MUMFORD ET AL.ENVIRONMENTAL INFLUENCES

Environmental Influenceson Ethical Decision Making: Climate

and Environmental Predictorsof Research Integrity

Michael D. Mumford, Stephen T. Murphy, Shane Connelly,Jason H. Hill, Alison L. Antes, Ryan P. Brown,

and Lynn D. DevenportThe University of Oklahoma

It is commonly held that early career experiences influence ethical behavior. Oneway early career experiences might operate is to influence the decisions people makewhen presented with problems that raise ethical concerns. To test this proposition,102 first-year doctoral students were asked to complete a series of measures examin-ing ethical decision making along with a series of measures examining environmen-tal experiences and climate perceptions. Factoring of the environmental measureyielded five dimensions: professional leadership, poor coping, lack of rewards, lim-ited competitive pressure, and poor career direction. Factoring of the climate in-ventory yielded four dimensions: equity, interpersonal conflict, occupational en-gagement, and work commitment. When these dimensions were used to predictperformance on the ethical decision-making task, it was found that the environmentaldimensions were better predictors than the climate dimensions. The implications ofthese findings for research on ethical conduct are discussed.

Keywords: ethical decision making, career experiences, environment, climate, re-search integrity

Few events in the sciences attract as much attention as violations of expectationsfor the appropriate conduct of research. In recent years, a number of well-publi-cized cases involving fraud, plagiarism, and death of human participants (Bechtel& Pearson, 1985; Kochan & Budd, 1992; Marshall, 1996) have led to questions

ETHICS & BEHAVIOR, 17(4), 337–366Copyright © 2007, Lawrence Erlbaum Associates, Inc.

Correspondence should be addressed to Michael D. Mumford, Department of Psychology, Univer-sity of Oklahoma, Norman, OK 73019. E-mail: mumford@ou.edu

about the integrity of investigators and the principles they are following in theirwork. Although few would dispute the “problematic” nature of these events,Steneck (2004) argued that scientific integrity may be undermined by a number ofless dramatic but more pervasive behaviors, ranging from questionable data trim-ming to inappropriate allocation of authorship. In fact, these less dramatic forms ofunethical behavior appear to be relatively common (Martinson, Anderson, & deVries, 2005) and may represent a noteworthy threat to the integrity of the scientificenterprise.

Theapparent frequencywithwhich these lessdramaticbutmorepervasiveuneth-ical behaviors are observed brings to fore a new question. What is the source of thesebehaviors? Although there is reason to suspect that characteristics of an individual,such as fear and narcissism, influence ethical conduct (Mumford & Helton, 2000),there is also reason to suspect that a number of other phenomena, such as underesti-mation of normative expectations (Pimple, 2002), work demands (Reall, Bailey, &Stoll, 1998), and environmental press (Sims & Keon, 1999), also influence ethicalbehavior. With this point in mind, the intent of the study presented here is to examinethe influence of environmental experiences and climate perceptions on day-to-dayethical decision making by scientists during the early stages of their careers.

ETHICAL DECISION MAKING

A variety of criteria have been used to assess the propensity for ethical behavior byscientists. One approach that has been applied involves asking scientists to reportexposure to unethical conduct by colleagues (e.g., Newman, McCoy, & Rhodes,2000). Another approach has involved assessing ethical conduct with respect to vi-olations of government guidelines (e.g., Davis & Riske, 2001). Although studiesapplying these and other potential criterion measures have value, they leave open afundamental question. Exactly what decisions were made by the scientist that ledto the occurrence of unethical behavior?

Recognition of the importance of ethical decision making has led a number ofinvestigators to assess ethical conduct through measures explicitly intended to as-sess people’s decisions or attributes of these decisions (e.g., Rest, Narváez,Bebeau, & Thoma, 1999). The logic underlying these decision-making studies isquite straightforward. Essentially, it is argued that engagement in unethical con-duct depends on the choices people make when placed in situations where ethicalconcerns might be operating (Lovell, 2002; Miner & Petocz, 2003). The deci-sion-making tasks applied in these studies, accordingly, present realistic decisionsituations where the individual is asked to select a preferred response, or re-sponses, from a set of response options that vary with respect to the degree of ethi-cal behavior. Peoples’ choices are then used to provide an assessment of their ethi-cal orientation. Although the idealized and decontextualized nature of these

338 MUMFORD ET AL.

decisions represents a limitation on generality, application of the method has be-come widespread in studies of ethics (Barnett & Vaicys, 2000; Eastman, Eastman,& Tolson, 2001; Forte, 2004; Mayo & Marks, 1990; Paolillo & Vitell, 2002) in partbecause of risk minimization and the direct measure of decision making providedby these measures.

Although studies applying ethical decision-making measures have met withsome success, it is also clear that decision-making measures represent a do-main-specific strategy for assessing the propensity for ethical behavior. Accord-ingly, effective application of these measures depends on careful specification ofthe kind of decisions to be examined. Recently, Helton-Fauth et al. (2003) re-viewed the available literature bearing on ethical conduct in the sciences. This re-view led to the identification of four general, higher order dimensions reflectingdecisions that involve ethical considerations: data management, study conduct,professional practices, and business practices. Within each of these four broad ar-eas, 2 to 6 more specific dimensions were identified. A total of 17 specific dimen-sions examined behaviors such as data massaging, institutional review board prac-tices, exploitation of staff or collaborators, and conflicts of interest. When thesespecific dimensions were evaluated with respect to relevant codes of scientific con-duct (e.g., American Medical Association, American Institute for Biological Sci-ence, American Psychological Association), it was found that more than 80% ofthe relevant ethical behaviors could be accounted for by one or more of these di-mensions. As a result, there is a reason to suspect that these dimensions might pro-vide an appropriate framework for the development of ethical decision-makingmeasures (Motowidlo, Dunnette, & Carter, 1990).

CLIMATE

Although formulation of a taxonomy of ethical behavior provides a basis for thedevelopment of measures of ethical decision making, the broader question at hand,the environmental variables influencing these decisions, remains. Perhaps themost commonly applied strategy in the measurement of environmental influencesmay be found in studies of work and organizational climate. Climate measurespresent people with statements describing select characteristics of their work envi-ronment (James, James, & Ashe, 1990; Parker et al., 2003; Schneider & Reichers,1983). For example, climate questions have been developed that ask, “Do people inyour work group look forward to new assignments?” or “Will people who turn anassignment in late be punished?” In responding to these questions, people areasked to indicate the extent to which these events characterize their workplace.Thus, climate measures seek to assess people’s perceptions of, or beliefs about, se-lect aspects of their environment.

ENVIRONMENTAL INFLUENCES 339

A number of studies have sought to identify climate dimensions that might in-fluence ethical decision making (e.g., Agarwal & Malloy, 1999; Hartman, Yrle, &Galle, 1999; Victor & Cullen, 1988). In one recent study along these lines, Peter-son (2001) examined how ethical behavior was related to perceptions of egoism(e.g., self-interest, company profit, and efficiency), benevolence (e.g., friendship,team interest, and social responsibility), and principle (e.g., personal morality,rules, and law). He found that perceptions of an egotistic environment, for exam-ple, perceptions of self-interest, were positively related to self-reported unethicalbehaviors such as stealing, calling in sick, and expense account padding. Climateperceptions related to benevolence and principle, however, were found to be nega-tively related to self-reported unethical behaviors. Other studies by Forte (2004),Fritzsche (2000), and Sims and Keon (1999) have shown that climate perceptionsare also related to ethical decision making, especially among younger profession-als who use their perceptions of the normative social environment as guidelines foraction.

Although there is reason to suspect that climate perceptions are related to bothethical behavior and ethical decision making, the relationship between climate andethics may be quite complex. For example, climate variables correlated with ethi-cal behavior and ethical decision making are also related to other variables, such asorganizational commitment and job satisfaction (Barnett & Schubert, 2002; Koh &Boo, 2001; Schminke, Ambrose, & Neubaum, 2005). The effects of climate per-ceptions on behavior and decision making, moreover, may be moderated by a num-ber of organizational-level variables (Morris, Schindehutte, Walton, & Allen,2002) including organizational age (Neubaum, Mitchell, & Schminke, 2004),leadership (Sims & Brinkmann, 2002; Thomas, Schermerhorn, & Dienhart, 2004),and work group characteristics (VanSandt, 2003). Finally, certain aspects of cli-mate, such as perceived support and challenging missions, have been found to con-tribute to effective performance on the part of scientists (Ekvall & Ryhammer,1999; Hunter, Bedell, & Mumford, 2007). As a result, one could expect that certainaspects of climate would be positively related to ethical behavior and ethical deci-sion making.

ENVIRONMENTAL EXPERIENCES

Climate perceptions, of course, do not arise in a vacuum. Instead, climate percep-tions are held to develop as a function of people’s behavior and experiences in theirenvironment. In fact, the available evidence indicates that people’s experiences,especially early career experiences, may have a particularly strong effect on theirperceptions of the work environment (Muchinsky, 2004). These experiences,moreover, appear to have a rather complex set of effects on people’s developmentas scientists.

340 MUMFORD ET AL.

In one recent study along these lines, Mumford et al. (2005) content analyzedobituaries for 499 scientists working in the physical, life, health, and social sci-ences. This analysis revealed that exposure to select events such as mentoring(Zuckerman, 1977), intense educational experiences (Subotnik & Steiner, 1993),and laboratory quality (Dunbar, 1995) discriminated more successful from lesssuccessful scientists producing multiple correlations with ratings of creative per-formance in the .50s—even when relevant controls, such as years in the professionand age at time of death, were taken into account.

Not only does the available evidence indicate that environmental experiencesinfluence career achievement in the sciences, it seems likely that exposure to cer-tain experiences will also influence ethical behavior and ethical decision making.For example, Jasanoff (1993) conducted an “in-depth” qualitative analysis of priorcases of scientific misconduct. Analysis of these cases indicated that poor men-toring, production pressure, and commitment to a particular theoretical perspec-tive all appeared to play a role in multiple cases of misconduct. In another studyalong these lines, Goldberg and Greenberg (1994) asked 1,500 professionalsworking in the biological, health, and social sciences to indicate whether they hadobserved ethical breeches such as fabricating and plagiarism. They then askedthese professionals to indicate the presumed causes of the misconduct incidents.They found that competition, production pressure, and poor study design werecommonly held to be causes of these breeches. Other experiences that appear tiedto ethical behavior, and ethical decision making, include poor role modeling by su-pervisors (Gelman & Gibelman, 2002), disregard of standard laboratory proce-dures (Jansen & von Glinow, 1985), conflict with peer groups (Levenson, 1986),and ineffectual collegial exchange (Akre, Falkum, Hoftvedt, & Aasland, 1997).

The joint influence of climate and experiences on ethical conduct, however,broaches a final question. More specifically, how do climate perceptions and environ-mental experiences operate together in shaping ethical behavior and ethical decisionmaking by scientists? One model proposes that environmental experiences lead to cli-mate perceptions, with climate perceptions in turn leading to ethical decision making(Sims & Keon, 1999). In contrast to this model, however, one might argue that careerexperiences determine both climate perceptions and ethical decision making—al-though climate perceptions might still exert some unique effects on ethical deci-sions. In fact, this later model may be more likely to apply among younger scientistswho are less strongly embedded in a professional network (Zuckerman, 1977).

METHOD

Sample

The sample used to examine the relationship of ethical decision making to climateand environmental experiences consisted of 102 first-year doctoral students at-

ENVIRONMENTAL INFLUENCES 341

tending a large research university in the southwest. The 40 men and 60 women (2did not designate) who agreed to participate in this study were recruited no earlierthan 4 months after beginning work at the university and no longer than 9 months.Sample members were drawn from programs awarding doctoral degrees in the bio-logical (28%), health (28%), and social (44%) sciences where independent re-search work is required for award of a terminal degree. Of the sample members,64% were majority group members (European/Caucasian), 32% were minoritygroup members (Asian, Hispanic, African, Middle Eastern, or Native American),and 4% did not indicate. A typical sample member was 27 years old and had com-pleted 17 years of schooling prior to admission to the doctoral program. Although59% of the sample was employed in nonresearch positions (such as teachingassistantships), 41% was employed in full-time research roles. All sample mem-bers, however, were actively engaged in research at one of the university’s labora-tories.

General Procedures

The study presented here was conducted as part of a larger, federally funded initia-tive concerned with research integrity. The university provided names, departmentassignments, e-mail addresses, and telephone numbers for all doctoral students en-tering the university in 2005. This information provided the background neededfor a multiple-stage recruitment effort. In the first stage, flyers announcing that thestudy was being conducted and that $100.00 would be paid for participation wereplaced in the mailboxes of 1st-year doctoral students in relevant biological, health,and social science departments. In the next stage, phone calls were made to eachstudent, and up to three e-mails were sent recruiting the student to participate in thestudy. Overall, roughly 25% of those contacted agreed to participate.

In both telephone calls and e-mails it was noted that the study was concernedwith research integrity. More specifically, the study was described as an investiga-tion of social effects on the students’ educational experiences with integrity andproblem solving. If a student agreed to participate in the study, he or she was askedto select a time and location where a battery of paper-and-pencil measures could becompleted. At the time a student arrived to complete this battery of measures, he orshe was again informed as to the nature of the study and asked to read and sign aninformed consent form.

After students had completed the informed consent document they were askedto begin working through the measures. Initially, students participating in thisstudy were asked to complete a background information form and a career experi-ences inventory in which they were asked a series of multiple-choice questions toindicate the nature of their experiences to date at the university. They were thenasked to complete a climate inventory describing the laboratory in which they werecurrently conducting their research work. After completing the climate inventory,

342 MUMFORD ET AL.

participants completed two measures examining ethical decision making in thesciences. Next, they were asked to complete a battery of standard psychologicaltests examining cognitive ability and dispositional characteristics. In the final seg-ment of this test session, participants completed an ethical decision making mea-sure tailored to issues they might encounter in their day-to-day work. It is of notethat these field-specific ethical decision-making measures were administered lastas work-oriented, problem-solving measures to minimize potential demand char-acteristics. Participants were given 4 hr to complete the battery of measures. Pilotstudies indicated that 4 hr was in fact sufficient time to complete all measures in-cluded in the battery without placing undue time pressure on participants.

Ethical Decision Making

The ethical decision-making measure participants completed provided the primarycriterion variables in this study. This decision-making measure was developed toreflect ethical issues participants might encounter in their day-to-day work using avariation of the low-fidelity simulation approach recommended by Motowidlo etal. (1990). This measure, although tailored to work in biological, health, and socialscience fields, provided scores on the data management, study conduct, profes-sional practices, and business practices dimensions identified by Helton-Fauth etal. (2003).

Development of this ethical decision-making measure, accordingly, began withHelton-Fauth et al.’s (2003) review of ethical codes of conduct in the biological,health, and social sciences. The review led to the identification of 17 constructssubsumed under the areas of data management, study conduct, professional prac-tices, and business practices:

• Data management: data massaging and publication practices• Study conduct: institutional review board, informed consent, confidentiality

protection, protection of human participants, protection of animal subjects• Professional practices: objectivity in evaluating work, recognition of exper-

tise, awareness of professional commitment, protection of intellectual prop-erty, protection of public welfare and the environment, exploitation of staff orcollaborators

• Business practices: conflict of interest, deceptive bid and contract practices,inappropriate use of physical resources, and inappropriate management prac-tices

Evidence for the validity of the taxonomy in accounting for ethical principles artic-ulated across various scientific fields has been provided by Helton-Fauth et al.(2003).

ENVIRONMENTAL INFLUENCES 343

After identifying these dimensions, online Web sites applying to the biological,health, and social sciences (e.g., On-line Ethics Center for Engineering andScience, Office of Research Integrity, American Biological Association) werereviewed to identify scenarios, or case studies, that might be used to assess ethicaldecision making with respect to one or more of these dimensions. Overall, in eacharea of biological, health, and social sciences, 45 cases were identified that mightbe used as a basis for development of the decision-making measures. The par-ticular cases selected for use in developing the scenarios to be applied in a givenfield were those that were relevant to day-to-day work, involved both technicaland ethical decisions, and would be understandable and potentially challenging toboth 1st-year doctoral students and professionals currently working in the field. Apanel of three psychologists and a subject-matter expert in the biological, health,and social sciences then selected a subset of 10 to 15 cases in each professionalarea that covered a range of different events. After these cases had been selected,they were rewritten into a short general scenario, typically one to two paragraphsin length.

Once the scenarios had been formulated, the decision-making aspect of themeasures was developed. Based on the general nature of the scenario, the panel ofpsychologists and the relevant subject-matter expert was asked to generate a list of8 to 12 events that might occur in the general situation presented in the scenario.Half of these events were to have only technical implications, and the remaininghalf were to have implications for 1 of the 17 dimensions of ethical conduct identi-fied by Helton-Fauth et al. (2003). The joint presentation of technical and ethicalevents was intended to minimize demand characteristics. Panel members werethen asked to review the list of events and for each scenario to select 2 ethicalevents and 2 technical events for inclusion in the final measure. The following cri-teria guided the selection of these events: The events must be likely to occur in thescenario, and the events could be organized into a general sequence of events flow-ing from the scenario.

After the sequence of event statements had been generated by panel members,the panel was asked to generate potential actions that might be taken in response toan event. All response options generated for an ethical decision-making event wereto be based on the particular dimension of ethical behavior under consideration.The response options generated were required to reflect actions that might resolvethe problem, or issues, implicit in the event. Response options, however, were todiffer with respect to ethical implications. Hence, for each event, 6 to 12 responseoptions were to be generated in which one third of the options reflected unethicalbehavior, one third questionable behavior, and one third ethical behavior. The re-sponse options, and scoring of these response options, were reviewed by a panel ofthree senior psychologists, all involved with this study and familiar with the litera-ture on ethical conduct, who suggested any necessary revisions with regard to re-sponse options and scoring.

344 MUMFORD ET AL.

On average, three events were formulated for each of the 17 ethical dimensionsapplying in the three fields under consideration. In responding to these events, par-ticipants were asked to read through the scenario. After they had read the scenario,participants were to assume the role of the primary actor in each event following ascenario. They were to select the two response options that they believed mostlikely would resolve the problem raised by the event. In scoring participants’ re-sponses, response options reflecting high, moderate, and low ethical behavior re-ceived scores of 3, 2, and 1, respectively. The average score of all events subsumedunder a dimension provided the basis for dimensional scoring. Dimensional scoreswere then aggregated to obtain overall ethical decision-making scores with respectto data management, study conduct, professional practices, and business practices.Table 1 provides an illustration of these ethical decision-making measures.

When dimensional scores were aggregated into the general dimensions of datamanagement, study conduct, professional practices, and business practices, the re-sulting average split-half reliability estimate, an estimate used because the ethicaldecision-making measures represent a form of a cognitive test, was .74 in our sam-ple. Some initial evidence bearing on the validity of these scales was provided byexamination of their convergent and divergent validity within this sample. It wasfound that data management was positively related to professional practices (r =.57) but less strongly related to study conduct (r = .22) and business practices (r =.18). Study conduct, however, was found to be more strongly related to businesspractices (r = .53) but less strongly related to data management (r = .22) and pro-fessional practices (r = .35). Some further evidence bearing on the validity of thesemeasures was obtained through their correlations with the ability and personalitymeasures. Here it was found that scores on all four dimensions were not stronglyrelated to scales examining intelligence (r = .19), divergent thinking (r = .18), andsocial desirability (r = –.03). Stronger relationships, however, were obtained with ameasure of cynicism (r = –.26).

Climate

Development of the climate inventory began with a review of prior studies examin-ing either ethical climate (e.g., Argandona, 1999; Barnett & Schubert, 2002;Hartman et al., 1999; Victor & Cullen, 1988; Viader-Cohen, 1998) or aspects ofclimate related to performance in research and development organizations (e.g.,Amabile, Conti, Coon, Lazerby, & Herron, 1996; Ekvall & Ryhammer, 1999;West, 2002; Witt & Beorkrem, 1999). Based on the literature review, 14 dimen-sions were identified that might be related to ethical behavior and ethical decisionmaking in scientific organizations: procedural justice, distributive justice, socialcontext, individual caring, law and code, trust, freedom, lack of conflict, debate,risk taking, humor, idea time, idea support, and challenge. Prior studies were then

ENVIRONMENTAL INFLUENCES 345

used to formulate operational definitions for each of these dimensions appropriatefor research organizations.

These operational definitions were then presented to a panel of four doctoralstudents, investigators on this project, in industrial and organizational psychology.Panel members were asked to apply the operational definition developed for eachclimate variable to generate 5 to 10 questions that might be used to measure this di-mension. In generating these questions panel members were told that viable cli-mate questions must capture perceptions of behavior in the workplace; reflect

346 MUMFORD ET AL.

TABLE 1Example of Ethical Decision-Making Questions

Moss is a researcher in the laboratory of Dr. Abrams, a well-known researcher in the field ofeconomics. Moss is trying to develop a model to predict performance of stocks in the technologysector, but she is having difficulty analyzing and selecting trends to include in the model. Sheenlists the help of Reynolds, another experienced researcher working on a similar topic. WithReynolds’s help, Moss eventually analyzes and identifies some key trends working them into atestable model. She also discusses some of her other research ideas with Reynolds. Two weekslater, Moss comes across a grant proposal developed by Reynolds and Abrams. She sees that itincludes ideas very similar to those she discussed with Reynolds. She takes the matter to Abrams,who declines to get involved, saying that the two researchers should work it out on their own.1. Reynolds admits to Abrams that he used slightly modified versions of Moss’s ideas. Abrams is

upset with this, but Reynolds is a key person on the proposal team and the grant applicationdeadline is soon. What should Abrams do? Choose two of the following:a. Fire Reynolds from the lab on the grounds of academic misconductb. Leave Reynolds as first author on the proposal since he wrote up the ideasc. Remove Reynolds from the proposal team, and offer Moss the position if she allows her ideas

to be usedd. Ask Moss to join the grant team, placing her as third author on the proposal if she allows her

ideas to be usede. Acknowledge Moss in the grant proposal because the ideas were hers originallyf. Apologize to Moss and indicate that the proposal must go out as is to meet the deadlineg. Remove Moss’s ideas from the proposal and try to rework it before the deadline

2. Moss is upset about Reynolds using her ideas and she decides to do something about it. Giventhat Moss works very closely with Reynolds and their boss Abrams, evaluate the likely successof the following plans of actions Moss can take. Choose two of the following:a. Moss asks Reynolds to give her credit by putting her name on the grant proposal as wellb. Moss asks Reynolds about the incident and tape records his reaction to later show Abramsc. Moss searches for annotated notes about her ideas that are dated prior to her conversation

with Reynoldsd. Moss appeals for a “mock trial” for Reynolds to testify under oath to his superiors that the in-

formation was hise. Moss searches for Reynolds’ lack of understanding of the concepts he claims were his own

by questioning him in front of other studentsf. Moss attempts to sway other researchers to support her to Abramsg. Moss visits Reynolds’ office in hopes of finding evidence that she contributed to the proposalh. Moss asks Reynolds to write an account of their conversation on the day in question and

shows her comparison account to him as evidence that he is using her ideas

norms, expectations, or typical patterns of social interactions; and reflect behav-iors or events that might be observed by doctoral students working in research lab-oratories. These items were framed as statements to which respondents would in-dicate their level of agreement, such as “People here do not steal other’s ideas” and“Meeting social responsibilities is seen as a critical goal.” Prior to starting work ongenerating these statements, panel members received 20 hr of instruction from thesenior psychologists involved in the project concerning the procedures to be ap-plied in generating climate questions.

The questions generated by panel members were reviewed by three senior psy-chologists, all of whom were familiar with the climate literature. Questions gener-ated to assess a given climate dimension were reviewed for construct coverage,concreteness with respect to relevant behaviors, potential for socially desirable re-sponding, objectivity, and clarity. Questions failing to meet one or more of thesecriteria were either revised or eliminated. The pool of revised questions was thenreviewed by three senior psychologists who selected the best 75 questions, be-tween 5 and 8 for each dimension.

All questions presented in the climate inventory were written in the presenttense to describe participants’ current work group and work environment. Eachquestion presented either a short positive or a short negative statement describingthe work environment. Participants were asked to indicate the frequency withwhich these events occurred in their workplace using a 5-point Likert scale from 1(never) to 5 (always). Table 2 provides an illustrative set of climate questions de-veloped for the 14 dimensions under consideration.

Environmental Experiences

In contrast to the climate questions, which examined perceptions of the workplace,the environmental experiences questions examined events that might have hap-

ENVIRONMENTAL INFLUENCES 347

TABLE 2Examples of Climate Questions

People here are adequately rewarded for their work or research.Distribution of rewards and credits is fair here.The processes by which decisions are made about credit allocation (e.g., authorship, paper or conference

presentations, grades) are fair here.The work environment here is characterized by infighting.There are power and territory struggles here.People here do not listen to each other in encouraging new initiatives.People here exhibit a sense of humor.People here are given the right type and amount of resources they need to do their work.A wide variety of viewpoints are expressed here.People here are given the autonomy and resources needed to define much of their own work.People here are expected to honor all agreements and show respect for others.Role models here set an example by sticking to their commitments and agreements.

pened to participants during their first few months at the university. These environ-mental influences questions were developed in accordance with the recommenda-tions of Mumford, Costanza, Connelly, and Johnson (1996) and Mumford, Stokes,and Owens (1990) for the construction of background data or life history ques-tions. Accordingly, development of these items began with a review of the litera-ture to identify dimensions of experience that might influence ethical behavior andscientific performance at the individual level (e.g., Arlow & Ulrich, 1980; Harri-son & Feit, 1999; Mullins & Sherman, 1993), the group level (e.g., Rallapalli,Vitell, & Szeinbach, 2000; Schminke & Wells, 1999; Trevino & Youngblood,1990), and the organizational level (e.g., Kim, Emmett, & Sikula, 2001; Verschoor,1998; Weaver, Trevino, & Cochran, 1999). These experiential dimensions in-cluded stress, individual competence, intrinsically interesting work, and competi-tive pressure at the individual level; leadership, group cohesion, and normlessnessat the group level; and accountability, munificence, and professional solidarity atthe organizational level. For each of the 43 experiential dimensions identifiedacross these three levels, operational definitions were formulated indicating howthese dimensions would be expressed in a scientific work environment.

Development of the environmental questions intended to provide measures ofthese dimensions was carried out by four doctoral students in industrial andorganizational psychology. Prior to beginning generation of these background dataquestions, panel members were provided with 40 hr of training concerning thedevelopment of background data questions by two senior psychologists; thistraining focused on the development of environmental experience questions.Following training, these four doctoral students were asked to generate 10 to 15questions that might reflect experiences occurring early in students’ graduatecareers bearing on a dimension. These questions might ask, “How often have youhad to wait for others in your laboratory to give you material you needed tocomplete your part of a process?” or “How often have you wondered if your majorprofessor approved or disapproved of work you had done?” Responses to thesequestions, which could be scored along a 5-point continuum, were formulated bypanel members at the time they generated questions accompanied, as necessary, bya “not applicable” response option (Mumford et al., 1990).

The initial set of questions developed by panel members was then presented to areview panel of three senior psychologists involved in this study. These psy-chologists reviewed item content for construct relevance, construct coverage,relevance to the experiences of the population, explicit situational focus, inap-propriate potential to induce socially desirable responding, offensiveness, andinvasion of privacy. Any questions that failed to meet one of these review criteriawere either dropped or edited. The 414 questions that survived the selectionprocess, 5 to 18 per dimension, were presented to participants in a random order tominimize construct guessing—as was the case in administering the climate ques-tions. Table 3 provides four illustrative environmental questions developed tomeasure individual-, group-, and organizational-level variables.

348 MUMFORD ET AL.

349

TAB

LE3

Exa

mpl

esof

Env

ironm

enta

lQue

stio

ns

Indi

vidu

alG

roup

Org

aniz

atio

nal

1.H

owof

ten

have

you

had

totu

rnin

two

orm

ore

clas

spr

ojec

tsin

one

wee

k?1.

How

ofte

nha

syo

urgr

oup

chal

leng

edits

elf

tode

velo

pun

ique

rese

arch

idea

sor

prod

ucts

?

1.H

owof

ten

has

your

rese

arch

been

dela

yed

beca

use

the

libra

rydi

dno

thav

eth

ere

sour

ces

you

need

ed?

2.H

owof

ten

have

you

esta

blis

hed

setw

ork

sche

dule

sfo

run

derg

radu

ate

rese

arch

assi

stan

tsw

hoar

ew

orki

ngon

one

orm

ore

ofyo

urpr

ojec

ts?

2.H

owof

ten

has

your

grou

pid

entif

ied

the

mer

itsof

each

indi

vidu

al’s

wor

k?2.

Tow

hate

xten

thav

ede

part

men

talf

unds

been

avai

labl

eto

acqu

ire

new

tech

nolo

gica

lres

ourc

es(e

.g.,

com

pute

rs,s

oftw

are,

othe

req

uipm

ent)

?3.

How

ofte

nha

veyo

uha

dto

resc

hedu

lem

eetin

gtim

esin

orde

rto

com

plet

ew

ork

befo

rea

mee

ting?

3.To

wha

text

enth

ave

grou

pm

embe

rsta

lked

abou

tgui

delin

esfo

rco

nduc

ting

rese

arch

?3.

How

ofte

nha

veth

em

embe

rsof

your

lab

fini

shed

thei

rth

eses

/dis

sert

atio

nst

udie

son

sche

dule

?4.

How

man

ytim

esha

veyo

uha

dto

canc

elm

eetin

gsor

mis

scl

asse

sin

orde

rto

mee

tade

adlin

e?

4.To

wha

text

entd

ogr

oup

mem

bers

cont

ribu

teth

ough

tful

idea

sin

lab

mee

tings

?

4.To

wha

text

entd

oes

depa

rtm

entl

itera

ture

(e.g

.,ha

ndbo

oks,

polic

ies)

stre

ssth

eim

port

ance

ofet

hica

lsta

ndar

ds?

Analyses

Analysis of the data gathered using the environmental experiences, climate, andethical decision-making measures occurred in two stages. Initially an oblique fac-tor analysis was conducted to provide dimensions capable of summarizing peo-ple’s responses to the environmental experience and climate questions. Based onthe recommendations of Schoenfeldt and Mendoza (1994), responses to the back-ground data questions were rationally scaled prior to factoring. The climate ques-tions, however, were factored without prior scaling. The environmental and cli-mate dimensions resulting from these initial analyses then provided the predictorset used in the next set of analyses.

In the second stage of analysis, the ability of the environmental and climatefactors to account for ethical decision making was examined. Initially, scores onthe four decision-making dimensions were correlated with scores on the environ-mental and climate factors. In the next set of analyses, a series of three partialleast-squares regression analyses were conducted in which each ethical decision-making criterion measure was regressed on the environmental factors alone, theclimate factors alone, and the climate factors followed by the environmentalfactors. It was anticipated that these analyses would indicate the unique effectsexerted by environmental experiences and climate perceptions on ethical decisionmaking.

RESULTS

Factors

Climate factors. Table 4 presents the results obtained from factoring the cli-mate questions. Inspection of the scree plot, indicating the percentage of varianceaccounted for by each factor, suggested that either a four- or six-factor solutionshould be retained. Examination of the cumulative variance accounted for by thesetwo potential factor solutions and the interpretability of the factors indicated thatthe four factor solution should be retained.

The first factor extracted in this analysis accounted for 27.85% of the variance.This factor produced an internal consistency coefficient (Cronbach’s alpha) of .95as estimated using those questions yielding loadings above .40. This first factorwas labeled equity because the majority of the questions yielding sizeable loadingswere concerned with the allocation of rewards and resources and the procedures bywhich these rewards and resources were administered. Thus, questions bearing onwhether people were rewarded (r = .87), they saw administration of awards as fair(r = .85), and fair processes were used in allocating credit (r = .76) all producedsizable loadings on this dimension.

350 MUMFORD ET AL.

The second factor obtained in this analysis accounted for 4.87% of the variancein responses to the climate questions, with questions yielding loadings above .40producing an internal consistency of .84. The questions yielding sizeable loadingson this factor indicated that the work environment was characterized by infighting(r = .80), there were territory and power struggles in the laboratory (r = .70), andpeople working in the laboratory did not listen to each other (r = .69). Based on thedistinct social conflict reflected in the questions yielding sizeable loadings, thissecond factor was labeled interpersonal conflict.

The third factor to emerge in this analysis accounted for 3.91% of the variance,with the climate questions yielding loadings above .40 producing an internal con-sistency coefficient of .91. The nature of the questions yielding sizeable loadingson this dimension indicated that people were characterized as enjoying their work.Specifically, people exhibited a sense of humor (r = .76), had fun at work (r = .74),

ENVIRONMENTAL INFLUENCES 351

TABLE 4Factor Analysis Results for Climate Measure

Equity (27.85%; ru = .95) Interpersonal Conflict (4.87%; ru = .84)

1. People adequately rewarded .87 1. Work environment characterized byinfighting

.80

2. Distribution of rewards fair .85 2. There are territory and power struggles .703. Fair process for credit allocation .76 3. People do not listen to each other .694. People are satisfied with rewards .73 4. Group differences lead to difficulties .635. People can take personal initiative .73 5. People here often gossip .606. Rewards are given for outstanding work .69 6.

7.

People disagree about resourcedistributionHostilities are present in the work place

.567. Honoring agreements is important .67

.488. There are opportunities to requestresources

.66

9. Equitable process determinesassignments

.64

10. People have opportunities to requestresources

.63

Occupational Engagement (3.91%; ru = .91) Work Commitment (2.84%; ru = .80)

1. People exhibit a sense of humor .76 1. There is a high degree of commitmentto goalsPeople have autonomy to defineresources neededPeople are expected to honor agreementsRole models stress organizationalcommitmentPeople intend to contribute to greatergood in work

.732. People have fun at work .743. Wide variety of viewpoints are expressed .70 2. .614. People discuss opposing perspectives .695. Debate is frequent .66 3.

4.

5.

.556. Many different views are expressed .64 .517.8.9.

People joke at workPeople count on others for supportPeople feel welcome to present newideas

.62

.61

.61

.51

a wide variety of viewpoints were expressed (r = .70), and people discussed oppos-ing perspectives on work problems (r = .69). As a result of these loadings, this thirdfactor was labeled occupational engagement.

The fourth and final factor identified in examining the climate questionsaccounted for 2.84% of the variance, with the items producing sizeable loadingsyielding an internal consistency of .80. The climate questions producing sizableloadings indicated that there was a high degree of commitment to laboratory goals(r = .73), people could request needed resources (r = .61), people were expected tohonor agreements (r = .55), role models stressed organizational commitment (r =.51), and people hoped their work would contribute to the greater good (r = .51).This pattern of loadings suggests that this final climate dimension captured workcommitment. Factor scores from this analysis were used as the climate predictorsin subsequent analyses.

Environmental experience factors. Table 5 presents the results obtainedfrom factoring of the environmental experiences questions following initialscaling of question responses. Examination of the scree plot indicated that ei-ther a five- or an eight-factor solution might be retained given the changes ob-served in variance accounted for. However, examination of the loadings ofthe dimensions on these factors indicated that the five-factor solution should beretained.

The first factor extracted in this analysis accounted for 36.90% of the variance.The internal consistency coefficient obtained for questions producing loadingsabove .40 was .95. As may be seen, a number of dimensions concerned with thebehavior of the laboratory leader, or major professor, produced sizeable loadingson this dimension, including leader initiating structure (r = .85), leader vision (r =.84), leader direction of group process (r = .79), leader intellectual stimulation (r =.77), leader individual consideration (r = .75), and leader expertise (r = .74). Inaddition, prosocial norms (r = .85), quality of group performance (r = .82),expertise of group members (r = .81), group trust (r = .73), production pressure (r =.67), and a lack of normlessness (r = –.75) also produced sizeable loadingsindicating that this factor should be labeled professional leadership.

The second factor extracted in analysis of the environmental experience ques-tions accounted for 11.02% of the variance. The questions yielding loadings above.40 on this factor produced an internal consistency coefficient of .87. Morecentrally, the dimensions yielding sizable loadings on this factor included aliena-tion (r = .74), feedback indicating a lack of competence (r = .74), stress (r = .71), anegative reward structure (r = .71), feedback ambiguity (r = .69), and negativerelationships with peer group members (r = .68). As a result, this dimensionindicated poor coping—either as a result of individual behavior or people’sinteractions with their work group.

352 MUMFORD ET AL.

353

TAB

LE5

Fact

orA

naly

sis

Res

ults

for

Env

ironm

enta

lMea

sure

Pro

fess

iona

lLea

ders

hip

(36.

90%

;r t

t=

.95)

Poor

Cop

ing

(11.

02%

;r t

t=.8

7)L

ack

ofR

ewar

ds(4

.30%

;r t

t=

.60)

1.In

itiat

ing

stru

ctur

e.8

51.

Alie

natio

n.7

41.

Lon

gtim

efo

rre

war

ds–.

692.

Pros

ocia

lnor

ms

.85

2.In

com

pete

nce

.74

2.N

egat

ive

prof

essi

onal

role

mod

els

.59

3.L

eade

rvi

sion

.84

3.St

ress

.71

3.Po

sitiv

ere

war

dst

ruct

ure

–.57

4.Q

ualit

yof

grou

ppe

rfor

man

ce.8

24.

Neg

ativ

ere

war

dst

ruct

ure

.71

4.L

eade

rin

divi

dual

cons

ider

atio

n–.

555.

Exp

ertis

eof

grou

pm

embe

rs.8

15.

Feed

back

ambi

guity

.69

5.L

eade

rvi

sion

–.50

6.D

irec

tion

ofpr

oces

s.7

96.

Neg

ativ

epe

ergr

oup

.68

6.Pr

ofes

sion

also

lidar

ity–.

437.

Lea

der

inte

llect

uals

timul

atio

n.7

77.

Intr

insi

cw

ork

expe

rien

ce.6

18.

Nor

mle

ssne

ss–.

758.

Com

petit

ive

pres

sure

.61

9.L

eade

rin

divi

dual

cons

ider

atio

n.7

59.

Prod

uctio

npr

essu

re.4

910

.Lea

der

expe

rtis

e.7

410

.Tur

bule

nce

.42

11.G

roup

trus

t.7

312

.Pro

duct

ion

pres

sure

.67

Lim

ited

Com

peti

tive

Pre

ssur

e(3

.08%

;r t

t=

.86)

Poor

Car

eer

Dir

ecti

on(2

.44%

;r t

t=

.74)

1.U

neth

ical

norm

s.8

31.

Posi

tive

rew

ard

stru

ctur

e–.

702.

Pers

onal

ized

com

petit

ive

pres

sure

–.81

2.L

ack

ofsi

tuat

iona

lcon

trol

.60

3.G

roup

conf

lict

–.68

3.In

tegr

atio

nof

grou

p–.

594.

Prod

uctio

npr

essu

re–.

634.

Qua

lity

ofgr

oup

perf

orm

ance

–.59

5.A

liena

tion

.51

5.C

ompe

titiv

epr

essu

re–.

586.

Neg

ativ

epe

ergr

oup

–.50

6.Pr

oduc

tion

pres

sure

–.53

7.L

eade

rin

telle

ctua

lstim

ulat

ion

–.49

7.A

nom

ie.4

98.

Feed

back

spec

ific

ity–.

479.

Lea

der

visi

on–.

47

The third factor extracted in this analysis accounted for 4.30% of the variance.Questions yielding loadings above .40 resulted in an internal consistency coef-ficient of .60. The dimensions yielding sizable loadings included a long time framefor rewards (r = –.69), positive rewards (r = –.57), negative professional rolemodels (r = .59), and consideration from these role models (r = –.55). Theseloadings indicated that this dimension reflected a lack of rewards. The fourthdimension extracted in this analysis accounted for 3.08% of the variance, pro-ducing an internal consistency coefficient of .86. It was found that this dimensionproduced a sizable positive loading (r = .83), whereas the personalized competitivepressure (r = –.81), group conflict (r = –.68), and production pressure (r = –.63)dimensions produced sizable negative loadings. These sizable negative loadings,combined with the fact that application of inappropriate norms is often associatedwith group disruption, led this factor to be labeled limited competitive pressure.

The fifth and final factor obtained in the analysis of the environmental variablesaccounted for 2.44% of the variance producing an internal consistency coefficient.74 when questions yielding loadings above .40 were examined. The dimensionsproducing sizeable loadings indicated the lack of positive reward structure (r =.70), a lack of situation control (r = .60), poor integration into the work group (r =.59), low quality of group performance (r = .59), limited competitive pressure (r =.58), limited production pressure (r = .53), feelings of anomie (r = .49), a lack ofspecific feedback (r = .47), and a lack of leader vision (r = .47). Because thesedimensions suggested poor, or inadequate, direction of the group vis-à-vis theindividual this factor was poor career direction. Factor scores from this analysiswere used as the environmental experiences predictors in subsequent analyses.

Correlations

Climate and environmental experience correlations. Table 6 presents thecorrelations among the various factor scales as well as their correlations withscores on the four ethical decision-making dimensions involving data man-agement, study conduct, professional practices, and business practices. Broadlyspeaking, the correlations observed among the factors provide some evidence forthe meaningfulness of the factor solutions. For example, the climate dimension,equity, was found to exhibit moderate positive relationships with occupational en-gagement (r = .40) and work commitment (r = .28) but a negative relationship withinterpersonal conflict (r = –.20). Similarly, neither the negative relationship ob-served between interpersonal conflict and occupational engagement (r = –.21) northe positive correlation observed between occupational engagement and workcommitment (r = .22) is especially surprising.

Not only did the correlations observed among the climate factors provide someevidence for their construct validity, the correlations observed among the en-vironmental experience factors also provided some evidence bearing on their

354 MUMFORD ET AL.

355

TAB

LE6

Cor

rela

tions

ofE

nviro

nmen

talE

xper

ienc

esan

dC

limat

eD

imen

sion

s

Men

Wom

en

Item

sM

SDM

SD1

23

45

67

89

1011

1213

1.E

quity

.07

.84

–.06

1.09

1.0

–.20

*.4

0*.2

8*.3

2*–.

01–.

38*

.20*

–.24

*.0

8.1

5.1

2–.

032.

Inte

rper

sona

lco

nflic

t–.

03.9

9.0

1.9

21.

0–.

21*

.07

–.03

.43*

.14

–.40

*–.

14–.

16–.

07–.

26*

–.24

*

3.O

ccup

atio

nal

enga

gem

ent

–.12

.82

.11

1.02

1.0

.22*

.31*

–.13

–.11

.14

–.15

.15

.10

.21*

–.09

4.W

ork

com

mitm

ent

.03

.85

–.07

.96

1.0

.14

–.08

–.15

.07

–.05

.06

.22*

.12

.18

5.Pr

ofes

sion

alle

ader

ship

–.03

1.09

–.02

.95

1.0

.30*

–.30

*–.

31*

–.37

*.0

7–.

07.1

1–.

18

6.Po

orco

ping

–.07

.75

.08

1.08

1.0

.02

–.48

*–.

25*

–.27

*–.

12–.

20*

–.20

*7.

Lac

kof

rew

ards

–.31

.76

.17

.97

1.0

.00

.16

–.21

*–.

04–.

36*

.03

8.L

imite

dco

mpe

titiv

ePr

essu

re.0

21.

01–.

02.9

31.

0.2

3*.1

9*.2

1*.2

4*.2

2*

9.Po

orca

reer

dire

ctio

n–.

09.9

8.1

1.8

11.

0–.

01.1

7.0

6.3

0*10

.Dat

am

anag

emen

t2.

33.2

02.

32.2

61.

0.2

2*.5

7*.1

711

.Stu

dyco

nduc

t2.

25.3

32.

33.2

21.

0.3

4*.5

2*12

.Pro

fess

iona

lpr

actic

es2.

27.2

22.

32.2

01.

0.2

2*

13.B

usin

ess

prac

tices

2.26

.22

2.36

.30

1.0

*p<

.05.

validity. For example, the professional leadership dimension was found to benegatively related to lack of rewards (r = –.30), limited competitive pressure (r =–.31), and poor career direction (r = –.37). However, the greater demands made onentering doctoral students in stronger laboratories led to a positive correlationbetween professional leadership and poor coping (r = .30). Similarly, it was notsurprising that poor coping was negatively related to limited competitive pressure(r = –.48) given the stress effects induced by competition.

Some additional evidence bearing on the meaningfulness of these factors isprovided by the correlations observed among the climate and environmentalfactors. As one might expect, equity was positively related to professional lead-ership (r = .32) and limited competitive pressure (r = .20) but negatively related tolack of rewards (r = –.38) and poor career direction (r = -.24). Interpersonalconflict, as might be expected, was positively related to poor coping (r = .43) butnegatively related to limited competitive pressure (r = –.40). Although theserelationships appear meaningful, generally, the environmental and climate factorsdid not evidence strong relationships, suggesting that they reflected different typesof influences on ethical decision making.

Correlations with ethical decision making. Table 6 also presents the cor-relations of the climate and environmental measures with the ethical decision-making measures. With respect to the climate measures, it was found that interper-sonal conflict among entering doctoral students was negatively related to ethicaldecision making with respect to both professional practices (r = –.26) and businesspractices (r = –.24). Apparently, an early climate laden with conflict tends to leadpeople to make unethical decisions in interactions with others. In contrast, occupa-tional engagement was found to be positively related to ethical decisions involvingprofessional practices (r = .21)—a finding suggesting that engagement with othersmay lead to prosocial professional behavior. Finally, as might be expected, workcommitment was positively related to ethical decisions about study conduct (r =.22). Thus, doctoral students entering an environment they perceived as beingcommitted to the work were more likely to make ethical decisions.

The correlations between the environmental experience measures and ethicaldecision making are also presented in Table 6. As may be seen, poor coping wasnegatively related to ethical decisions about data management (r = –.27), pro-fessional practices (r = –.20), and business practices (r = –.20). Apparently,doctoral students who are having difficulty adjusting to the demands of graduateeducation make poor decisions. In addition, lack of rewards was found to benegatively related to ethical decisions about data management (r = –.21) andethical decisions about professional practices (r = –.36). These findings, of course,suggest that young graduate students might take actions to bring about rewards andreinforcements, even unethical actions, when these rewards and reinforcementsare not provided by the work environment.

356 MUMFORD ET AL.

Although these findings seem reasonable, it should also be noted that limitedcompetitive pressure proved positively related to ethical decision making. Morespecifically, limited competitive pressure was positively related to ethical deci-sions with regard data management (r = .19), study conduct (r = .21), professionalpractices (r = .24), and business practices (r = .22). Apparently, undue competitivepressure, when placed on people lacking requisite skills, can lead to unethicaldecision making across a number of domains. In addition, it was found that poorcareer direction was positively related to ethical decision making with respect tobusiness practices (r = .30), a finding that may reflect the broader effects of a lackof direction.

Regression Analyses

The results obtained in the regression analyses are presented in Table 7. As may beseen, when scores on the ethical decision-making criteria were regressed on theclimate factors, the average multiple correlation was .27. However, significant (p <.05) multiple correlations were obtained only for ethical decisions involving pro-fessional practices (R = .31) and business practices (R = .37). In both cases, signifi-cant (p < .05) regression weights were produced by the interpersonal conflict fac-tor, which were –.22 and –.29 for professional practices and business practices,respectively. Again, it appears that conflict in the climate of a laboratory mayvis-à-vis generalization led to unethical decisions involving others. In addition, inthe case of business practices, work commitment was found to be positively relatedto ethical decisions (β = .24). This result, given the correlational data presentedearlier, may, however, reflect the operation of a suppressor effect.

When the ethical decision-making measures were regressed on the environ-mental experience factors, a larger average multiple correlation of .36 was ob-tained. Three of the four ethical decision-making criteria produced significantmultiple correlations—more specifically, decisions involving data management (R= .37), professional practices (R = .47), and business practices (R = .33). In the caseof data management, a significant (p < .05) regression weight was produced bypoor coping (β = –.28), a finding suggesting that difficulty in adaptation may leadto poor decision making with regard to data management. In the case of pro-fessional practices, a significant (p < .05) regression weight was produced by lackof rewards (β = –.34). Apparently, lack of rewards may, at least among entry-leveldoctoral students, be associated with poor decisions regarding professional be-havior. Finally, for business practices a significant (p < .05) regression weight wasproduced by poor career direction (β = .25). This finding, again, may reflect thefailure of graduate school experiences to influence business decisions whenoverall direction was weak.

When the experience factors and climate factors were used in predicting thefour ethical decision-making criteria, the average multiple correlation increased to

ENVIRONMENTAL INFLUENCES 357

358

TAB

LE7

Reg

ress

ion

ofE

thic

alD

ecis

ion

Mak

ing

Mea

sure

onC

limat

ean

dE

nviro

nmen

talS

cale

s

Dat

aM

anag

emen

tSt

udy

Con

duct

Pro

fess

iona

lP

ract

ices

Bus

ines

sP

ract

ices

Dat

aM

anag

emen

tSt

udy

Con

duct

Pro

fess

iona

lP

ract

ices

Bus

ines

sP

ract

ices

Blo

ck1

Blo

ck1

1.E

quity

–.06

.07

–.02

–.09

1.Pr

ofes

sion

alle

ader

ship

.12

.00

.14

–.02

2.In

terp

erso

nal

conf

lict

–.13

–.03

–.22

*–.

29*

2.Po

orco

ping

–.28

*.0

1–.

12–.

05

3.O

ccup

atio

nal

enga

gem

ent

.11

.01

.14

.19

3.L

ack

ofre

war

ds–.

16–.

13–.

34*

–.03

4.W

ork

com

mitm

ent

.02

.19

.07

.24*

4.L

imite

dco

mpe

titiv

epr

essu

re

.09

–.16

.18

.10

Ove

rall

mul

tiple

corr

elat

ion

.20

.24

.31*

.37*

5.Po

orca

reer

dire

ctio

n.0

4.1

6.1

1.2

5*

Ove

rall

mul

tiple

corr

elat

ion

.37*

.27

.47*

.33*

359

Blo

ck1

1.E

quity

–.08

.07

–.16

–.02

2.In

terp

erso

nal

conf

lict

.02

.05

–.06

–.20

3.O

ccup

atio

nal

enga

gem

ent

.06

.05

.14

–.17

4.W

ork

com

mitm

ent

.00

.18

.04

.23*

Blo

ck2

5.Pr

ofes

sion

alle

ader

ship

.14

–.05

.13

–.02

6.Po

orco

ping

–.33

*.0

0–.

18–.

057.

Lac

kof

rew

ards

–.15

–.10

–.35

*.0

38.

Lim

ited

Com

petit

ive

pres

sure

.08

.11

.14

.04

9.Po

orca

reer

dire

ctio

n–.

07.1

6.0

4.1

7

Ove

rall

mul

tiple

corr

elat

ion

.40*

.33

.51*

.43*

*p<

.05.

.42 with the data management, professional practices, and business practicesdimensions producing significant (p < .05) multiple correlations of .40, .51, and.43. Notably, however, only one factor, work commitment (β = .23) produced asignificant (p < .05) regression weight for business practices. Addition of theexperience factors resulted in significant (p < .05) gains in prediction with respectto data management, due to poor coping (β = –.33), and with respect to pro-fessional practices, due to lack of rewards (β = –.35). Apparently, at least withregard to data and professional practices, environmental variables exert uniqueinfluences on ethical decision making that can not be accounted for by climate.

DISCUSSION

Before turning to the broader conclusions flowing from this study, certain limita-tions should be noted. To begin, it should be recognized that we have, in our study,examined the influence of climate perceptions and environmental experiences onethical decision making in a somewhat unique sample—specifically, 1st-year doc-toral students in the biological, health, and social sciences. Although the relation-ship of climate and environment are of particular interest in this “early career”stage, caution is clearly called for in extending our findings to professional popula-tions who have greater expertise.

Along somewhat different lines, it should also be recognized that the resultsobtained in this study have focused solely on ethical decision making. Althoughthe decision-making measures applied in this study allowed multiple types ofethical decisions to be examined in accordance with the model proposed byHelton-Fauth et al. (2003), caution is called for in generalizing findings fromstudies of ethical decision making to actual incidents of ethical behavior in“real-world” settings. Moreover, it should be recognized that the ethical decisionmaking being examined in this study focused on generic forms of ethical decisionsapplying across a variety of fields (Csikszentmihalyi, 1999). Accordingly, thequestion remains as to how climate and environmental factors might be related tomore field-specific forms of ethical decision making.

Finally, it should be recognized that both climate perceptions and environ-mental experiences examined in this study were those appropriate to the sample athand. As a result, it is possible, and indeed likely, that a different set of climate andexperiential factors might emerge in other populations. Moreover, it seems likelythat shifts might be observed in how climate variables and environmental ex-periences are related to ethical decision making. Even bearing these caveats inmind, however, we believe the results obtained in our study have some noteworthyimplications for understanding how the situation in which people find themselvesinfluences ethical decision making.

360 MUMFORD ET AL.

Perhaps the most clear-cut conclusion to emerge from studies of ethics is thatclimate makes a difference with regard to people’s ethical decision making (Forte,2004; Fritzsche, 2000; Sims & Keon, 1999). In fact, the results obtained in ourstudy indicate that climate perceptions are associated with ethical decision makingamong 1st-year doctoral students. In our study, four general dimensions of climatewere identified—equity, interpersonal conflict, occupational engagement, andwork commitment. Although occupational engagement and work commitmentwere positively related to certain forms of ethical decision making, specificallyprofessional practices for occupational engagement and study conduct for workcommitment, only one climate dimension exerted strong, consistent relations withethical decision making. More specifically, in both the correlation and regressionanalyses that focused strictly on climate, perceptions of interpersonal conflict werefound to be negatively related to ethical decisions by young scientists with regardto professional practices and business practices.

The influence of interpersonal conflict on these kinds of ethical decisionsmight be accounted for by a variety of models. For example, these negative as-sociations may reflect generalization of work environment beliefs to appraisalsof other events calling for social interaction. It is possible, however, that con-flict in the immediate work environment may, at least among younger students,be associated to the formation of mental models for interpreting other’s inten-tions. Mental models associated with a negative view of others may lead to un-ethical decisions as a self-protective mechanism (Mumford, 2006). Finally, it ispossible this conflict might arise from more basic personality variables givingrise to both conflict and unethical decisions. We hope that future research willallow a more detailed examination of specific causal mechanisms underlyingthese effects. In fact, such research, especially research examining mainte-nance or diminishment of these effects over time, may prove of practical valueby identifying interventions likely to minimize conflict among entry-level doc-toral students.

Although interpersonal conflict was related to unethical decision making withrespect to professional and business practices, the effects of climate perceptions onethical decisions were not as large as the associations emerging from specificenvironmental experiences of 1st-year doctoral students. In our factoring of theenvironmental experiences inventory, five factors emerged—professional leader-ship, poor coping, lack of rewards, limited competitive pressure, and poor careerdirection. These environmental experience dimensions, in fact, produced moresignificant correlations and substantially larger multiple correlations with the fourethical decision-making measures than the climate dimensions. Moreover, theyadded to the prediction resulting from the climate factors for two of the four ethicaldecision-making dimensions. Thus it appears, at least among 1st-year doctoralstudents, that experience exerts stronger effects on ethical decision making thanthe climate of the work group.

ENVIRONMENTAL INFLUENCES 361

In fact, the focus of 1st-year doctoral students on adapting to a professionalfield and learning basic operations within this field, may lead them to be par-ticularly sensitive to environmental influences with respect to subsequent ethicaldecision making. Although the importance of early career experiences and sub-sequent ethical decisions is not surprising (e.g., Gelman & Gibelman, 2002;Mumford et al., 2005), the specific nature of the dimensions giving rise to theseeffects is of greater interest.

The correlational analysis indicated that limited competitive pressure waspositively related to ethical decision making across all four types of decisions.Taken at face value, this pattern of findings suggests that ethical decision makingmight be improved if people are not placed under excessive or undue pressurein the early phases of their career. In fact, excessive competitive pressure may leadto the acquisition of beliefs likely to engender the potential for unethical de-cisions throughout an individual’s career. By the same token, however, science iscompetitive (Feist & Gorman, 1998). Thus what may be required is effectivemanagement of competitive pressure vis-à-vis the individual’s readiness andcapabilities.

In this regard, however, it is important to bear in mind the point that limitedcompetitive pressure did not produce significant prediction of ethical decisionmaking in the regression analyses. When relationships among the environmentalexperience factors were taken into account, poor coping was found to be stronglynegatively related to ethical decisions involving data management, whereas lack ofrewards was found to be strongly negatively related to ethical decisions involvingprofessional practices. Thus competitive pressure per se may not be the criticalinfluence on ethical decisions, but, instead, poor coping and a lack of rewards.Apparently, environmental experiences that disrupt individual performance tendsto be associated in unethical decisions with regard to data management, whereas alack of rewards for individuals, vis-à-vis frustration mechanisms, is related tounethical decisions with respect to professional practices.

More broadly, the findings sketched out previously, although complicated, dopaint a coherent picture of the environmental experiences and climate influencesthat could give rise to unethical decisions on the part of entering doctoral students.Poor coping may not only give rise to unethical decisions, the resulting per-formance decrements will result in a lack of rewards. A lack of rewards, and poorcoping, will in turn give rise to perceptions of interpersonal conflict which, in turn,will lead to unethical decisions. This model, of course, should be explicitly testedin subsequent efforts. Nonetheless, it suggests that maladaptation to the pro-fessional environment may be an important source of unethical decisions and,presumably, unethical behavior due to both direct effects and indirect effectsthrough lack of rewards and perceptions of conflict.

Although the associations between poor coping, lack of rewards, perceptionsof conflict, and ethical decision making can be readily explained, they have note-

362 MUMFORD ET AL.

worthy implications with regard to the management of 1st-year doctoral students.More specifically, major professors should attempt to minimize alienation, stress,and feelings of incompetence while developing a strong group in which feedbackis provided in unambiguous terms. Moreover, major professors, or supervisors,should provide entering doctoral students with positive, albeit appropriate, feed-back that stresses the value of the professional work being done. In fact, inter-ventions that seek to provide the directors of entering doctoral students with thecompetencies needed to manage students in this fashion may prove of substantialvalue in enhancing ethical decision making. We hope that this study will providean impetus for future research along these lines.

ACKNOWLEDGMENTS

The data collection was supported in part by the National Institutes of Health, Na-tional Center for Research Resources, General Clinical Research Center GrantM01 RR-14467. Parts of this work were sponsored by a grant from the National In-stitutes of Health and the Office of Research Integrity (5R01-NS049535-02), Mi-chael D. Mumford, Principal Investigator. Complete measures may be obtained bycontacting Michael D. Mumford.

We thank Jennifer Carmichael and Ethan Waples for their contributions in thepresent effort.

REFERENCES

Agarwal, J., & Malloy, D. C. (1999). Ethical work climate decisions in a not-for-profit organization: Anempirical study. Journal of Business Ethics, 20, 1–14.

Akre, V., Falkum, E., Hoftvedt, B. O., & Aasland, O. G. (1997). The communication atmosphere be-tween physician colleagues: Competitive perfectionism or supportive dialogue? A Norwegian study.Social Science & Medicine, 44, 519–526.

Amabile, T. M., Conti, R., Coon, H., Lazerby, J., & Herron, M. (1996). Assessing the work environmentfor creativity. Academy of Management Journal, 39, 1154–1185.

Argandona, A. (1999). Sharing out in alliances: Trust and ethics. Journal of Business Ethics, 21,217–228.

Arlow, P., & Ulrich, T. A. (1980). Business ethics and social responsibility of business students: An em-pirical comparison of Clark’s study. Akron Business and Economic Review, 11, 17–23.

Barnett, T., & Schubert, E. (2002). Perceptions of the ethical work climate and covenantal relation-ships. Journal of Business Ethics, 36, 279–290.

Barnett, T., & Vaicys, C. (2000).The moderating effect of individuals’ perceptions of ethical work cli-mate on ethical judgments and behavioral intentions. Journal of Business Ethics, 27, 351–362.

Bechtel, H. K., & Pearson, W. (1985). Deviant scientists and scientific deviance. Deviant Behavior, 6,237–252.

Csikszentmihalyi,M.(1999). Implicationsofasystem’sperspectivefor thestudyofcreativity. InR.J.Stern-berg (Ed.), Handbook of creativity (pp. 313–338). Cambridge, England: Cambridge University Press.

ENVIRONMENTAL INFLUENCES 363

Davis, M. S., & Riske, M. L. (2001). Preventing scientific misconduct: Insights from convicted offend-ers. In N. Steneck & M. Scheetz (Eds.), Proceedings of the First Conference on Research Integrity(pp. 143–149). Washington, DC: National Institutes of Health.

Dunbar, K. (1995). How scientist really reason: Scientific reasoning in real world laboratories. In R.J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 365–396). Cambridge, MA: MITPress.

Eastman, J. K., Eastman, K. L., & Tolson, M. A. (2001). The relationship between ethical ideology andethical behavior intentions: An exploratory look at physicians’ responses to managed care dilemmas.Journal of Business Ethics, 31, 209–224.

Ekvall, G., & Ryhammer, L. (1999). The creative climate: Its determinants and effects at a SwedishUniversity. Creativity Research Journal, 12, 303–310.

Feist, G. J., & Gorman, M. E. (1998). The psychology of science: Review and integration of a nascentdiscipline. Review of General Psychology, 2, 3–47.

Forte, A. (2004). Business ethics: A study of the moral reasoning of selected business managers and theinfluence of organizational ethical climate. Journal of Business Ethics, 51, 167–173.

Fritzsche, D. J. (2000). Ethical climates and the ethical dimension of decision making. Journal of Busi-ness Ethics, 24, 125–140.

Gelman, S., & Gibelman, M. (2002). Plagiarism in academia: Are students emulating (bad) facultyrole models? Paper presented at the ORI Research Conference on Research Integrity, November,Potomac, MD.

Goldberg, L. R., & Greenberg, M. (1994). A survey of ethical conduct in risk management: Environ-mental economists. Ethics & Behavior, 4, 331–343.

Harrison, J. S., & Feit, J. O. (1999). New CEOs pursue their own self-interests by sacrificing stake-holder value. Journal of Business Ethics, 19, 301–308.

Hartman, S. J., Yrle, A. C., & Galle, W. P. (1999). Procedural and distributive justice: Examining equityin a university setting. Journal of Business Ethics, 20, 337–351.

Helton-Fauth, W., Gaddis, B., Scott, G., Mumford, M., Devenport, L., Connelly, S., et al. (2003). A newapproach to assessing ethical conduct in scientific work. Accountability in Research: Policies andQuality Assurance, 10, 205–228.

Hunter, S. T., Bedell, K. E., & Mumford, M. D. (2007). Climate for creativity: A quantitative review.Creativity Research Journal, 19, 69–90.

James, L. R., James. L. A., & Ashe, D. K. (1990). The meaning of organizations: The role of cognitionand values. In B. Schneider (Ed.), Organizational climate and culture. (pp. 40–84). San Francisco,CA: Jossey-Bass.

Jansen, E., & von Glinow, M. A. (1985). Ethical ambivalence and organizational reward systems. Acad-emy of Management Review, 10, 814–822.

Jasanoff, S. (1993). Innovation and integrity in biomedical research. Academic Medicine, 68, 91–95.Kim, C. W., Emmett, D., & Sikula, A. (2001). Employee relations ethics and the changing nature of the

American workforce. Ethics & Behavior, 11, 23–38.Kochan, C. A., & Budd, J. M. (1992). The persistence of fraud in the literature. Journal of the American

Society for Information Science, 43, 488–493.Koh, H. C., & Boo, E. H. (2001). The link between organizational ethics and job satisfaction: A study of

managers in Singapore. Journal of Business Ethics, 29, 309–324.Levenson, J. L. (1986). When a colleague practices unethically: Guidelines for intervention. Journal of

Counseling Psychology and Development, 64, 315–317.Lovell, A. (2002). Ethics as a dependent variable in individual and organizational decision making.

Journal of Business Ethics, 37, 145–145.Marshall, E. (1996). Fraud strikes top genome lab. Science, 274, 908–910.Martinson, B. C., Anderson, M. S., & de Vries, R. (2005). Scientists behaving badly. Nature, 435,

737–738.

364 MUMFORD ET AL.

Mayo, M. A., & Marks, L. J. (1990). An empirical investigation of a general theory of marketing ethics.Journal of the Academy of Marketing Science, 18, 163–171.

Miner, M., & Petocz, A. (2003). Moral theory in ethical decision making: Problems, clarifications andrecommendations from a psychological perspective. Journal of Business Ethics, 42, 11–25.

Morris, M. H., Schindehutte, M., Walton, J., & Allen, J. (2002).The ethical context of entrepreneurship:Proposing and testing a developmental framework. Journal of Business Ethics, 40, 331–361.

Motowidlo, S. J., Dunnette, M. D., & Carter, G. W. (1990). An alternative selection measure: Thelow-fidelity simulation. Journal of Applied Psychology, 75, 640–647.

Muchinsky, P. M. (2004). When the psychometrics of test development meets organizational realities:A conceptual framework for organizational change, examples, and recommendations. PersonnelPsychology, 57, 175–209.

Mullins, R. F., & Sherman, R. (1993). Creativity and performance appraisal: Shall never the twainmeet. Creativity Research Journal, 6, 425–434.

Mumford, M. D. (2006). Pathways to outstanding leadership: A comparative analysis of charismatic,ideological, and pragmatic leadership. Mahwah, NJ: Lawrence Erlbaum Associates.

Mumford, M. D., Connelly, M. S., Scott, G. M., Espejo, J., Sohl, L. M., Hunter, S. T., et al. (2005). Ca-reer experiences and scientific performance: A study of social, physical, life, and health sciences.Creativity Research Journal, 17, 105–129.

Mumford, M. D., Costanza, D. P., Connelly, M. S., & Johnson, J. F. (1996). Item generation proceduresand background data scales: Implications for construct and criterion-related validity. Personnel Psy-chology, 49, 361–398.

Mumford, M. D., & Helton, W. B. (2000). Organizational Influences on Scientific Integrity. Paper pre-sented at the ORI Conference on Research on Research Integrity, November, Bethesda, MD.

Mumford, M. D., Stokes, G. S., & Owens, W. A. (1990). Patterns of life adaptation: The ecology of hu-man individuality. Hillsdale, NJ: Lawrence Erlbaum Associates.

Neubaum, D. O., Mitchell, M. S., & Schminke, M. (2004). Firm newness, entrepreneurial orientation,and ethical climate. Journal of Business Ethics, 52, 334–347.

Newman, E., McCoy, V., & Rhodes, A. (2000). Ethical research practice with human participants:Problems, pitfalls, and beliefs of funded researchers. Paper presented at the ORI Research Confer-ence on Research Integrity, November, Bethesda, MD.

Paolillo, J. G. P., & Vitell, S. J. (2002). An empirical investigation of the influence of selected personal,organizational and moral intensity factors on ethical decision making. Journal of Business Ethics,35, 65–74.

Parker, C. P., Baltes, B. B., Young, S. A., Huff, J. W., Altmann, R. A., Lacost, H. A., et al. (2003). Rela-tionships between psychological climate perceptions and work outcomes: A meta-analytic review.Journal of Organizational Behavior, 24, 389–416.

Peterson, R. S. (2001). Toward a more deontological approach to the ethical use of social influence. InJ. M. Darley & D. M. Messick (Eds.), Social influences on ethical behavior in organizations (pp.21–36). Mahwah, NJ: Lawrence Erlbaum Associates.

Pimple, K. (2002). Assessing teaching and learning in the responsible conduct of research. Paper pre-sented at the ORI Research Conference on Research Integrity, Potomac, MD.

Rallapalli, K. C., Vitell, S. J., & Szeinbach, S. (2000). Marketers’norms and personal values: An empir-ical study of marketing professionals. Journal of Business Ethics, 24, 65–75.

Reall, M. J., Bailey, J. J., & Stoll, S. K. (1998). Moral reasoning on hold during a competitive game.Journal of Business Ethics, 17, 1205–1210.

Rest, J., Narváez, D., Bebeau, M., & Thoma, S. (1999). Postconventional moral thinking: A neo-kohl-bergian approach. Hillsdale, NJ: Lawrence Erlbaum Associates.

Schminke, M., Ambrose, M. L., & Neubaum, D. O. (2005). The effect of leader moral development onethical climate and employee attitudes. Organizational Behavior & Human Decision Processes, 97,135–151.

ENVIRONMENTAL INFLUENCES 365

Schminke, M., & Wells, D. (1999). Group processes and performance and their effects on individual’sethical frameworks. Journal of Business Ethics, 21, 251–267.

Schneider, B., & Reichers, A. E. (1983). On the etiology of climates. Personnel Psychology, 36, 19–39.Schoenfeldt, L. F., & Mendoza, G. C. (1994). Developing and using factorially derived biographical

scales. In G. S. Stokes, M. D. Mumford, & W A. Owens (Eds.), Biodata handbook: Theory, researchand use of biographical information in selection and performance prediction (pp. 147–170). PaloAlto, CA: Consulting Psychologists Press.

Sims, R. R., & Brinkmann, J. (2002). Leaders as moral role models: The case of John Gutfreund atSalomon Brothers. Journal of Business Ethics, 35, 327–339.

Sims, R. L., & Keon, T. L. (1999). Determinants of ethical decision-making: The relationship of theperceived organizational environment. Journal of Business Ethics, 19, 393–404.

Steneck, N. H. (2004). ORI introduction to the responsible conduct of research. Washington, DC:Health and Human Services.

Subotnik, R. F., & Steiner, C. L. (1993). Adult manifestations of adolescent talent in science. RoeperReview, 15, 164–169.

Thomas, T., Schermerhorn,J. R., Jr., & Dienhart, J. W. (2004). Strategic leadership of ethical behaviorin business. Academy of Management Executive, 18, 56–66.

Trevino, L., & Youngblood, S. A. (1990). Bad apples in bad barrels: A causal analysis of ethical deci-sion-making behavior. Journal of Applied Psychology, 75, 378–385.

VanSandt, C. V. (2003). The relationship between ethical work climate and moral awareness. Business& Society, 42, 144–153.

Verschoor, C. C. (1998). A study of the link between companies and their suppliers. Journal of BusinessEthics, 17, 1509–1516.

Viader-Cohen, D. (1998). Moral climate in business firms: A conceptual framework for the analysis ofchange. Journal of Business Ethics, 17, 1211–1226.

Victor, B., & Cullen, J. B. (1988). The organizational bases of ethical work climates. AdministrativeScience Quarterly, 33, 101–125.

Weaver, G. R., Trevino, L. K., & Cochran, P. L. (1999). Corporate ethics programs as control systems:Influences of executive commitment and environmental factors. Academy of Management Journal,42, 41–57.

West, M. A. (2002). Sparkling fountains or stagnant ponds: An integrative model of creativity and inno-vation implementation in work groups. Applied Psychology: An International Review, 51, 355–424.

Witt, L. A., & Beorkrem, M. N. (1999). Climate for creative productivity as an organization. CreativityResearch Journal, 2, 30–40.

Zuckerman, H. (1977). The scientific elite. New York: Free Press.

366 MUMFORD ET AL.