Social Science

While the conventions for evaluating information systems case studies conducted according to the natural science model of social science are now widely accepted, this is not the case for interpretive field studies. A set of principles for the conduct and evaluation of interpretive field research in information systems is proposed, along with their philosophical rationale. The usefulness of the principles is illustrated by evaluating three published interpretive field studies drawn from the IS research literature. The intention of the paper is to further reflection and debate on the important subject of grounding interpretive research methodology.

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a large percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory . In our survey of such studies where such moderating variables are explored, a majority fail to either detect and/or provide an estimate of the effect size. In cases where effects sizes are estimated, the numbers are generally small. These results have, in turn, led some to question the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of such interaction effects by accounting for the measurement error in measures which attenuates the estimated relationships. The feasibility of this approach at recovering the true effects is demonstrated in two studies: a simulated data set where the underlying true effects are known and a Voice Mail adoption data set where the emotion of enjoyment is shown to have both a substantial direct and interaction effect on adoption intention.

Currently there are no brief, self-administered instruments for measuring the degree to which an adult with normal intelligence has the traits associated with the autistic spectrum. In this paper, we report on a new instrument to assess this: the Autism-Spectrum Quotient (AQ). Individuals score in the range 0-50. Four groups of subjects were assessed: Group 1: 58 adults with Asperger syndrome (AS) or high-functioning autism (HFA); Group 2: 174 randomly selected controls. Group 3: 840 students in Cambridge University; and Group 4: 16 winners of the UK Mathematics Olympiad. The adults with AS/HFA had a mean AQ score of 35.8 (SD = 6.5), significantly higher than Group 2 controls (M = 16.4, SD = 6.3). 80% of the adults with AS/HFA scored 32+, versus 2% of controls. Among the controls, men scored slightly but significantly higher than women. No women scored extremely highly (AQ score 34+) whereas 4% of men did so. Twice as many men (40%) as women (21%) scored at intermediate levels (AQ score 20+). Among the AS/HFA group, male and female scores did not differ significantly. The students in Cambridge University did not differ from the randomly selected control group, but scientists (including mathematicians) scored significantly higher than both humanities and social sciences students, confirming an earlier study that autistic conditions are associated with scientific skills. Within the sciences, mathematicians scored highest. This was replicated in Group 4, the Mathematics Olympiad winners scoring significantly higher than the male Cambridge humanities students. 6% of the student sample scored 32+ on the AQ. On interview, 11 out of 11 of these met three or more DSM-IV criteria for AS/HFA, and all were studying sciences/mathematics, and 7 of the 11 met threshold on these criteria. Test-retest and interrater reliability of the AQ was good. The AQ is thus a valuable instrument for rapidly quantifying where any given individual is situated on the continuum from autism to normality. Its potential for screening for autism spectrum conditions in adults of normal intelligence remains to be fully explored.

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors' own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.

We review the recent fast progress in statistical physics of evolving networks. Interest has focused mainly on the structural properties of random complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of such a kind came into existence recently. This opens a wide field for the study of their topology, evolution, and complex processes occurring in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of large sizes of these networks, the distances between most their vertices are short -a feature known as the "smallworld" effect. We discuss how growing networks self-organize into scale-free structures and the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation in these networks. We present a number of models demonstrating the main features of evolving networks and discuss current approaches for their simulation and analytical study. Applications of the general results to particular networks in Nature are discussed. We demonstrate the generic connections of the network growth processes with the general problems of non-equilibrium physics, econophysics, evolutionary biology, etc.

Many empirical questions in economics and other social sciences depend on causal effects of programs or policiy interventions. In the last two decades much research has been done on the econometric and statistical analysis of the effects of such programs or treatments. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization and other areas of empirical micro-economics. In this review we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.

Major health care problems such as patient dissatisfaction, inequity of access to care, and spiraling costs no longer seem amenable to traditional biomedical solutions. Concepts derived from anthropologic and cross-cultural research may provide an alternative framework for identifying issues that require resolution. A limited set of such concepts is described and illustrated, including a fundamental distinction between disease and illness, and the notion of the cultural construction of clinical reality. These social science concepts can be developed into clinical strategies with direct application in practice and teaching. One such strategy is outlined as an example of a clinical social science capable of translating concepts from cultural anthropology into clinical language for practical application. The implementation of this approach in medical teaching and practice requires more support, both curricular and financial.

Building on an idea in Abadie and Gardeazabal (2003), this article investigates the application of synthetic control methods to comparative case studies. We discuss the advantages of these methods and apply them to study the effects of Proposition 99, a large-scale tobacco control program that California implemented in 1988. We demonstrate that following Proposition 99 tobacco consumption fell markedly in California relative to a comparable synthetic control region. We estimate that by the year 2000 annual per-capita cigarette sales in California were about 26 packs lower than what they would have been in the absence of Proposition 99. Given that many policy interventions and events of interest in social sciences take place at an aggregate level (countries, regions, cities, etc.) and affect a small number of aggregate units, the potential applicability of synthetic control methods to comparative case studies is very large, especially in situations where traditional regression methods are not appropriate. The methods proposed in this article produce informative inference regardless of the number of available comparison units, the number of available time periods, and whether the data are individual (micro) or aggregate (macro).

Communication can be seen as the main ingredient in medical care. In reviewing doctor-patient communication, the following topics are addressed: (1) different purposes of medical communication; (2) analysis of doctor-patient communication; (3) specific communicative behaviors; (4) the influence of communicative behaviors on patient outcomes; and (5) concluding remarks.Three different purposes of communication are identified, namely: (a) creating a good inter-personal relationship; (b) exchanging information; and (c) making treatment-related decisions. Communication during medical encounters can be analyzed by using different interaction analysis systems (IAS). These systems differ with regard to their clinical relevance, observational strategy, reliability/validity and channels of communicative behavior. Several communicative behaviors that occur in consultations are discussed: instrumental (cure oriented) vs affective (care oriented) behavior, verbal vs non-verbal behavior, privacy behavior, high vs low controlling behavior, and medical vs everyday language vocabularies. Consequences of specific physician behaviors on certain patient outcomes, namely: satisfaction, compliance/adherence to treatment, recall and understanding of information, and health status/psychiatric morbidity are described. Finally, a framework relating background, process and outcome variables is presented.

This paper presents an overview of GeoDa TM , a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for non-GIS specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis and criminology.

Increasing emphasis has been placed on the use of effect size reporting in the analysis of social science data. Nonetheless, the use of effect size reporting remains inconsistent, and interpretation of effect size estimates continues to be confused. Researchers are presented with numerous effect sizes estimate options, not all of which are appropriate for every research question. Clinicians also may have little guidance in the interpretation of effect sizes relevant for clinical practice. The current article provides a primer of effect size estimates for the social sciences. Common effect sizes estimates, their use, and interpretations are presented as a guide for researchers.

Some mass communications scholars have contended that uses and gratifications is not a rigorous social science theory. In this article, I argue just the opposite, and any attempt to speculate on the future direction of mass communication theory must seriously include the uses and gratifications approach. In this article, I assert that the emergence of computer-mediated communication has revived the significance of uses and gratifications. In fact, uses and gratifications has always provided a cutting-edge theoretical approach in the initial stages of each new mass communications medium: newspapers, radio and television, and now the Internet. Although scientists are likely to continue using traditional tools and typologies to answer questions about media use, we must also be prepared to expand our current theoretical models of uses and gratifications. Contemporary and future models must include concepts such as interactivity, demassification, hypertextuality, and asynchroneity. Researchers must also be willing to explore interpersonal and qualitative aspects of mediated communication in a more holistic methodology. What mass communication scholars today refer to as the uses and gratifications (U&G) approach is generally recognized to be a subtradition of media effects research (McQuail, 1994). Early in the history of communications research, an approach was developed to study the gratifications that attract and hold audiences to the kinds of media and the types of content that satisfy their social and psychological needs (Cantril, 1942). Much early effects research adopted the experimental or quasi-experimental approach, in which communication conditions were manipulated in search of general lessons about how better to communicate, or about the unintended consequences of messages (Klapper, 1960).

Twitter-a microblogging service that enables users to post messages ("tweets") of up to 140 characters-supports a variety of communicative practices; participants use Twitter to converse with individuals, groups, and the public at large, so when conversations emerge, they are often experienced by broader audiences than just the interlocutors. This paper examines the practice of retweeting as a way by which participants can be "in a conversation." While retweeting has become a convention inside Twitter, participants embrace it for diverse reasons and using varying styles. Our data and analysis reveal the messiness of retweeting by highlighting how issues of authorship, attribution, and communicative fidelity are negotiated in diverse ways. Using a series of case studies and empirical data, this paper maps out different conversational aspects of retweeting. This paper is currently in review. For citation information, contact danah. We are making it public for commentary/critique. All feedback welcome!!

Using comparative data from five countries, this study investigates the psychometric properties of the effort-reward imbalance (ERI) at work model. In this model, chronic work-related stress is identified as non-reciprocity or imbalance between high efforts spent and low rewards received. Health-adverse effects of this imbalance were documented in several prospective and cross-sectional investigations. The internal consistency, discriminant validity and factorial structure of 'effort', 'reward', and 'overcommitment' scales are evaluated, using confirmatory factor analysis. Moreover, content (or external) validity is explored with respect to a measure of self-reported health.

This article outlines a framework for the analysis of economic integration and its relation to the asymmetries of economic and social development. Consciously breaking with state-centric forms of social science, it argues for a research agenda that is more adequate to the exigencies and consequences of globalization than has traditionally been the case in 'development studies'. Drawing on earlier attempts to analyse the cross-border activities of rms, their spatial con gurations and developmental consequences, the article moves beyond these by proposing the framework of the 'global production network' (GPN). It explores the conceptual elements involved in this framework in some detail and then turns to sketch a stylized example of a GPN. The article concludes with a brief indication of the bene ts that could be delivered by research informed by GPN analysis.

This paper advances an``information goods'' theory that explains prestige processes as an emergent product of psychological adaptations that evolved to improve the quality of information acquired via cultural transmission. Natural selection favored social learners who could evaluate potential models and copy the most successful among them. In order to improve the fidelity and comprehensiveness of such ranked-biased copying, social learners further evolved dispositions to sycophantically ingratiate themselves with their chosen models, so as to gain close proximity to, and prolonged interaction with, these models. Once common, these dispositions created, at the group level, distributions of deference that new entrants may adaptively exploit to decide who to begin copying. This generated a preference for models who seem generally``popular.'' Building on social exchange theories, we argue that a wider range of phenomena associated with prestige processes can more plausibly be explained by this simple theory than by others, and we test its predictions with data from throughout the social sciences. In addition, we distinguish carefully between dominance (force or force threat) and prestige (freely conferred deference). D

| Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.

A major consideration we had in writing this survey was to make it accessible to mathematicians as well as to computer scientists, since expander graphs, the protagonists of our story, come up in numerous and often surprising contexts in both fields. But, perhaps, we should start with a few words about graphs in general. They are, of course, one of the prime objects of study in Discrete Mathematics. However, graphs are among the most ubiquitous models of both natural and human-made structures. In the natural and social sciences they model relations among species, societies, companies, etc. In computer science, they represent networks of communication, data organization, computational devices as well as the flow of computation, and more. In mathematics, Cayley graphs are useful in Group Theory. Graphs carry a natural metric and are therefore useful in Geometry, and though they are "just" one-dimensional complexes, they are useful in certain parts of Topology, e.g. Knot Theory. In statistical physics, graphs can represent local connections between interacting parts of a system, as well as the dynamics of a physical process on such systems. The study of these models calls, then, for the comprehension of the significant structural properties of the relevant graphs. But are there nontrivial structural properties which are universally important? Expansion of a graph requires that it is simultaneously sparse and highly connected. Expander graphs were first defined by Bassalygo and Pinsker, and their existence first proved by Pinsker in the early '70s. The property of being an expander seems significant in many of these mathematical, computational and physical contexts. It is not surprising that expanders are useful in the design and analysis of communication networks. What is less obvious is that expanders have surprising utility in other computational settings such as in the theory of error correcting codes and the theory of pseudorandomness. In mathematics, we will encounter e.g. their role in the study of metric embeddings, and in particular in work around the Baum-Connes Conjecture. Expansion is closely related to the convergence rates of Markov Chains, and so they play a key role in the study of Monte-Carlo algorithms in statistical mechanics and in a host of practical computational applications. The list of such interesting and fruitful connections goes on and on with so many applications we will not even

s Abstract Ecological studies of terrestrial urban systems have been approached along several kinds of contrasts: ecology in as opposed to ecology of cities; biogeochemical compared to organismal perspectives, land use planning versus biological, and disciplinary versus interdisciplinary. In order to point out how urban ecological studies are poised for significant integration, we review key aspects of these disparate literatures. We emphasize an open definition of urban systems that accounts for the exchanges of material and influence between cities and surrounding landscapes. Research on ecology in urban systems highlights the nature of the physical environment, including urban climate, hydrology, and soils. Biotic research has studied flora, fauna, and vegetation, including trophic effects of wildlife and pets. Unexpected interactions among soil chemistry, leaf litter quality, and exotic invertebrates exemplify the novel kinds of interactions that can occur in urban systems. Vegetation and faunal responses suggest that the configuration of spatial heterogeneity is especially important in urban systems. This insight parallels the concern in the literature on the ecological dimensions of land use planning. The contrasting approach of ecology of cities has used a strategy of biogeochemical budgets, ecological footprints, and summaries of citywide species richness. Contemporary ecosystem approaches have begun to integrate organismal, * The U.S. Government has the right to retain a nonexclusive, royalty-free license in and to any copyright covering this paper.

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.

a r t i c l e i n f o a b s t r a c t Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneitywhich includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement errorrenders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

This paper provides a framework for developing sampling designs in mixed methods research. First, we present sampling schemes that have been associated with quantitative and qualitative research. Second, we discuss sample size considerations and provide sample size recommendations for each of the major research designs for quantitative and qualitative approaches. Third, we provide a sampling design typology and we demonstrate how sampling designs can be classified according to time orientation of the components and relationship of the qualitative and quantitative sample. Fourth, we present four major crises to mixed methods research and indicate how each crisis may be used to guide sampling design considerations. Finally, we emphasize how sampling design impacts the extent to which researchers can generalize their findings.

To help inform drug abuse prevention research in school settings about the issues surrounding implementation, we conducted a review of the fidelity of implementation research literature spanning a 25-year period. Fidelity has been measured in five ways: (1) adherence, (2) dose, (3) quality of program delivery, (4) participant responsiveness and (5) program differentiation. Definitions and measures of fidelity were found not to be consistent across studies, and new definitions are proposed. While there has been limited research on fidelity of implementation in the social sciences, research in drug abuse prevention provides evidence that poor implementation is likely to result in a loss of program effectiveness. Studies indicate that most teachers do not cover everything in a curriculum, they are likely to teach less over time and training alone is not sufficient to ensure fidelity of implementation. Key elements of high fidelity include teacher training, program characteristics, teacher characteristics and organizational characteristics. The review concludes with a discussion of the tension between fidelity and reinvention/adaptation, and ways of resolving this tension. Recommendations are made for developing a consistent methodology for measuring and analyzing fidelity of implementation. Further, researchers and providers should col

Disasters are large intractable problems that test the ability of communities and nations to effectively protect their populations and infrastructure, to reduce both human and property loss, and to rapidly recover. The seeming randomness of impacts and problems and uniqueness of incidents demand dynamic, real-time, effective and cost efficient solutions, thus making the topic very suitable for OR/MS research. While social sciences and humanities literatures enjoy an abundance of articles on disaster management, the OR/MS community is yet to produce a critical mass. In this paper, we survey the literature to identify potential research directions in disaster operations, discuss relevant issues, and provide a starting point for interested researchers.

Socioeconomic inequalities in adolescent health have been little studied until recently, partly due to the lack of appropriate and agreed upon measures for this age group. The difficulties of measuring adolescent socioeconomic status (SES) are both conceptual and methodological. Conceptually, it is unclear whether parental SES should be used as a proxy, and if so, which aspect of SES is most relevant. Methodologically, parental SES information is difficult to obtain from adolescents resulting in high levels of missing data. These issues led to the development of a new measure, the Family Affluence Scale (FAS), in the context of an international study on adolescent health, the Health Behaviour in School-Aged Children (HBSC) Study. The paper reviews the evolution of the measure over the past 10 years and its utility in examining and explaining health related inequalities at national and cross-national levels in over 30 countries in Europe and North America. We present an overview of HBSC papers published to date that examine FAS-related socioeconomic inequalities in health and health behaviour, using data from the HBSC study. Findings suggest consistent inequalities in self-reported health, psychosomatic symptoms, physical activity and aspects of eating habits at both the individual and country level. FAS has recently been adopted, and in some cases adapted, by other research and policy related studies and this work is also reviewed. Finally, ongoing FAS validation work is described together with ideas for future development of the measure.

Almost 30 years ago, Hamachek (1978) suggested that two forms of perfectionism be distinguished, a positive form labeled „normal perfectionism” and a negative form labeled „neurotic perfectionism.” Focusing on the positive, we present an overview of the different empirical conceptions of the two forms of perfectionism and present a common framework for the two basic approaches: the dimensional approach differentiating two dimensions of perfectionism (perfectionistic strivings and perfectionistic concerns) and the group-based approach differentiating two groups of perfectionists (healthy perfectionists and unhealthy perfectionists). Moreover, we review the evidence demonstrating that (a) perfectionistic strivings are associated with positive characteristics and (b) healthy perfectionists show higher levels of positive characteristics compared to unhealthy perfectionists and nonperfectionists. While questions on core facets, positive effects, and developmental antecedents of positive forms of perfectionism remain, our findings suggest that self-oriented perfectionistic strivings are positive, if perfectionists are not overly concerned about mistakes and negative evaluations by others.

While the importance of being reflexive is acknowledged within social science research, the difficulties, practicalities and methods of doing it are rarely addressed. Thus, the implications of current theoretical and philosophical discussions about reflexivity, epistemology and the construction of knowledge for empirical sociological research practice, specifically the analysis of qualitative data, remain underdeveloped. Drawing on our doctoral experiences, we reflect on the possibilities and limits of reflexivity during the interpretive stages of research.We explore how reflexivity can be operationalized and discuss reflexivity in terms of the personal, interpersonal, institutional, pragmatic, emotional, theoretical, epistemological and ontological influences on our research and data analysis processes. We argue that data analysis methods are not just neutral techniques.They reflect, and are imbued with, theoretical, epistemological and ontological assumptions -including conceptions of subjects and subjectivities, and understandings of how knowledge is constructed and produced. In suggesting how epistemological and ontological positionings can be translated into research practice, our article contributes to current debates aiming to bridge the gap between abstract epistemological discussions and the nitty-gritty of research practice.

This paper is concerned with the power of social science and its methods. We first argue that social inquiry and its methods are productive: they (help to) make social realities and social worlds. They do not simply describe the world as it is, but also enact it. Second, we suggest that, if social investigation makes worlds, then it can, in some measure, think about the worlds it wants to help to make. It gets involved in 'ontological politics'. We then go on to show that its methodsand its politics -are still stuck in, and tend to reproduce, nineteenth-century, nation-statebased politics.

Natural resources management in general, and water resources management in particular, are currently undergoing a major paradigm shift. Management practices have largely been developed and implemented by experts using technical means based on designing systems that can be predicted and controlled. In recent years, stakeholder involvement has gained increasing importance. Collaborative governance is considered to be more appropriate for integrated and adaptive management regimes needed to cope with the complexity of social-ecological systems. The paper presents a concept for social learning and collaborative governance developed in the European project HarmoniCOP (Harmonizing COllaborative Planning). The concept is rooted in the more interpretive strands of the social sciences emphasizing the context dependence of knowledge. The role of frames and boundary management in processes of learning at different levels and time scales is investigated. The foundation of social learning as investigated in the HarmoniCOP project is multiparty collaboration processes that are perceived to be the nuclei of learning processes. Such processes take place in networks or "communities of practice" and are influenced by the governance structure in which they are embedded. Requirements for social learning include institutional settings that guarantee some degree of stability and certainty without being rigid and inflexible. Our analyses, which are based on conceptual considerations and empirical insights, suggest that the development of such institutional settings involves continued processes of social learning. In these processes, stakeholders at different scales are connected in flexible networks that allow them to develop the capacity and trust they need to collaborate in a wide range of formal and informal relationships ranging from formal legal structures and contracts to informal, voluntary agreements.

. The use of field-based empirical methodologies in the production and operations management POM area has been steadily increasing over the past several years. One of the most prominent among these is the survey research methodology which has often been used to capture data from business organizations. However, to effectively contribute to theory development in the POM field, this methodology must be carefully implemented. Poorly designed and executed survey research is of little or no value. This paper attempts to provide a normative perspective on 'good survey research practices'. In doing so, it attempts to bridge the gap between survey research and theory development. The paper liberally draws from work in other social science disciplines like psychology, marketing, organizational behavior, and other related fields that are more mature in the use of surveys, and applies it to the POM area. A set of ideal survey research attributes are identified, and existing POM survey work in four prestigious academic journals is evaluated with respect to these attributes. Results of assessing 25 survey-based articles published in these journals between 1990 and 1995 indicate that both exploratory as well as explanatory survey research is being conducted, with current emphasis being more on explanatory kind of research. In order to move the field forward, greater attention will need to be focused on employing multi-item constructs, assessing them for content validity, and purifying them through field-based pretesting. More sophisticated POM studies in the future would have to aim for testing theory with reliable and valid scales that are relatively free of measurement related errors. It is hoped that this work will act as a catalyst in compressing the learning curve with respect to survey research practices in POM, and also accelerate the use of greater methodological rigor in future empirical studies in the field. q 1998 Elsevier Science B.V. All rights reserved.