Suchergebnisse

This is a conference paper. ; Text analytics and sentiment analysis can help researchers to derive potentially valuable thematic and narrative insights from text-based content such as industry reviews, leading OM and OR journal articles and government reports. The classification system described here analyses the opinions of the performance of various public and private, manufacturing, medical, service and retail organizations in integrating big data into their logistics. It explains methods of data collection and the sentiment analysis process for classifying big data logistics literature using KNIME. Finally, it then gives an overview of the differences and explores future possibilities in sentiment analysis for investigating different industrial sectors and data sources.

PurposeThe purpose of this paper is to explore how the sourcing process of the electric sports car sector is changing with respect to competitive advantage, required capabilities and emerging opportunism.Design/methodology/approachThe case study data collection covered the period from January till August 2017, which implies a total period of eight months. The empirical analysis implies a sequence of 20 conducted interviews with senior managers, team leaders and operational employees from various organizational departments and functions within Company A, various suppliers and experts from the automobile industry as well as primary and secondary literature.FindingsThis work makes a contribution to the operations capability literature. It highlights the important role that sourcing will play to achieving strategic advantage in the electric sports car segment. Four key operational capabilities are emerging in the operating model. The first links to "capacity" and the ability of suppliers to be locally based so that they can deliver high-quality products and services in the minimum time (optimizing the "time-value" configuration). The second is the "design" of the supplier network. The third relates to "supplier management." Finally, the fourth capability relates to the ability of the firm to "integrate" and "align" their marketing and IT planning processes with their sourcing process.Research limitations/implicationsThroughout the adaption of a sourcing framework and its extension to consider operational capabilities, the authors have begun to answer the research question of how the sourcing process for the supply of new electric powertrain components is being transformed. These initial findings, the authors intend to expand with more advanced case study work with the firm that will involve empirical modeling of process efficiency and inventory management.Practical implicationsThe work closes the gap regarding the need for practical application tools, designed for process managers, who are being confronted by turbulent, unpredictable and fast moving technological-driven market environments. Although the sourcing framework was developed to test the impact of the electric mobility trend, it can likewise be applied for the sourcing of components in other fast changing environments as well.Social implicationsThe paper raises the issues of the social role of the smart city planners in providing city spaces to enable the servicing of electric vehicles and to assist their production by developing the skills, capacity and capabilities of local city populations which will be needed to sustain and scale up any locally based operating model of electric vehicle production and servicing.Originality/valueAlthough much has been written about the technological challenges of electric vehicles and the rise of new entrants such as Tesla to challenge the dominance of the sports car manufacturer's very little work to data have explored the business-to-business (B2B) dimensions. The focus has been largely with the business-to-consumers (B2C) market.

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

This paper investigates the impact of human and political capitals of entrepreneurs on enterprise performance in four emerging nations.The rent generation potential of these capitals is a well established fact, however, much less is known concerning the contingent nature of their value creation prowess. In this work, we draw on institutional theory and dynamic managerial capabilities perspective to examine the interactive effect of country of origin economic developement level and the international experience of entrepreurs, on the capitals, with respect to a set of financial indicators. Employing a quantitative methodology, our findings reveal that the relationship between the capitals and enterprise performance are indeeed contingent with the capitals of home-grown entrepreneurs, rather than those of returnee migrant entrepreneurs, exhibiting a greater propensity to influence enterprise performance. We conclude with implications for theory and practice.

The concept of social capital has attracted much attention from researchers and policy makers, largely due to links with positive social outcomes and philanthropic acts such as volunteering and donations. However, a rapid growth in Internet technologies and social media networks has fundamentally affected the formation of social capital, as well as the way in which it potentially associates with prosocial behaviors. This study uses unique data from a survey of online volunteers to explore the interrelationships between social capital and a mix of self-reported and observed philanthropic activities in both online and offline settings. Our results show that while social capital levels associate strongly with offline donations, there are key differences in the relationships between social capital and volunteering in online and offline settings. Using two-stage least squares (2SLS) regression analysis to control for endogeneity, we also infer a number of causal relationships between social capital and philanthropy.