The Role of Interorganizational and Organizational Factors on the Decision Mode for Adoption of Interorganizational Systems*

The dramatic growth of interorganizational systems (IOS) has changed the way organizations conduct their business, and has resulted in significant tangible and intangible benefits being realized by participating firms. However, the implementation of these IOS requires the cooperation and commitment of all the participating members. These members may have complex economic and business relationships among themselves that can result in a number of social, political, and economic factors influencing the adoption and implementation of IOS. This study examines the role of interorganizational and organizational factors on the decision mode for adoption of IOS, in the specific context of electronic data interchange (EDI). Four interorganizational factors, based on the socio-political framework derived from research in marketing, and five organizational factors based on research in IS were used in the study. The data for the study were collected through a large scale field survey. Two respondents, the sales/purchase manager and the IS manager, from 201 firms responded to the survey. The results of discriminant analysis of the data reveal that two interorganizational variables, competitive pressure and exercised power, and two organizational variables, internal need and top management support, are important variables to differentiate firms with proactive decision mode from firms with reactive decision mode. The study also evaluates the differences between proactive and reactive firms on three implementation outcomes. Proactive firms are found to have greater extent of adaptation, more external connectivity with trading partners, and better integration of EDI information in their internal IS applications.     

Inventory Policy with Parametric Demand: Operational Statistics,Linear Correction,and Regression

In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

Amplitude modulated model for analyzing non-stationary speech signals

Recently amplitude modulated (AM) model in presence of additive white noise was used to analyze certain non-stationary speech data. It is observed that the assumption of white noise may not be proper in many cases. In this article, we consider the AM signal model in presence of stationary noise. We consider the least squares estimators and the estimators obtained by maximizing the Periodogram function. The two estimators are asymptotically equivalent. We study the theoretical properties of both estimators and observe their performances through numerical simulations. One speech data is analyzed and it is observed that the performance of the proposed estimators is quite satisfactory.     

Analysis of middle-censored data with exponential lifetime distributions

Recently Jammalamadaka and Mangalam [2003. Non-parametric estimation for middle censored data. J. Nonparametric Statist. 15, 253–265] introduced a general censoring scheme called the “middle-censoring” scheme in non-parametric set up. In this paper we consider this middle-censoring scheme when the lifetime distribution of the items is exponentially distributed and the censoring mechanism is independent and non-informative. In this set up, we derive the maximum likelihood estimator and study its consistency and asymptotic normality properties. We also derive the Bayes estimate of the exponential parameter under a gamma prior. Since a theoretical construction of the credible interval becomes quite difficult, we propose and implement Gibbs sampling technique to construct the credible intervals. Monte Carlo simulations are performed to evaluate the small sample behavior of the techniques proposed. A real data set is analyzed to illustrate the practical application of the proposed methods.     

A Dynamic Perspective on Diverse Teams: Moving from the Dual-Process Model to a Dynamic Coordination-based Model of Diverse Team Performance

The existing literature on diverse teams suggests that diversity is both helpful to teams in making more information available and encouraging creativity and damaging to teams in reducing cohesion and information sharing. Thus the extant literature suggests that diversity within teams is a double-edged sword that leads to both positive and negative effects simultaneously. This literature has not, however, fully embraced the increasing calls in the broader groups literature to take account of time in understanding how groups function [e.g. Cronin, M. A., Weingart, L. R., &; Todorova, G. (2011). Dynamics in groups: Are we there yet? The Academy of Management Annals, 5, 571–612]. We review the literature on diverse teams employing this lens to develop a dynamic perspective that takes account of the timing and flow of diversity's effects. Our review suggests that diversity in groups has different short-term and long-term effects in ways that are not fully captured by the currently dominant double-edged sword metaphor. We identify an emerging perspective that suggests a tropical depression metaphor—that has the potential, over time, to develop either into a dangerous hurricane or diffuse into a rainstorm that gives way to sunshine, as more apt to capture the dynamic effects of diversity in teams. We conclude by outlining an agenda for redirecting future research on diverse teams using this more dynamic perspective.     

Inventor knowledge recombination behaviors in a pharmaceutical merger: The role of intra-firm networks

We study firms' abilities to increase the generative appropriability of their knowledge by studying the knowledge recombination patterns of inventors in the context of a merger between two equally sized pharmaceutical firms. Specifically, we study inventors' choices to recombine knowledge originating in the firm with which they merge. We hypothesize that mergers focus inventors' attention to units of knowledge originating in the other firm and that therefore, inventors will choose to recombine more of this knowledge, which exists in their intra-firm network, following a merger. We also hypothesize that inventors vary in terms of their recombination choices following a merger. We explore these differences by linking inventors' network positions with their abilities and motivations to recombine knowledge originating in the other firm. Specifically, we hypothesize an inverted-U shaped relationship between centrality and knowledge recombination from the other firm and a linear relationship between brokerage and knowledge recombination from the other firm. We test our hypotheses using patent data from the merger between Bristol-Myers and Squibb and find support for our hypotheses. The paper contributes to knowledge recombination research by exploring changes in knowledge recombination dynamics following a merger and by understanding how mergers affect firms’ generative research trajectories. Practically, we suggest that managers should identify and nurture certain types of inventors following a merger to be able to better leverage the knowledge bases of merging firms.     

Developing human resource competencies: an empirical evidence

The paper conceptually defines and empirically investigates the density of work experience along with individual characteristics (cognitive ability, Conscientiousness, and Openness to Experience) and examine their influence on Human Resource (HR) competencies. 274 HR executives from diverse industry background and their supervisors participated in the data collection efforts spread over three phases. Data analyses revealed three key findings (i) executives’ density of work experience positively relates to HR competencies, (ii) executives’ cognitive ability is the strongest predictor of the positive relationship to HR competencies, and (iii) executives with high Conscientiousness tend to achieve levels of density of work experience. Relative weight analyses reinforce that cognitive ability and density of work experience are the most prominent predictors of HR competencies. Implications for practice are discussed.     

Organizational context, climate and innovativeness: adoption of imaging technology

Managers make decisions to adopt technological innovations within an organizational context. This research explores the role of organizational climate as it affects the impacts of organizational context on innovativeness. Context refers here to organizational size, slack resources, and organizational age. We analyze three known climate dimensions as moderator variables: risk orientation, external orientation, and achievement orientation. Data describe the adoption of medical imaging technologies by 70 hospitals. Climate measures come from several technology decision-makers within each organization. Technology measures of radicalness and relative advantage are ratings by five outside experts in the use of these 68 technologies. The study also includes the traditional measure that counts the overall number of innovations adopted. Innovativeness is a multi-dimensional composite variable composed of radicalness, relative advantage, and number of innovations adopted. As expected, results show that organizational size and slack are positively related with innovativeness. Hierarchical regression analyses indicate that the climate measures of risk orientation and external orientation interact significantly with the context dimensions of organizational size and organizational age. The model developed and tested in this project explains over 50% of the total variance in innovativeness.