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Smart industry initiatives focus on intelligent and interconnected cyber-physical systems. These initiatives develop complex technical architectures that integrate heterogenous technologies, causing significant organizational complexity. Tapping into the digital capabilities of distant partners while capturing profit from such innovation is demanding. Furthermore, firms often need to establish and orchestrate inter-organizational collaborations without prior relations or established trust. As a result, smart industry initiatives bring together disparate organizational forms and institutional environments, distinctive knowledge bases, and geographically dispersed organizations. We conceptualize this organizational capability as ‘distant capabilities integration’. This research explores the governance mechanisms that support such integration and their relation to value capture. We analyse 11 IoT case studies organized in three categories (process, product and technologies) of smart industry initiatives. Building on existing literature, we consider different ways to describe distance, including knowledge heterogeneity and organizational, geographical, institutional, cultural and cognitive distance. Finally, we describe the governance mode appropriate for upstream (developing foundational technologies) and downstream (leveraging existing distant technologies) smart industry initiatives.

This research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation.

Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet.

The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications.

The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability.

In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

The purpose of this study is to analyse the trends regarding housing segregation over the past 10–20 years and determine whether housing segregation has a spillover effect on neighbouring housing areas. Namely, the authors set out to determine whether proximity to a specific type of segregated housing market has a negative impact on nearby housing markets while proximity to another type of segregated market has a positive impact.

For the purposes of this paper, the authors must combine information on segregation within a city with information on property values in the city. The authors have, therefore, used data on the income of the population and data on housing values taken from housing transactions. The case study used is the city of Stockholm, the capital of Sweden. The empirical analysis will be the estimation of the traditional hedonic pricing model. It will be estimated for the condominium market.

The results indicate that segregation, when measured as income sorting, has increased over time in some of the housing markets. Its effects on housing values in neighbouring housing areas are significant and statistically significant.

A better understanding of the different potential spillover effects on housing prices in relation to the spatial distribution of various income groups would be beneficial in determining appropriate property assessment levels. In other words, awareness of this spillover effect could improve existing property assessment methods and provide local governments with extra information to make an informed decision on policies and services needed in different neighbourhoods.

On housing prices emanating from proximity to segregated areas with high income differs from segregated areas with low income, policies that address socio-economic costs and benefits, as well as property assessment levels, should reflect this pronounced difference. On the property level, positive spillover on housing prices near high-income segregated areas will cause an increase in the number of higher income groups and exacerbate segregation based on income. Contrarily, negative spillover on housing prices near low-income areas might discourage high-income households from moving to a location near low-income segregated areas. Local government should be aware of these spillover effects on housing prices to ensure that policies intended to reduce socioeconomic segregation, such as residential and income segregation, produce desirable results.

Furthermore, a good estimation of these spillover effects on housing prices would allow local governments to carry out a cost–benefit analysis for policies intended to combat segregation and invest in deprived communities.

The main contribution of this paper is to go beyond the traditional studies of segregation that mainly emphasise residential segregation based on income levels, i.e. low-income or high-income households. The authors have analysed the spillover effect of proximity to hot spots (high income) and cold spots (low income) on the housing values of nearby condominiums or single-family homes within segregated areas in Stockholm Municipality in 2013.

The purpose of this paper is to investigate how to optimise corporate social responsibility (CSR) when communicating via social media. In particular, the communication type, cause proximity and CSR motives are addressed facing the increased demand for transparency and the grown consumers' expectations towards socially responsible brands.

Online survey was developed, based on a profound literature review and a field research we did on the actual social media behaviour of socially responsible brands. Consumers attitudes and behavioural reactions in terms of willingness to participate in a campaign, to spread e-WOM and to purchase were investigated, as a function of CSR motives (value vs performance vs value and performance) and cause proximity (national vs international), respectively, with monologue (study 1) and dialogue communication type (study 2).

Cause proximity enhanced the campaign participation, and this effect was pronounced for both, monologue and dialogue type of communication. CSR motives modulated the willingness to spread electronic word-of-mouth, and this holds for both, monologue and dialogue communication. Attitudes and purchase intention were highest when value- and performance-driven motives were communicated, but these effects appeared only when the message was in a dialogue form of communication. Message credibility and CSR motives credibility perception further modulated consumers response.

The outcomes could be used in developing marketing (communication) strategies leading to values and revenues optimisation.

The results are discussed in a framework of how CSR resonates via social media.

Internet of Things’ (IoT’s) first wave started with tracking services for better inventory management mainly using radio frequency identification (RFID) technology. Later on, monitoring services became one of the major interests, including sensing technologies, and then more actuation for remote control-type of IoT applications such as smart homes, smart cities and Industry 4.0. In this paper, the authors focus on the RFID technology impairment. They propose to take advantage of the mature IoT technologies that offer native service discovery such as blutooth or LTE D2D ProSe or Wifi Direct. Using the automatic service discovery in the new framework will make heterogeneous readers aware of the presence of other readers and this will be used by the proposed distributed algorithm to better control the multiple RFID reader interference problem. The author clearly considers emerging Industry 4.0 use case, where RFID technology is of major interest for both identification and tracking. To enhance the RFID tag reading performance, collisions in the RFID frequency should be minimized with reader-to-reader coordination protocols. In this paper, the author proposes a simple distributed reader anti-collision protocol named DiSim that makes use of proximity services of IoT network and is compliant with the current RFID standards. The author evaluates the efficiency of the proposal via simulation.

In this paper, the author proposes a simple distributed reader anti-collision protocol named DiSim that makes use of proximity services of IoT network and is compliant with the current RFID standards. The author evaluates the efficiency of the proposal via simulation to study its behavior in very dense and heterogeneous RFID environments. Specifically, the author explores the coexistence of powerful static readers and small mobile readers, comparing the proposal with a standard ETSI CSMA method. The proposal reduces significantly the number of access attempts, which are resource-expensive for the readers. The results show that the objectives of DiSim are met, producing low reader collision probability and, however, having lower average readings per reader per time.

DiSim is evaluated with the ETSI standard LBT protocol for multi-reader environments in several environments with varied levels of reader and tag densities, having both static powerful RFID readers and heterogeneous randomly moving mobile RFID readers. It effectively reduces the number of backoffs or contentions for the RFID channel. This has high reading success rate due to the avoided collisions; however, the readers are put to wait, and DiSim has less average readings per reader per time. As an additional side evaluation, the ETSI standard LBT mechanism was found to present a good performance for low-density mid-coverage scenarios, however, with high variability on the evaluation results.

To show more results, the author needs to do real experimentation in a warehouse, such as Amazon warehouse, where he expects to have more and more robots, start shelves, automatic item finding on the shelve, etc.

Future work considers experimentation in a real warehouse equipped with heterogeneous RFID readers and real-time analysis of RFID reading efficiency also combined with indoor localization and navigation for warehouse mobile robots.

More automatization is expected in the future; this work makes the use of RFID technology more efficient and opens more possibilities for services deployment in different domains such as the industry which was considered not only in this paper but also in smart cites and smart homes.

Compared to the literature, the proposal offers the advantage to not be dependent on a centralized server controlling the RFID readers. It also offers the possibility for an existing RFID architecture to add new readers from a different manufacturer, as the readers using the approach will have the possibility to discover the capabilities of the new interaction other RFID readers. This solution takes advantage of the available proximity service that will be more and more offered by the IoT technologies.

Innovation ecosystems (IEs) have attracted the attention of policymakers and researchers because of their potential to positively affect territories, creating shared value. However, due to the fragmentation of IEs, how this happens in different IEs has been explored only partially. This research aims to bridge this gap, aiming to support policymakers in understanding how to foster shared value in diverse IEs.

The paper identifies, based on the literature, two “drivers of aggregation” of IE's actors as key dimensions characterizing shared value in IEs, namely physical proximity and dominant issue. If these are combined, three archetypes emerge: Hub- and Chain-Driven, Place-Driven, Competence- and Issue-Driven IEs.Then, elements useful for understanding shared value creation in these archetypes are framed and studied in real cases.

Results reveal that aggregation drivers affect shared value creation, which differ among archetypes: in Competence- and Issue-Driven IEs alignment is challenged by the low physical proximity, which in Place-Driven IEs is high, but not enough to grant shared value; in Hub- and Chain-Driven IEs, the hub is the orchestrator, representing both a driver and a risk.

Differences in shared value creation processes relate to the set-up of the IE, which has relevant implications for policy definition. In Competence- and Issue-Driven IEs, policies at diverse levels align in funding and promoting the IE; in Place-Driven IEs, policies support anchors' development on-site; in Hub- and Chain-Driven IEs, policies, sometimes absent, should foster partnerships for projects for the territory, IE's enlargement and resilience.

This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.

In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.

In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.

The study is based on actual historical data and is purely data driven.

The study demonstrates the utility of the analytical methodology but carries not implementation recommendations.

This is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.

This article aims to evaluate the entry and exit of companies from local productive structures, with a specific focus on the sectoral complexity of these activities and the complexity of these portfolios. The study focuses on empirically demonstrating the thesis that related economic diversification exacerbates the development gap between more and less complex regions.

The article uses indicators formulated by the economic complexity approach. They allow a relevant descriptive analysis of the economic diversification process in Brazilian micro-regions and provide the foundation for the econometric tests conducted. Through three distinct estimation strategies (OLS, logit, probit), the influence of complexity and relatedness on the entry and exit events of firms from local portfolios is tested.

In all estimated models, the stronger relationship between an activity and a portfolio significantly increases its probability of entering the productive structure and, at the same time, acts as a significant factor in preventing its exit. Furthermore, the results reveal that the complexity of a sector reduces the probability of its specialization in less complex regions while increasing it in more complex regions. On the other hand, sectoral complexity significantly increases the probability of a sector leaving less complex local structures but has no significant effect in highly complex regions.

Due to the data used, the indicators are calculated considering only formal job numbers. Additionally, the tests do not detect the influence of spatial issues. These limitations should be addressed by future research.

The article characterizes a prevailing process of uneven development among Brazilian regions and brings relevant implications, primarily for policymakers. Specifically, for less complex regions, policies should focus on creating opportunities to improve their diversification capabilities in complex sectors that are not too distant from their portfolios.

The article makes an original contribution by proposing an evaluation of regional diversification in Brazil with a focus on complexity, introducing a more detailed differentiation of regions based on their complexity levels and examining the impact of sectoral complexity on diversification patterns within each group.

This paper aims to map literature about innovation capabilities (IC) taking into consideration industrial clusters to propose a conceptual framework that synthetizes the main factors and subfactors responsible for ICs; in addition, the paper also proposes a research agenda.

A systematic literature review (SLR) was performed; academic papers were analyzed qualitatively and quantitatively.

The authors provide a descriptive analysis followed by a thematic synthesis, in which we present 05 enablers and 20 critical factors (CF) of IC in clusters. The proposed framework emphasizes what needs to be done or improved to increase IC in cluster-based companies. Based on this systematic review and the framework proposed, the authors identified opportunities for future research.

The enablers and CF identified through SLR were not validated empirically. Therefore, future studies on the current topic are required to validate the framework by investigating which factors are more relevant to cluster-based companies that intend to improve their innovative performance.

The present findings have important implications for the identification of the factors and subfactors that may contribute to the development of IC, which may help managers and decision-makers in recognizing which factors are the most responsible for business innovation.

The paper identifies enablers related to the development of IC in industrial cluster and presents a research agenda. The framework represents a guideline for companies to achieve better innovation performance.

The purpose of this paper is to investigate the most recurrent variables characterizing the collaborative relationships of industrial symbiosis (IS) (hereinafter also referred to as “anatomic” variables) established in the attempt to adopt circular economy (CE) by collecting evidence from a rich empirical set of implementation cases in Italy.

The current literature on IS was reviewed, and a content analysis was performed to identify and define the “anatomic” variables affecting its adoption in the circular economy. We followed a multiple-case study methodology investigating 50 cases of IS in Italy and performed a content analysis of the “anatomic” variables characterizing each case.

This research proposes the “anatomic” variables (i.e. industrial sectors involved, public actors involvement, governmental support, facilitator involvement and geographical proximity) explaining the cases of IS in the circular economy. Each “anatomic” variable is discussed at length based on the empirical evidence collected, with a particular reference to the impact on the different development strategies (i.e. “bottom-up” and “top-down”) in the cases observed.

Current literature on IS focuses on a sub-set of variables characterizing collaboration in IS. This research builds on extant literature to define a new framework of five purposeful “anatomic” variables defining IS in the circular economy. Moreover, we also collect and discuss a broad variety of empirical evidence in what is a still under-investigated context (i.e. Italy).