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The collaborative economy (CE), and within it, collaborative consumption (CC) has become a central element of the global economy and has substantially disrupted service markets (e.g. accommodation and individual transportation). The purpose of this paper is to explore the trends and develop future scenarios for market structures in the CE. This allows service providers and public policy makers to better prepare for potential future disruption.

Thought experiments – theoretically grounded in population ecology (PE) – are used to extrapolate future scenarios beyond the boundaries of existing observations.

The patterns suggested by PE forecast developmental trajectories of CE leading to one of the following three future scenarios of market structures: the centrally orchestrated CE, the social bubbles CE, and the decentralized autonomous CE.

The purpose of this research was to create CE future scenarios in 2050 to stretch one’s consideration of possible futures. What unfolds in the next decade and beyond could be similar, a variation of or entirely different than those described.

Public policy makers need to consider how regulations – often designed for a time when existing technologies were inconceivable – can remain relevant for the developing CE. This research reveals challenges including distribution of power, insularity, and social compensation mechanisms that need consideration across states and national borders.

This research tests the robustness of assumptions used today for significant, plausible market changes in the future. It provides considerable value in exploring challenges for public policy given the broad societal, economic, and political implications of the present market predictions.

Strategic positioning of foreign firms in a host market is vital for their success. By integrating the resource partitioning theory and the resource-based view, this study aims to investigate foreign firms’ strategic positioning (i.e. their choice of generalist or specialist positioning strategy) and its performance implications in the US market.

The final sample includes 212 foreign companies from 28 countries operating in the US market. Multiple data sources were used to collect data of these foreign companies’ subsidiaries in the USA This study used logistic regression to test its major hypotheses.

The results of this study suggest that a generalist positioning strategy is positively related to performance in a host market. It is also found that market concentration and local market knowledge moderate this strategic positioning – performance relationship.

For a foreign firm that enters a host market, market concentration (an industry-level factor) in the host market and the firm’s local market knowledge (a firm-specific factor) play prominent roles in the strategic positioning – performance relationship.

This study offers a novel perspective of international business strategy by applying the lens of resource partitioning theory to study the relationships between multinational enterprises’ strategic positioning and performance. This study contributes to the strategy literature in that it examines the performance implications of firms’ strategic positioning (i.e. generalist or specialist positioning).

During these unprecedented times, acts of charity are deemed essential to help individuals in need and support the social safety net. Given the importance of prosocial behavior for survival through the coronavirus disease 2019 (COVID-19) outbreak, the authors investigate the effects of partitioning experiential consumption and self-construal on consumer responses.

Based on the literature on partitioning and self-construal, the findings across three experimental studies provide novel insights into the interplay between partitioning and self-construal, and offer psychological processes on prosocial and behavioral intention.

Individuals with predominantly independent (vs. interdependent) self-construals and those primed with independent (vs. interdependent) self-construals showed higher prosocial intention when the experiential product ad was in an aggregated (vs. partitioned) format. The fit between the type of format and self-construal leads to the high control coping mechanism, and ultimately prosocial intention.

Partitioning experiential consumption has not been directly examined using self-construal, providing novel insights into consumer reactions during the pandemic. This paper provides practical implications to practitioners and researchers to better understand and adapt to shifting digital consumption patterns.

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

The purpose of this paper is to explore new banks ' market niche position choices at the time of founding.

The authors used data on the establishment of new banking organizations in California, over 1979-1988, to test the hypotheses. During that time, banking within California experienced dramatic deregulation, which provided ample opportunities for new bank start-ups.

New banks were found to enter more often in specialist market niches when the market was highly concentrated, but less often when there were more non-bank financial institutions active in the market. The frequency of new specialist entries displayed an inverted U-shaped pattern as the number of established specialist banks in the market increased.

The findings confirm the idea that elements of market structure influence the niche positioning decisions of new ventures. The paper contributes to our understanding of entrepreneurial decision making in response to environmental conditions.

The decomposition of production facilities into efficient cells is one of the areas attracting increasing research attention due to the performance benefits which cellular manufacturing offers. One of the problems associated with cell formation is the restrictive character of the existing task formalization models resulting in most cases from the use of single machine tool routeings in representing the component requirements. In contrast, the industrial reality provides a more complex picture with multiple choice of processing routeings in terms of available machine alternatives which need to be considered in order to achieve an “optimum” cellular decomposition of manufacturing facilities. Presents a facility decomposition approach based on multiple choice of processing alternatives for each component. The decision making is based on a generic description of the component processing routeings using unique machine capability patterns ‐ “resource elements”. Manufacturing cells are formed using concurrent fuzzy clustering methodology and a validation procedure for selection of the “optimum” facility partition.

This article seeks to introduce the concept of the Grid‐GMPLS control plane architecture with focus on new services, models, and interoperability issues of Grid‐GMPLS (G²MPLS) and GMPLS control planes. The purpose of this activity is to design, implement and test extensions of the GMPLS Control Plane that can enable the paradigm of a G²MPLS network.

The approach deploys extended GMPLS implementations aiming to facilitate bandwidth provisioning to Grid users.

G²MPLS is a Network Control Plane architecture that implements the concept of Grid Network Services. GNS is a service that allows the provisioning of network and Grid resources in a single‐step through a set of integrated procedures. By providing a unified network/Grid infrastructure the control plane can adapt to the demands of applications having intensive requirements on both computational and network resources.

The project delivers the prototype implementation of G²MPLS. Consideration should be given to involving the vendors of the optical equipment in the development process to incorporate the project findings into their operating systems.

The G²MPLS protocol stack has been designed to be compatible with the ASON/GMPLS architecture. This could lead to possible integration of Grids in the existing operational networks, by overcoming the current limitations of Grids running over dedicated networks with their own administrative procedures.

The paper provides the description of Grid Network Infrastructures with a common and transversal Control Plane approach based on G²MPLS, in which Grid resources and optical network resources are both controlled by the same Control Plane, seamlessly spanning the different Control and Management domains of the network.

The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.

In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.

Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.

The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.

Current e-learning platforms are based on relational database management systems (RDBMS) and are well suited for handling structured data. However, it is expected from e-learning solutions to efficiently handle unstructured data as well. The purpose of this paper is to show an alternative to current solutions for unstructured data management.

Current repository-based solution for file management was compared to MongoDB architecture according to their functionalities and characteristics. This included several categories: data integrity, hardware acquisition, processing files, availability, handling concurrent users, partition tolerance, disaster recovery, backup policies and scalability.

This paper shows that it is possible to improve e-learning platform capabilities by implementing a hybrid database architecture that incorporates RDBMS for handling structured data and MongoDB database system for handling unstructured data.

The study shows an acceptable adoption of MongoDB inside a service-oriented architecture (SOA) for enhancing e-learning solutions.

This research enables an efficient file handling not only for e-learning systems, but also for any system where file handling is needed.

It is expected that future single/joint e-learning initiatives will need to manage huge amount of files and they will require effective file handling solution. The new architecture solution for file handling is offered in this paper: it is different from current solutions because it is less expensive, more efficient, more flexible and requires less administrative and development effort for building and maintaining.

The paper has dual objectives. First, the paper aims to consolidate prior research in the area of population ecology theory and provide a review and critique of this influential organizational theory. The review is both broad and extensive, covering all major theoretical streams in population ecology. Second, the paper aims to highlight a new and hitherto unexplored area for future research, which lies at the intersection of population ecology and sustainability.

The extensive and broad review included all salient published scholarly work on the topic of population ecology from 1996‐2010. Findings are reported in nine separate tables, classified by primary research focus, chronology, author, etc. Additionally, a brief summary of prior research on sustainability is provided.

Population ecology continues as a valuable and influential perspective for organizational scholars. In comparison, sustainability is a relatively new entrant in the organizational literature, since 2008. Several areas of convergence between population ecology and sustainability exist (construct dimensions, levels of analysis and outcomes). An important gap in the literature allows future research agendas to be pursued.

The major, and most widespread, global implication is that unsustainable organizational practices and strategies may be selected by ecological pressures, and that such organizations may face a decline in population density, or mortality. Sustainable practices may allow for greater firm density and a rise in survival rates for organizational populations. Future research directions investigating population ecology links to sustainability are provided.

This is the first instance where the potential contribution of population ecology to sustainability in organizations is provided.