Search results

This chapter acknowledges the difficulties in the empirical study of social ventures – broadly defined as market-driven ventures that produce social change – that arise from the vast differences among social venture firms in terms of missions, goals, identities, strategies, and structures. In an effort to improve research in this area and advance the field of social entrepreneurship, the authors advocate approaching social ventures from a configurational perspective.

This chapter begins with a discussion of what social ventures are and why organizational configurations – sets of firms that are similar across key characteristics – may be an appropriate perspective to utilize. Then, two methods – cluster analysis and set-theoretic analysis – are discussed in detail as ways to approach the study of social venture configurations. Details include descriptions of the techniques, instructions for use, examples, and limitations for each.

This chapter identifies research opportunities using configurations approaches in social venture research. Substantial possibilities for multilevel and temporally based research are discussed in depth.

A configurational approach can address the incongruence and non-findings in current social venture research and offers new opportunities for future research.

Globalization speeds up competition among nations in various sectors. In terms of multinational and transnational phenomena, countries are seen as inescapable from competition, thus the linking of the term global with “competitiveness.” The research described here explores the relationship between the competitiveness of a country and its implications for human development. For this purpose, using data envelopment analysis (DEA) and cluster analysis, 44 selected countries were evaluated. An output-oriented super-efficiency model where global competitiveness indicators are taken as input variables with human development indicators as output variables is utilized. Then cluster analysis depending on the competitiveness and human development indicators is conducted by using self-organizing maps to specify the development levels of the countries. Both analyses are repeated for years between 2005 and 2007. Finally, the relationship between the super efficiency scores and the development levels is analyzed.

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series dataset contains numerous observations of important predictor variables that can effectively be classified into groups based on their values. In such situations, cluster analysis is often employed to improve the MLR models predictive accuracy, usually by creating separate regressions for each cluster. We introduce a novel approach in which we use the clusters and cluster centroids as input data for the predictor variables to improve the predictive accuracy of the MLR model. We illustrate and test this approach with a real dataset on fleet maintenance.

This paper explores how key insights from highly cited and well-used frameworks that describe the strategies and structures of MNCs are reflected in the international configurations of US MNCs. After reviewing existing frameworks that highlight different MNC choices regarding the integration, responsiveness, and dispersion of firm value chain activities, I perform a cluster analysis on a comprehensive and confidential database of US MNCs. The results reveal five configurations which both support the importance of key insights from existing frameworks while at the same time highlighting underexplored configuration characteristics, like the low levels of integration in US MNCs, the global sourcing arrangements for accessing foreign inputs and distribution, different approaches to regional expansion, and the limited geographic expansion of US MNCs pursuing product diversification. I argue that these underexplored characteristics suggest directions for future research to better reflect the international configuration choices of MNCs.

The new Silk Road provides cross-border-e-commerce firms with an opportunity to widen their markets. Under this circumstance, the preference recognition of countries and inventory allocation among overseas warehouses both become critical issues to solve. Three Chinese smartphone brands, including HTC, Huawei, and MI, are selected in this chapter for their relatively enormous sales. DHgate and AliExpress websites are chosen as platforms to analyze the sales for data availability. This chapter first depicts key features of the sales and then, based on which, divide countries into several groups according to their preference for phones by cluster analysis. Then, based on the results of cluster analysis, this chapter further models the inventory assignment among the seven major overseas warehouses that were built by AliExpress in 2015. The results show that the HTC seems to be pursuing the “high value with high price” strategy, while the other two companies seem to be pursuing a hybrid strategy of “low-price” strategy and “high value with low price” strategy. This chapter also provides an assignment pattern of inventory among the overseas warehouses based on the real data of sales and costs.

The customer experience (CX) in the field of tourism and hospitality has captured new heights. The study aims to understand how CX has been studied in the tourism and hospitality field history using bibliometric analysis. A total of 188 research articles in the Web of Science (WoS) database were selected for bibliometric analysis using VOSviewer from 2008 to 2021. The citation analysis highlighted the most influential journals published in CX within the tourism and hospitality field. Bibliographic coupling along with content analysis helps to categorize intellectual structure in six clusters: (1) Customer experience in the physical environment, (2) Technology-oriented customer experience, (3) Customer experience as driver of well-being, (4) Emotional value in the consumption experience, (5) Behavioral intentions-oriented customer experience, and (6) Total customer experience. Even though bibliometric analysis has gained attraction in business research and growing trends of the experience economy, CX within the tourism and hospitality field was not yet explored comprehensively. The current study was an effort to fill this gap by examining how customer experience in the tourism and hospitality context has been evolved historically. Theoretical, social, and practical implications are presented to establish future research directions.

Technological progress is a determining factor in the factors leading to economic and social well-being. Simultaneously, the development of a sustainable economy is based on the conservation of resources. In the energy sector, this fact can be corroborated with the reduction of energy consumption, thus increasing economic efficiency. On the one hand, improving energy efficiency contributes to increasing the quality of life, productivity, and, implicitly, the economy, but on the other hand, it leads to excess energy use – this behavioral change is known as rebound. The research estimates the rebound effect at the macroeconomic level for European countries in the period 2000–2019, referring the analysis to each country's gross domestic product (GDP) and energy consumption, as well as comparing the preaccession and postaccession periods of Romania in the EU space. The rebound effect is determined using multidimensional analysis methods, depending on the GDP of each country as well as the behavior of each in the use of energy resources in industry, agriculture, and services. Although the study results confirm the strong link between energy consumption and GDP at the level of each state, they did not show considerable changes between countries at the level of the two periods.

Air cargo was traditionally considered as a by-product of passenger air transport. However, in the last decade a defined strategy for air cargo has gained a key position in the strategies of most combination airlines, contributing largely to the cash and profit levels of these airlines. The global air cargo industry is nowadays a mature industry with over 60 billion USD in direct revenues. The strategic context is, therefore, far beyond the basic entrepreneurial framework in which an emerging and young industry tends to operate. This chapter aims to gain an enhanced insight into the strategies of airlines that transport cargo, either in the bellies of passenger aircraft or in full-freighter aircraft. A Cluster Analysis generates a typology of seven representative clusters of air cargo operators’ strategy models. The typology proposes a spectrum of strategies for air cargo, ranging from the cluster group “Carpet Sellers” up to the “Cargo Stars” cluster. While the former tend to be the small airlines or all-cargo carriers which barely manage to cover their costs with their revenues, the latter are profitable, very large globally operating airlines that focus on both passengers and cargo with passenger and freighter aircraft. Within this spectrum there are five other main strategy groups: the “Basic Cargo Operators,” the “Strong Regionals,” the “Low Cost Low Yielder,” the “Large Passenger Wide-body Operators,” and the “Premium Cargo Operators.” Our findings suggest the existence of superior strategy models that could be defined as “winning strategies” that differ according to airline size.