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The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

This study aims to undertake a review of how carbon trading contributes to a reduction in emission of greenhouse gases (CHGs).

A narrative literature review approach was adopted to identify and synthesise existing literature using the Scopus and Web of Science databases. Articles were limited to the past 10 years to obtain the most current literature. The various ways in which carbon trading leads to reductions in emissions were identified and discussed.

The results showed that the main ways in which carbon trading contributes to reductions in emissions are through innovation in low-carbon technologies, restoration of ecosystems through offset money, development of renewable and clean energy and providing information on investment related to emissions.

The value of this study is to contribute to the built environment’s climate change mitigation agenda by identifying the role of carbon trading.

The output of this research identifies and contextualises the role carbon trading plays in the reduction of CHG emissions.

The study examines the presence of the pollution haven or pollution halo hypothesis in Brazil, Russia, India, China and South Africa (BRICS) and Next-11 economies. Hence, it empirically tests the direct impact of foreign direct investment (FDI) on the ecological footprint. Further, it explores the moderating role of green innovation on the nexus between FDI and ecological footprint.

The study uses the Driscoll–Kraay (DK) standard error panel regression technique to examine the long-run elasticities amongst the variables for the group of emerging countries, BRICS and Next-11, during the period of 1992 to 2018. Further, statistical robustness is demonstrated using the fully modified ordinary least squares technique.

The empirical finding shows that FDI degrades environmental quality by raising the ecological footprint. Thus, it proves that FDI is a source of pollution haven in BRICS and Next-11 countries. However, green innovation negatively moderates the relationship between FDI and ecological footprint. That means the joint impact of green innovation, and FDI proves the presence of the pollution halo hypothesis. Further, renewable energy consumption is reducing the ecological footprint, but economic growth and industrialisation are worsening the environmental quality.

This study offers policy implications for governments and policymakers to promote environmental sustainability by improving green innovation and allowing FDI that encourages clean and advanced technology.

No prior studies examine the moderating role of green innovation on the relationship between FDI and ecological footprint in the context of emerging countries.

This study has conducted exploratory research to understand who should comprise the members of a resilient supply chain for promoting an entrepreneurial ecosystem of a startup project and to determine the mechanisms for the balanced coexistence of all stakeholders. This is necessary to ensure mutual benefits for all stakeholders, each of whom has multidimensional interests. Additionally, this supply chain must be able to withstand any potential disruption risks.

This research has employed a mixed-design approach. In this context, the study conducted an extensive qualitative and quantitative investigation, including 30 interviews and a survey involving 180 potential stakeholders in this supply network, respectively in the capital city of Bangladesh, Dhaka. The analysis of the interviews utilized principles of matrix thinking, while structural equation modeling (SEM) through LISREL was employed to understand cause-and-effect relationships.

Network, platform and governance—these three independent constructs have the potential to contribute to the dependent construct, a resilient supply chain, aimed at promoting an entrepreneurial ecosystem for startup projects. It has been revealed that the management of such projects depends on the rules and regulations within the ecosystem. An excellent governance mechanism is essential for this purpose. To facilitate coexistence, the establishment of a platform is crucial, where cooperation among all members is mandatory.

For practitioners, three distinctive but closely interdependent issues are explored and resolved in this philanthropic study. It has unfolded the elements of any startup project with essential settings.

The identification of the structural dynamics of potential stakeholders within the entrepreneurial ecosystem of startups is largely absent in existing literature. Therefore, there is a need to comprehensively investigate the entire network, including their roles, responsibilities and associations. This study makes a significant and novel contribution to the existing literature. Academics and practitioners alike have ample opportunities to learn from this new aspect of relationships across three distinct areas: the entrepreneurial ecosystem, startup projects and the development of a resilient supply chain.

This study aims to help uncover corporate culture and values to attract and retain talent by understanding job reviews written by business-to-business (B2B) salespeople.

Over 40,000 job reviews on Glassdoor.com are analyzed by a dictionary-based content analysis tool, Linguistic Inquiry and Word Count (LIWC2015), to explore the links between corporate culture and linguistics characteristics of reviews as articulated by B2B salespeople. This study adopted a multidimensional scaling approach based on the nine cultural value scores to create a map of corporate profiles. A projection of the LIWC2015 scores on this map uncovers differences in language patterns and emotions expressed across the profiles.

Findings reveal a map of corporate profiles with two dimensions, namely, product-centricity and customer-centricity, that divide salesforce subculture into a 2 × 2 matrix of four types: Empathic Innovators, Product Pioneers, Customer Champions and Commodity Traders.

This study combined two data sets, scores on CultureX’s nine cultural values (agility, collaboration, customer orientation, diversity, execution, innovation, integrity, performance and respect) and job reviews on Glassdoor.com. This research seeks to develop profiles of the organizational culture and to use a blend of qualitative and quantitative methods. This study adds to the literature on salesforce subculture and showcases a solution to the methodological difficulty in categorizing and measuring culture.

Firms are driven to ride on the digital wave in today’s open innovation ecosystem. This study aims to explore the effect of digital transformation (DT) on knowledge-intensive business services (KIBS) firms’ innovation ambidexterity, namely, radical versus incremental innovation, respectively. Meanwhile, the authors evaluated the moderating role of the complexity of R&D collaboration portfolio (i.e. organizational diversity and geographic diversity) in the above relationships.

Using a panel data set of 171 Chinese listed firms in the information and communications technology services industry from 2010 to 2018, the proposed hypotheses were empirically attested.

It is found that DT has a positive relationship with radical innovation and an inverted U-shaped relationship with incremental innovation. In terms of the R&D collaboration portfolio, organizational diversity positively moderates the relationships between DT and innovation ambidexterity, respectively. The geographic diversity weakens the inverted U-shaped effect of DT on incremental innovation; however, its moderating role in the link between DT and radical innovation is not empirically verified.

Extant scholars mainly addressed the interplay between KIBS firms and their manufacturing clients, while this study reveals the different consequences of DT on KIBS firms’ innovation ambidexterity to highlight the role of KIBS firms is an independent and essential innovator in a knowledge-driven economy. Notably, the findings contribute to knowledge management (KM) and R&D literature by confirming the diversity of the R&D collaboration portfolio is a critical KM strategy for KIBS firms to develop and promote external knowledge resources.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper explores how financial technology (FinTech) organisations address poverty-related challenges when providing digital financial services. Employing the conceptual foundation of the liability of poorness (i.e. literacy gaps, a scarcity mindset, intense non-business pressures and a lack of financial slack), this paper explores the innovative strategies that FinTechs use to address these liabilities and promote entrepreneurship.

The paper uses detailed case data collected from three FinTech organisations operating in one South Asian country.

FinTech organisations' innovative strategies reflect a combination of “high touch” (human) vs “low touch” (digital) solutions. All the organisations simplified internal systems or procedures to accommodate customers. The degree to which the three organisations adopted each of the identified strategies shows an emerging typology of FinTechs; that is, innovators with high digital interactions, a mix of digital-human interactions and high human interactions.

The paper develops a typology which categorises FinTech innovative strategies. The typology highlights strategies pro-poor FinTechs use and explains the types of entrepreneurial support innovative organisations provide for their customers. Both the typology and the innovative strategies contribute to enhanced financial inclusion and entrepreneurial promotion amongst the poor.

The originality of the paper comes from its focus on FinTechs' innovative pro-poor strategies. Existing studies typically address the technology-side of innovations. In contrast, this paper combines innovative strategies with the liability of poorness to identify issues associated with financial inclusion.

This study aims to explore the adoption behaviour of consumers towards innovative technology products and services (ITPS).

A qualitative study using semi-structured in-depth interviews was conducted with 47 respondents. Their lived experiences across 50+ ITPS were studied. A grounded theory approach was used to develop a theory with reference to consumer adoption of ITPS.

Themes emerged across the adoption process, such as triggers for adoption (pressing need, making life more pleasurable, urge to acquire and forced initiation); hesitancies faced by consumers during evaluation (value alignment, utilization, ecosystem, risks with new technology and price); and factors that help in overcoming the hesitancies (word of mouth, de-risking schemes and self-devised strategies).

Innovators must understand customer triggers and design offerings that activate the same – addressing a pressing need or making lives more pleasurable. Users driven purely by an urge to acquire can be a source of early word of mouth for radical innovations. Innovations must be designed and communicated to minimize hesitancies. Mitigating schemes such as equated monthly installment and return policy can be offered to empower customers to overcome hesitancies. Factors such as price, risk, beliefs, traditions and nationalistic values assume importance, specifically in an emerging economy.

This study based on grounded theory keeps the user at the centre and explains the innovation adoption phenomenon for a wide variety of 50+ ITPS in the context of an emerging economy.

The purpose of this paper is to demonstrate how network theory and methods can provide insights into the forces shaping architectural learning agendas and knowledge construction in architectural schools.

The methodology involves conceptualising learning as a constructivist process and the agenda as an interconnected network of actors, concepts and relations. Network analysis techniques, including centrality and brokerage metrics, are used to identify roles and knowledge flows using the data locally collected from Turkish universities as well as from the OpenSyllabus open-source database.

The analysis reveals the enduring influence of early modernists, signalling imbalanced canon formation in the architectural learning system. However, marginal voices highlight struggles in integrating unconventional perspectives. Limited integration of local figures indicates a consolidation of Eurocentric epistemes. Identifying these hidden forces is vital for reimagining learning agendas and socio-culturally engaged forms of learning. Pioneering figures demonstrate potential for synthesis when situated as brokers, not bifurcated schools.

The outcomes are limited by the geographical and temporal boundaries of the data and the analysis method employed. Despite limitations, the diagnostic network framework reveals architectural learning as an open, contested ecosystem demanding pluralistic pedagogies concerning not only the global but the local, both canonical and marginal. Further research covering more data could enrich the understanding of qualitative complexities.

The network perspective prompts critical reflexivity about power, ideology and exclusion in knowledge construction. Strategic inclusion and diversification of voices provide pathways to bridge divides and ground learning locally.

This research offers a methodology model to examine forces and influences shaping architectural education by elucidating hidden and remote roles and knowledge gaps in learning agendas. Extending the techniques more widely can enable strategic interventions toward inclusive, impactful learning across disciplines, time and geographies.