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This paper aims to examine the interrelation between two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. The authors first explore whether ex ante more productive firms are those that introduce innovations (the self-selection hypothesis) and if the introduction of innovations boosts TFP growth (the returns-to-innovation hypothesis). Second, the authors study the firm’s joint dynamic decision to implement process and/or product innovations. The authors use Colombian manufacturing data from the Annual Manufacturing and the Technological Development and Innovation Surveys.

This study uses a four-stage procedure. First, the authors estimate TFP using a modified version of Olley and Pakes (1996) and Levinsohn and Petrin (2003), proposed by De Loecker (2010), that implements an endogenous Markov process where past firm innovations are endogenized. This TFP would be estimated by GMM, Wooldridge (2009). Second, the authors use multivariate discrete choice models to test the self-selection hypothesis. Third, the authors explore, using multi-value treatment evaluation techniques, the life span of the impact of innovations on productivity growth (returns to innovation hypothesis). Fourth, the authors analyse the joint likelihood of implementing process and product innovations using dynamic panel data bivariate probit models.

The investigation reveals that the self-selection effect is notably more pronounced in the adoption of process innovations only, as opposed to the adoption of product innovations only or the simultaneous adoption of both process and product innovations. Moreover, our results uncover distinct temporal patterns concerning innovation returns. Specifically, process innovations yield immediate benefits, whereas implementing both product innovations only and jointly process and product innovations exhibit significant, albeit delayed, advantages. Finally, the analysis confirms the existence of dynamic interconnections between the adoption of process and product innovations.

The contribution of this work to the literature is manifold. First, the authors thoroughly investigate the relationship between the implementation of process and product innovations and productivity for Colombian manufacturing explicitly recognising that firms’ decisions of adopting product and process innovations are very likely interrelated. Therefore, the authors start exploring the self-selection and the returns to innovation hypotheses accounting for the fact that firms might implement process innovations only, product innovations only and both process and product innovations. In the analysis of the returns of innovation, the fact that firms may choose among a menu of three innovation strategies implies the use of evaluation methods for multi-value treatments. Second, the authors study the dynamic inter-linkages between the decisions to implement process and/or product innovations, that remains under studied, at least for emerging economies. Third, the estimation of TFP is performed using an endogenous Markov process, where past firms’ innovations are endogenized.

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.

At the beginning of the 21st century, a new class of information workers, the “information have-less” has risen. This class of workers alleviates the influence of information and communication technologies (ICTs) revolution on poverty and unemployment. The purpose of this study is to investigate the presence of this class of workers in Egypt and assess the size and potential growth of this category of workers.

The study clarifies the conceptual framework of the new division of labor, in the information age. The Central Agency for Public Mobilization and Statistics, American Chamber of Commerce in Egypt, Ministry of Communications and Information Technology and Information and Decision Support Center websites provided secondary data for this study. These data are used to assess the size of “the information have less” in Egypt.

The division of work and class, in the 21st century, depends on the level of skills possessed to work with ICTs. So, class and labor nowadays could be divided into self-programmable labor (Innovators). Information have-less labor class, adding value to the economy by learning skills and presenting repetitive work. Generic labor class, who cannot work with ICTs, and work in jobs, that do not need computers or other ICTs. The study has shown that the “information have-less” labor class is present in Egypt since the beginning of the 21st century, in all its categories; entrepreneurism, the service sector and the manufacturing sector. There are approximately 50% of this labor class in the service sector and only 13% of the information have-less works in manufacturing sector despite the great opportunities that Egypt has to expand manufacturing to absorb more employment. The inclusion of information technology (IT), in all domains, has not decreased employment in Western countries but has reallocated information have-less employment toward the service sector, and there would probably be the same effect in Egypt.

The study highlights the need for Egyptian policymakers to encourage the manufacturing and service sectors to provide huge working opportunities. The Egyptian government has to change the educational policies, at all stages, to include digital learning skills so IT can be incorporated in a wide range of economic activities. Further research includes: conducting a survey to measure the contribution of the entrepreneurial part of the information have-less employment in Egypt. In addition, a model may be developed, by the researcher to examine the reallocation of employees in Egypt.

Studying employment, in Egypt, using the conceptual framework of the information age is rarely being done.

This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

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.

Although the place stakeholders play a key role in participatory place branding, surprisingly little interest has been shown in the people involved in participatory place branding initiatives. The purpose of this study is to explore place stakeholders’ perceptions of the meaning and scope of place branding.

This paper is based on two cases of participatory place branding, and the research design is inspired by participatory action research. The empirical material comprises observations, qualitative questionnaires and interviews.

This study identifies and describes four paradoxes in place stakeholders’ perceptions of the meaning and scope of place branding, embracing the target group (internal vs external), the objective (explore vs exploit), the stakeholders’ role (active vs passive) and the main value of place branding initiatives (process vs outcome). Furthermore, in this study, the place stakeholders’ paradoxical perceptions of place branding meant that, during the participatory processes, the authors encountered and needed to manage various opinions and behaviours, for example, “critics”, “innovators” and “relators”.

This paper contributes with a new perspective on participatory place branding. By capturing place stakeholders’ perceptions and understanding of place branding, this paper develops our knowledge and understanding of the starting point of participatory processes.

Worldwide academia is going through a major transformation because of Open Science and Recognition and Rewards movements that are linked to big societal challenges such as climate change, digitalization, growing inequality, migration, political instability, democracies under threat and combinations of these challenges. The transformations affect the human resource management (HRM) and talent management of universities. The main focus of this chapter is on collaborative innovation and the way universities participate in coalitions and strategic alliances on national and international levels. These platforms not only discuss the transformations and support the academic changes but also act as talent pools and talent exchange. This chapter provides an overview of the current state of affairs with respect to Open Science and Recognition and Rewards in academia. Next, a theoretical foundation is presented on the concepts of collaborative innovation, coopetition and HRM innovation in general. The leaders or leading organizations in the HRM innovation models often can’t make it happen on their own, in particular in highly institutionalized contexts such as academia. The legitimacy of transformations requires coalitions of the willing and therefore strategic alliances on different levels. The coalitions in academia can also contribute to academic talent management through sectoral transformations (see Recognition and Rewards) and through the way these coalitions operate.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation – artificial intelligence (AI) – for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

This study aims to explore the ways that social enterprises (SE) create value by embedding themselves in networks through the process of social innovation (SI). The processes of achieving common social missions were studied through selected organizations using an open approach to SI. Novel operational structures as well as unique forms of created value were explored.

Two organizations embedded in local and international networks were studied and were chosen due to their SI profiles. The study was based on qualitative exploratory research. In-depth analysis was conducted through interviews, open discussions, document analysis as well as personal observation to understand the dynamic interrelatedness of the main factors influencing success of SI ventures.

This paper identified the role of SI in SEs embedded in networks. Furthermore, the social value creation processes of these organizations as well as the value they create were explored. Based on the findings, SI is rooted in the personality of the included members of the network. The tools of collaboration are platforms that connect the network members to each other. The embedded organizations apply the concept of community sharing with the aim of social value creation.

By focusing mainly on system design principles, the sample consists of mainly those at the core of organizations in facilitator roles, leaving peripheral actor perceptions to be determined by secondhand observations.

While providing a general summary of factors influencing SI activities from extent literature, the paper mainly contributes by providing deeper insight into complex models of SI practices used by SEs. The paper further contributes to popularizing the growing role of SI activities in SEs.

This final chapter of this book highlights and critically discusses some specific issues concerning talent management in the context of higher education raised in the chapters of this book. It recapitulates the transition higher education is going through. This transition started decades ago but was boosted by the movements of Open Science and Recognition and Rewards. It leads to a reorientation on the conceptualization of academic performance and subsequently also on the meaning of talent and talent management in academia. It points to a shift from an exclusive and performance orientation on talent, to an inclusive, developmental approach to talent management or a hybrid form. Yet, Thunnissen and Boselie state that there is a talent crisis in academia, and this crisis urges the need for more innovative ways of developing and implementing talent management practices. This chapter ends with some recommendations for further talent management research and practice.