Slack and the performance of state-owned enterprises

Organizational slack has been recognized as critical to firm performance, although its impact is not always positive. Slack may be used to fuel innovation or alternatively excess resources may be squandered on pet projects. However, most research on slack is rooted in studying private firms in developed economies, especially the United States. Whether prior research on organizational slack can readily inform our understanding of state-owned enterprises’ (SOEs) behavior is questionable since SOEs prioritize goals such as social welfare and full employment differently than do the privately owned enterprises (POEs). The differences between SOEs and POEs influence their sources and use of slack due to the nature of their ownership, budget constraints, and agency relations. To bring insight to this issue we develop an institutional change lifecycle model to study the relationship between slack and the economic and social aspects of SOE performance.     

Agglomeration and clustering over the industry life cycle: Toward a dynamic model of geographic concentration

Research on agglomeration finds that either a higher survival rate of incumbent firms or a higher founding rate of new entrants, or both, can sustain an industry cluster. The conditioning effects of time on the two distinct mechanisms of survival and founding are, however, rarely examined. We argue that the forces driving geographic concentration vary across the industry life cycle. Data from Ontario's winery industry from 1865 to 1974 demonstrates a dynamic model of geographic concentration: agglomeration attracts more new entry in the growth stage only, whereas it contributes to firm survival in the mature stage only. The results not only establish the importance of understanding the temporal dynamics underlying agglomeration externalities, but also provide a possible explanation for the mixed empirical results found in previous studies. Copyright © 2013 John Wiley & Sons, Ltd.     

Capitalism and evolution

In this paper I to ask an old fashioned question, “Why do capitalist economies evolve in the way that they do?” The answer will lie, in the nature of human curiosity and the corresponding growth of knowledge and in the particular instituted rules of the game that induce the self transformation of each particular economic order. The essential idea is this; the manner of self transforming is contingent on the manner of self-ordering, so that different instituting frames have different dynamic consequences. The notion of order provides the bridge to the systemic properties of the economy, the nature of its parts and the manner of their interconnection, while the notion of transformation provides the link with evolution and the open-ended, essentially unpredictable, development of capitalism. From my perspective capitalist economies are ignorance economies, in which highly specialised individuals and teams know a great deal about very little, so that the productive strength of the system, its collective knowing, depends on how the pools of specialised, narrow understandings are connected. Connectivity requires organisation and organisation depends on rules of the game and on belief and trust so that we can rely upon the testimony and actions of others. Failure of trust leads to failure of connectivity and a corresponding loss of system coherence. Order is central to the notion of economic evolution and, in practice, economic configurations demonstrate immense richness and subtlety but order is not equilibrium. Systems in equilibrium do not evolve. That the day to day structures of capitalism are the product of ordering processes in the epistemic as well as the material realm seems to me self evident and it is equally self evident that these structures are restless, that their development is open-ended and unpredictable.     

Strategies and language trends in learning success and failure of negotiation

In negotiation by electronic means, language is an important deal-making tool which helps realize negotiation strategies. Negotiators may use language to request information, exchange offers, persuade, threaten, as well as reach a compromise or find prospective partners. All this is recorded in texts exchanged by negotiators. We explore the language signals of strategies—argumentation, persuasion, negation, proposition. Leech and Svartvik’s approach to language in communication gives our study the necessary systematic background. It combines pragmatics, the communicative grammar and the meaning of English verbs. Language signals become features in the task of classifying those texts. We employ Statistical Natural Language Processing and Machine Learning techniques to find general trends that negotiation texts exhibit. Our hypothesis is that language signals help predict negotiation outcomes. We run experiments on the Inspire data. The electronic negotiation support system Inspire was gathering data for several years. The data include text messages which negotiators may exchange while trading offers. We conduct a series of Machine Learning experiments to predict the negotiation outcome from the texts associated with first halves of negotiations. We compare the results with the classification of complete negotiations. We conclude the paper with an analysis of the results and a list of suggestions for future work.     

Strengthening the satisfaction loyalty link: the role of relational switching costs

Marketing Letters - The extant retail research has placed much emphasis on understanding customer switching and the concept of switching costs (SCs). However, the empirical evidence is inconclusive...