International Business Research: Understanding Past Paths to Design Future Research Directions

In this paper we examine the extant research in IB by conducting a bibliometric study of the articles published in three leading international business journals – International Business Review, Journal of International Business Studies and Management International Review, over their entire track record of publication available in the ISI – Institute for Scientific Information. In longitudinal analyses of citation data we ascertain the most relevant works to the international business field. We also identify intellectual interconnectedness in co-citation networks of the research published in each journal. A second-tier analysis delves into publication patterns of those articles that are not at the top citation listings. Our results permit us better understand and depict the extant international business research and, to some extent, its evolution thus far.info:eu-repo/semantics/publishedVersio

Expanding research on corporate corruption, management and organizations

In this special issue introduction, we briefly describe a variety of research paths researchers have followed to study the multifaceted phenomenon of corruption. Furthermore, we classify the papers included in this special issue according to their contribution to these research paths and briefly preview them. Finally, drawing on these four research paths and the papers included in this special issue, we propose a six-item agenda for future research on corruption

Analysis of Economic Motives in the Individual Choice of Educational Paths

The authors consider the economic motivations when individuals choose an educational path. This line of research is relevant from both, the point of view of science — research of economic behavior of an individual, and the point of view of practice — allows to increase efficiency of investments in a human capital. The authors have developed the economic and mathematical model of choice of optimum educational paths by individuals. The model is realized in the software and approved on real data on more than 5,5 thousand students. For the analysis of the importance of rational economic expectations when an educational path has to be chosen, the paths chosen by students is compared and the educational paths optimum from the point of view of economic rationality are calculated. The analysis of the results has showed that mainly, the choice of educational paths happens according to the economic motivations. On the considered selection, 66 % of prospective students have chosen an optimum path from the point of view of economic preferences. The most significant factor providing development of optimum educational paths is an expectation of higher income upon completion of education — 22 % of all educational paths, and a possibility of cost-cutting of educating or state-subsidized education — 12 %. In our opinion, one of the most important practical results of the research of optimum educational path is the need to consider expectations of students and prospective student when developing a state policy of investment in human capital

Methods for Analyzing Pathways through a Physics Major

Physics Education Research frequently investigates what students studying physics do on small time scales (e.g. single courses, observations within single courses), or post-education time scales (e.g., what jobs do physics majors get?) but there is little research into how students get from the beginning to the end of a physics degree. Our work attempts to visualize students paths through the physics major, and quantitatively describe the students who take physics courses, receive physics degrees, and change degree paths into and out of the physics program at Michigan State University.Comment: submitted to Physics Education Research Conference Proceedings 201

Generating feasible transition paths for testing from an extended finite state machine (EFSM)

The problem of testing from an extended finite state machine (EFSM) can be expressed in terms of finding suitable paths through the EFSM and then deriving test data to follow the paths. A chosen path may be infeasible and so it is desirable to have methods that can direct the search for appropriate paths through the EFSM towards those that are likely to be feasible. However, generating feasible transition paths (FTPs) for model based testing is a challenging task and is an open research problem. This paper introduces a novel fitness metric that analyzes data flow dependence among the actions and conditions of the transitions in order to estimate the feasibility of a transition path. The proposed fitness metric is evaluated by being used in a genetic algorithm to guide the search for FTPs

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character's psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model's performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge

“I Identify with Her,” “I Identify with Him”: Unpacking the Dynamics of Personal Identification in Organizations

Despite recognizing the importance of personal identification in organizations, researchers have rarely explored its dynamics. We define personal identification as perceived oneness with another individual, where one defines oneself in terms of the other. While many scholars have found that personal identification is associated with helpful effects, others have found it harmful. To resolve this contradiction, we distinguish between three paths to personal identification—threat-focused, opportunity-focused, and closeness-focused paths—and articulate a model that includes each. We examine the contextual features, how individuals’ identities are constructed, and the likely outcomes that follow in the three paths. We conclude with a discussion of how the threat-, opportunity-, and closeness-focused personal identification processes potentially blend, as well as implications for future research and practice

Real Business Cycle Realizations

Much recent business cycle research focuses on moments of macroeconomic aggregates. We construct examples of real business cycle sample paths for output, consumption, and employment for the U.S. economy. Annual sample paths are generated from an initial condition in 1925, measured technology and government spending shocks since then, and a standard, calibrated, one-sector model of the business cycle. Quarterly sample paths are generated similarly, from an initial condition in 1955. The law of motion for shocks is not parametrized and so decision-rules are estimated by GMM. We compare the paths with actual history graphically and by spectral methods.real business cycles, Solow residuals, US business cycle history

On Leveraging Partial Paths in Partially-Connected Networks

Mobile wireless network research focuses on scenarios at the extremes of the network connectivity continuum where the probability of all nodes being connected is either close to unity, assuming connected paths between all nodes (mobile ad hoc networks), or it is close to zero, assuming no multi-hop paths exist at all (delay-tolerant networks). In this paper, we argue that a sizable fraction of networks lies between these extremes and is characterized by the existence of partial paths, i.e. multi-hop path segments that allow forwarding data closer to the destination even when no end-to-end path is available. A fundamental issue in such networks is dealing with disruptions of end-to-end paths. Under a stochastic model, we compare the performance of the established end-to-end retransmission (ignoring partial paths), against a forwarding mechanism that leverages partial paths to forward data closer to the destination even during disruption periods. Perhaps surprisingly, the alternative mechanism is not necessarily superior. However, under a stochastic monotonicity condition between current v.s. future path length, which we demonstrate to hold in typical network models, we manage to prove superiority of the alternative mechanism in stochastic dominance terms. We believe that this study could serve as a foundation to design more efficient data transfer protocols for partially-connected networks, which could potentially help reducing the gap between applications that can be supported over disconnected networks and those requiring full connectivity.Comment: Extended version of paper appearing at IEEE INFOCOM 2009, April 20-25, Rio de Janeiro, Brazi