is to evaluate the moderation of management commitment & human resource practices in an association between green innovation and organizational & environmental performance. A total of 320 employees provided their perspectives on a self-administrated questionnaire from the textile industry of Pakistan. We have employed SEM-based multivariate modeling to examine the data. This research has measured the reflective indicators measurement model through confirmatory factor analysis, an obvious choice of structural equation modeling to examine observed and unobserved variables and indicators using PLS-SEM (partial least square-structural equation modeling). The research findings reveal a positive & significant effect of product & process innovation on green innovation. Further, green innovation significantly impacts environmental and organizational performance. A two-way interaction (moderation) of human resource practices & green innovation does not have a cogent moderating effect on organizational & environmental performance. However, management commitment has a significant moderation between green innovation & organizational performance. A three-way interaction (moderated moderation) model finds a substantial effect on organizational attainment but an insignificant impact on environmental performance. The research outcomes significantly contribute and suggest that practitioners and policymakers must institutionalize green innovation practices in their organizations to enhance their organizational and environmental performance. HR practitioners play a vibrant role in creating green norms and organizational culture. The study findings also suggest that management commitment to green innovation advocates organization-level transformations toward adopting green practices.

Introduction

     With the advent of technology and scientific advancements, the world has provided immense exposure to artificial resources, making life leisurely. However, artificial resource opulence has come at the cost of sustainability and the environment. The world has thus been facing issues with balance acquisition between sustainable usage of resources and economic development [1,2]. Managing both have been a great concern for the economies as one comes at an expense and tradeoff. Researchers have observed that economic growth and sustainability cannot go together [3,4]. Green growth and general innovation are well recognized for their interrelationship [5]. It is essential to observe that national economic development is established through soaring industrial activities [6]. In this essence, Segerstrom [7] validated through his proposed model that firm-level innovations immensely support economic growth. Green innovation is beneficial to prevent the environment from pollution, help in recycling waste, and could also save non-biodegradable energy [8,9]. Moreover, green technologies could achieve sustainable development [10,11]. In order to implement and achieve the target of green innovativeness, firms need to adopt teamwork within their organizational ecosystems (Muisyo et al., 2022). The said process can be run by key parameters such as the organizations’ access to large-scale data, the commitment of the management, and proper usage of human reserves [12,13], among other considerations. The outcomes demonstrated that firms are enticed to accept green innovation practices when internally and externally motivated organizations. Internal motives combine management’s commitment and supplier’s partnership, whereas external motives are regulations and demand pressures from customers. Thus, there is a direct relationship between the two [14]; (Parmar et al., 2022). Organizations can enhance their performance through green innovation as green practices guide them to devise strategies that can help them use fewer resources, reduce the chances of harm, and create a carbon footprint in the organization [15]. The green revolution is a unique strategy that permits organizations to create new techniques and systems that help organizations enhance their performance [16,17].

Conclusion 

     This study evaluated the connotation between green product & process innovations and green innovation. Moreover, the study examined the impact of green innovation in affiliation with organizational & environmental attainment. The study’s findings explored all the hypotheses devised to meet study objectives. Moderation effects of management commitment towards greening and green human resource practices were also examined between the primary relationship. Primary responses were obtained from the mid-career and upper management of the textile sector of Pakistan. This research revealed that product & process innovations have a cogent and affirmative effect on green innovation. The findings further demonstrated that green innovation exerted an affirmative and cogent effect on organizational & environmental performance. Thus, the senior management should seriously consider green innovation projects to increase the firm’s environmental performance and the long run, sustainable competitive growth. While evaluating the moderating effect of HR practices on environmental attainment, the findings are not supported.

Abstract

     This paper develops an ordonomic approach to the governance of sustainable business model innovation (SBMI). We clarify the distinctive roles of optimization and governance for the management of sustainable value networks and develop a sustainability cube as a new management tool for the governance of SBMI. Our cube helps management to identify and overcome social dilemmas within value networks, i.e. to form and reform relevant business relationships, thus creating and tapping second-order win-win-win potentials. Furthermore, our cube encourages management to interpret negative externality problems as “missing markets”, i.e. as an entrepreneurial challenge and as a business opportunity to serve as yet unmet needs. Finally, our cube offers an avenue to develop and strengthen the specific management competencies that foster a successful governance of SBMI.

Introduction

     After having promoted the sustainability concept of a “triple bottom line” (TBL) for 25 years, Elkington (2018) is rather disillusioned and (self-)critical of what, in effect, has become an accounting concept. Looking back, he clarifies his initial goal—and identifies what went wrong (para. 8, emphasis in original): “[T]he original idea was wider still, encouraging businesses to track and manage economic (not just financial), social, and environmental value added—or destroyed. … [T]he TBL wasn’t designed to be just an accounting tool. It was supposed to provoke deeper thinking about capitalism and its future, but many early adopters understood the concept as a balancing act, adopting a trade-off mentality.” He further elaborates (para. 13, emphasis in original): “TBL’s stated goal from the outset was system change—pushing toward the transformation of capitalism. … It was originally intended as a genetic code, a triple helix of change for tomorrow’s capitalism, with a focus … on breakthrough change, disruption, asymmetric growth (with unsustainable sectors actively sidelined), and the scaling of next-generation market solutions.” Looking ahead, he states (para. 16 f.): “[W]e need a new wave of TBL innovation and deployment. … Hence the need for a ‘recall.’ I hope that in another 25 years we can look back and point to this as the moment [we] started working toward a triple helix for value creation, a genetic code for tomorrow’s capitalism, spurring the regeneration of our economies, societies, and biosphere.”

Conclusion and outlook

     This paper develops an ordonomic approach to the governance of SBMI. We clarify the distinctive roles of optimization and governance for the management of sustainable value networks and develop a sustainability cube as a new management tool for the governance of SBMI. Our cube helps management to identify and overcome social dilemmas within value networks, i.e. to form and reform relevant business relationships, thus creating and tapping second-order win-win-win potentials. Furthermore, our cube encourages management to interpret negative externality problems as “missing markets”, i.e. as an entrepreneurial challenge and as a business opportunity to serve as yet unmet needs. Finally, our cube helps management to develop and strengthen the specific competencies that foster a successful governance of SBMI.

     As an outlook, we would like to emphasize several limitations of our contribution in this paper, which may stimulate further research.

     First, we deliberately skipped an important aspect. Since we wanted to concentrate on sustainability opportunities beyond the organizational boundaries of the firm, we edited out the ordonomic strategy of individual self-commitments. We therefore concentrate on perceiving the organization and its interior structures as a monolithic block. On the one hand, this assumption needs to be highlighted as a serious limitation of our tool, but on the other hand it provides a vital starting point for complementary further research on intra-organizational challenges such as culture, mind-set, etc. (see e.g., Evans et al., 2017; Geissdoerfer et al., 2018) and tensions’ management in SBMI (see e.g., Schultz, 2022a). Hence, managers using the cube must be constantly aware of this blind spot. Further research could explore the possible interplay between the three commitment strategies (external relations management) embodied in the cube and the one we left out (internal relations management). For the latter, we would like to hint to an initial ordonomic contribution along those lines cf. Will and Pies (2018).

The purpose of this research is to explore phenomena within the experience of consumer packaged goods (CPG) account representatives in customer-facing roles by investigating the impact of supply chain training, experience, and skills on sales role effectiveness in CPG companies. Findings suggest that skills needed for sales include technical competency, excellent communication skills, and the knowledge to keep commitments. Supply chain understanding within their industry impacts these identified skills by removing confusion, improving identification with their company, realistically aligning customer expectations, and building confidence.

Abstract

To what extent does the collapse of a commercial bank spread contagion across cryptocurrency markets? How do markets behave around bankruptcy if digital assets remain stuck within the bank and cannot be withdrawn? We use a BEKK model to examine contagion effects across major digital assets during the Silicon Valley Bank (SVB) collapse period in early March 2023. We find evidence of contagion across major stablecoins and Bitcoin. We also examine the price action when nearly all withdrawals at SVB were prohibited. We find substantial abnormal movements in stablecoin cumulative returns and traded volumes, indicating a “flight to safety” from less to more authoritative and trusted stablecoins. The implications for practitioners and policymakers are discussed.

Introduction

Cryptocurrencies are gradually gaining traction in trading and becoming a notable segment of the financial landscape, providing new investment avenues. However, their inherent volatility continues to hinder their broader acceptance. Among cryptocurrencies, a subset known as stablecoins, which is intended to maintain a “stable” peg to a reference currency, is crucial to the market as it allows traders to work around the volatility issue and keep money in a form equal to US dollars (De Blasis et al., 2023). Access to those digital assets is typically given by new financial technologies (also called Fintech) such as digital wallets or Centralized cryptocurrency Exchanges (CEXs), with the aim of decentralizing finance through innovative offers of financial services. However, due to the high costs associated with the trading of cryptocurrencies in US dollars, the impossibility of using US dollars on cryptocurrency exchanges,1 and the simplicity and speed of transferring those virtual assets between exchanges, stablecoins occasionally trade at a premium2 to the underlying asset they imitate (De Blasis et al., 2023). The recent bankruptcy of Silicon Valley Bank (SVB), the largest commercial bank for nearly half of all venture-backed tech startups in Silicon Valley,3 in March 2023 tied up $3.3 out of $40 billions of stablecoins in its financial statement, making this exogenous shock a significant event to study.

Thus, the aim of this research is to examine the impact of bank failure on the stability of stablecoin markets. Specifically, we examine the question of whether halting access to digital assets through the bankruptcy procedure of a traditional finance institution spills over contagion effects across those innovative instruments. Answering this question is important as the March 2023 collapse of SVB, together with the ensuing instability in a number of major stablecoins, demonstrated the importance of the interlinks between traditional and digital financial systems. The results of this research are therefore relevant to policymakers in both the traditional and modern financial systems interested in avoiding the risk of financial contagion, as well as investors willing to defend their savings and balance their portfolios against market uncertainty.

Conclusion

This study examined the extent to which the collapse of a commercial bank spread contagion across cryptocurrency markets and how those markets behave around the failure when asset withdrawals are halted. We use a BEKK-GARCH multivariate model to test for financial contagion around the Silicon Valley Bank collapse across multiple digital assets. Arguably, this is the second-largest bank run after the global financial crises experienced in history. We find evidence of volatility spillover effects across major stablecoins and Bitcoin, with a “flight to safety” from less to more stable virtual currencies.

Hence, this study provides important evidence of the ways the traditional and cryptocurrency financial systems are interlinked. Arguably, it links centralized and decentralized finance, showing the ways in which spillovers can occur between what seem to be separate realms of finance. Academics, practitioners, and policymakers alike interested in destabilizing risk in the digital finance ecosystem should pay attention to the connections with traditional finance and thus to the results of this research. A limitation of this study is that it focuses on the most liquid stablecoins and cryptocurrencies. Future studies may, therefore, further explore the links between centralized and decentralized finance, including stablecoins and other means of digital value exchange.

Abstract

Corporate bankruptcy and financial distress prediction is a topic of interest for a variety of stakeholders, including businesses, financial institutions, investors, regulatory bodies, auditors, and academics. Various statistical and artificial intelligence methodologies have been devised to produce more accurate predictions. As more researchers are now focusing on this growing field of interest, this paper provides an up-to-date comprehensive survey, classification, and critical analysis of the literature on corporate bankruptcy and financial distress predictions, including definitions of bankruptcy and financial distress, prediction methodologies and models, data pre-processing, feature selection, model implementation, performance criteria and their measures for assessing the performance of classifiers or prediction models, and methodologies for the performance evaluation of prediction models. Finally, a critical analysis of the surveyed literature is provided to inspire possible future research directions.

Introduction

Corporate bankruptcy prediction (BP) and financial distress prediction (FDP) have been a field of investigation by researchers around the world for nearly a half century. However, increasing attention has been paid to this evergreen subject since the Global Financial Crisis of 2008 (Alaminos et al., 2016), as predictions can have a significant impact on the decisions and returns of various stakeholders (Alam et al., 2021).

Corporate bankruptcy and financial distress events are not desirable (Gordon, 1971), as their legal and financial costs are prohibitive (Weiss, 1990). In addition, these events increase the expected costs for financial institutions, such as banks, to hedge against the risk of these events happening (Alnassar & Chin, 2015). To reduce exposure to risk and catch early warning signs, stakeholders including investors, bankers and governments are proactively looking for solutions to effectively analyze and predict corporate bankruptcy and financial distress events. The earliest study in terms of modern bankruptcy prediction can be traced back to 1932 when Fitzpatrick (1932) presented a successful way to distinguish between failed and healthy companies by analyzing 20 pairs of companies’ accounting ratios. Since the 1960s, several accounting-based statistical and probabilistic models have been proposed to predict corporate bankruptcy and financial distress (e.g., Beaver, 1966; Altman, 1968; Ohlson, 1980; Zmijewski, 1984; Zavgren, 1985). In addition, several studies focused on the identification of bankruptcy and financial distress drivers2 (e.g., Altman, 1968; Liang et al., 2016; Tobback et al., 2017; Tinoco et al., 2018; Mai et al., 2019), on one hand, and others focused on the design of methodologies for selecting such drivers (e.g., Tsai, 2009; Lin et al., 2014; Tian & Yu, 2017; Uthayakumar et al., 2020; Kou et al., 2021), on the other hand. Furthermore, a stream of literature investigated the causes of the under-performance of bankruptcy and financial distress prediction models such as data sample imbalance (e.g., Le et al., 2018; Veganzones & Séverin, 2018; Zoričák et al., 2020, Shen et al., 2020). In the era of the rise of machine learning and artificial intelligence technologies, the prediction of bankruptcy and financial distress has been lifted to a whole new level, as more and more new methodologies managed to improve prediction accuracy through the design features of new models (e.g., Ouenniche & Tone, 2017; Ouenniche et al., 2018a, 2018b, 2018c, 2019; Hosaka, 2019; Matin et al., 2019; Yuan et al., 2022), the type of implementation decisions such as over-sampling and under-sampling (e.g., Le et al., 2018; Sun et al., 2020; Shen et al., 2020; Du et al., 2020), and bagging and boosting (e.g., West et al., 2005; Zięba et al., 2016; Jones & Wang, 2019; Chen et al., 2020). On the other hand, another stream of research focused on reducing the computational requirements of prediction models or methods using parallel implementations that take advantage of today’s powerful computers, or to be more specific, GPUs (e.g., Huang & Yen, 2019; Le et al., 2019).

Conclusions and future research directions

The topic of corporate bankruptcy and financial distress prediction has attracted the attention of many researchers for several decades and continues to evolve with more and more advanced prediction methodologies and issue fixation solutions being proposed. This study contributes to this domain of research by providing an up-to-date state-of-the-art review, classification and critical analysis of the current BP and FDP literature, where six major research streams are identified and discussed, namely, the definition and coding of bankruptcy and financial distress; design of new prediction models/classifiers or new application of existing ones; design of new drivers or evaluation of existing ones; design or evaluation of feature selection methods; design of methodologies for the performance evaluation of prediction models; and issues affecting the performance of prediction models and related solutions. By painting the landscape of research and analyzing each research stream, this study would serve as a guide for researchers who intend to explore this field of study and/or contribute to its development.

Our analysis of the surveyed papers revealed a clear trend in terms of prediction methodologies of bankruptcy and financial distress, where more emphasis is put on advanced machine learning and artificial intelligence models such as ensemble learning models. Ensemble learning models and other methodological advances, such as DEA- and MCDA-based prediction methods, have contributed to further pushing the boundaries of research. Overall, it is fair to conclude that there is no single methodology that is better than the others, as each methodology has its strengths and weaknesses, some of which are design-related, and others are implementation decisions related, which have made the design and implementation of new ensemble models more popular.