Although definitions of this construct have essentially focused on perceived disagreements, others include negative emotions Jehn and Mannix For reasons of conceptual clearness, this study focuses on disagreement about ideas and goals. The literature on the effects of disagreement or task conflict on team processes is inconclusive.
On the one hand, some studies show that facile and uncritical agreement within the team can have a negative impact on problem solving Aldag and Fuller ; Janis , whereas conflict forces individuals to think more deeply and more creatively about the problem they have to solve. Van Offenbeek finds that the more divergent the ideas team members have about the task initially are, the more team members will experience having learned something at the end.
Sauquet concludes that the inability to face conflict in a more global way makes creation of collective knowledge impossible. This is in line with De Dreu , Turner and Pratkanis , and Janis , who argue that conflict suppression reduces individual creativity and decision-making quality in teams.
On the other hand, Stock concludes from a literature review based on 72 empirical studies investigating the antecedents of team performance that both positive and negative effects have been found for task-related conflicts.
De Dreu and Weingart found a strong negative relationship between task conflict and team performance in their meta-analysis of 30 studies.
Although there are no studies available on the relation between disagreement and knowledge sharing activities, such as advice seeking behavior and openness for sharing opinions and suggestions, it is more likely that when team members disagree with each other they will be less inclined to ask and give advice to each other and to be open to hearing the ideas, opinions, and suggestions of team members.
Therefore, it is hypothesized that disagreement is negatively related to asking and giving advice to each other Hypothesis 1a and that disagreement is negatively related to openness for sharing opinions and suggestions Hypothesis 1b. When team members undertake activities together, such as having lunch together, visiting each other at home, or having a drink after work, they get to know each other better and relations become stronger, making the team more cohesive Sanders and Van Emmerik Cohesiveness increases the energy team members can devote to task-related activities because team maintenance needs are reduced.
Highly cohesive teams experience less inter-member friction, higher member trust, and greater interpersonal coordination Dobbins and Zaccaro Furthermore, research shows positive relationships between cohesiveness and employee satisfaction Dobbins and Zaccaro , as well as cohesiveness and cooperative behavior Kidwell et al. Shaw argues that members of highly cohesive teams are likely to be more motivated to achieve established team goals. Based on a survey of managers, Berman et al.
Given the above-mentioned research, it is likely that team cohesiveness plays a role in knowledge sharing within teams, as well. Research from workplace-learning behavior shows that individuals tend to attribute more of their learning to informal support of co-workers than to formal training provided by the organization Maurer et al. In this way, knowledge sharing within a team can be seen as cooperative behavior of team members, which has shown to be affected by the cohesiveness of a team.
As sharing knowledge with other team members is a voluntary and conscious act on the part of an individual Dixon ; Nonaka , involving commitment from both transmitter and receiver Michailova and Hutchings , we hypothesize that cohesiveness in a team is positively related to asking and giving advice Hypothesis 2a and that cohesiveness in a team is positively related to openness for sharing opinions and suggestions Hypothesis 2b.
For the past decade, practitioners have pronounced the importance of knowledge sharing for organizational effectiveness.
Yet, research has only begun to examine the empirical relationship between knowledge sharing and both team and individual performance Druskat and Kayes Although empirical evidence does not consistently support the claim that teams enhance performance Allen and Hecht , some authors have found positive relationships. In this study, we will focus on individual performance instead of team performance.
As it is likely that the performance of individual team members will also benefit from the advice and feedback from their team members Kluger and DeNisi ; Moye and Langfred ; Pearsall and Ellis ; Tindale et al. Summarizing the above hypotheses, we expect that knowledge sharing is an intermediate variable in the relationship between cohesiveness and disagreement, on the one hand, and individual performance, on the other hand.
Consequently, we hypothesize that asking and giving advice mediates the relationship between cohesiveness Hypothesis 4a and disagreement Hypothesis 4b , on the one hand, and individual performance, on the other hand. Furthermore, we hypothesize that openness for sharing opinions and suggestions mediates the relationship between cohesiveness Hypothesis 4c and disagreement Hypothesis 4d , on the one hand, and individual performance, on the other hand.
We employed a cross-sectional design in which we sampled teams from a wide range of organizations both in the public sector three faculties of a university, a ministry, departments of the royal air force, a nursing home, a swimming pool and in the private sector a consultancy firm, some small manufacturing organizations. Although our sample consists of a diversity of teams, all respondents participated in ongoing teams, with long task duration, and involving between 5 and 15 team members.
Questionnaires were distributed to a total of 3, respondents. Due to the fact that some measures were based on team measures, only data from employees for which there were at least five respondents from their team were retained, as past research has indicated that biases in using aggregate scores begin to diminish with groups of five or more employees Bliese As teams with less than five respondents were excluded from our sample, our final sample consisted of 1, respondents from teams in 17 Dutch organizations, with an average group size of The respondents had, on average, The mean total work experience was The respondents worked, on average, Before the questionnaires were distributed, stage meetings were held with the board of directors and the managers of all the organizations see Lambooij et al.
The relevant unions and works councils were also informed of the goals, design, and possible consequences of the research. Thereafter, all employees were informed about the research and the way the data would be collected. In all of the different organizations, the data were collected by master students who used the data from the specific organization for writing a master thesis.
Within all organizations, these students personally handed over the questionnaires and reply envelopes to the employees. The respondents were asked to send the completed questionnaire by means of the reply envelope to the university. Employees were told that data would be collected about their experiences working within a team. Employees answered the questionnaire voluntarily; there were no consequences for answering the questionnaire, neither positive nor negative. The items that comprise the different scales are included in Appendix.
Disagreement was measured with a 3-item scale that is part of the outcome interdependence scale of Van der Vegt et al. For knowledge sharing , two measures were used. First, we measured the extent to which team members ask and give advice to their team members with a self-developed 5-item scale. Secondly, to measure the openness for sharing opinions and suggestions, three items of the scale developed by Costa were used.
For this study, items were recoded so that low scores indicate little openness and high scores indicate high openness. To measure individual performance , we used four items from the subjective performance scale part of organizational citizenship behavior MacKenzie et al.
As various studies have indicated that the positive effects of teams depend on work, worker, and team characteristics Barrick et al.
We have added team size to our analyses, because previous studies Mohammed and Angell ; Steiner ; Wilke and Meertens have shown this variable to be relevant to team effectiveness. For this control variable, we used the number of team members and not the number of respondents from the different teams. The measures of disagreement and cohesiveness that are used in this study refer to characteristics of teams instead of characteristics of individual employees.
For instance, a team is more or less cohesive, not an employee. Asking and giving advice and openness, on the other hand, are interpreted in this study as perceptions of the employees and taken into account on the individual level. The values of ICC1 are similar to what is found in the research literature e. Given the number of groups in the study, we can assume there is enough agreement within groups to make our study feasible. Values of ICC2 above. The ICC2 for disagreement is.
This means that the different scales intended for this study are acceptable on a team level. The dataset consisted of employees nested in teams, which were in turn nested in organizations. Because the variance of the different dependent variables asking and giving advice, openness, and individual performance is hardly related to the organization level ranging from.
This means that the data can be conceptualized at two levels employee and teams. Level 1 captures the information of the employees in each team the two aspects of knowledge sharing for the first analyses and the perceived individual performance for the second analyses , and level 2 captures the variability between teams disagreement and cohesiveness.
In such situations, it is appropriate to use a hierarchical 2-level modeling approach that simultaneously models effects at the within- and between subunit-level Raudenbush and Bryk The data were analyzed using hierarchical regression analyses. First, the mediating variables asking and giving advice and openness were regressed on the independent variables disagreement and cohesiveness. The results of these analyses are presented in Table 2. Second, the dependent variable individual performance was regressed on the independent and mediating variables Table 3.
Table 1 provides the means, standard deviations, and correlations for all variables included in this study. From the correlations with the control variables, we can see that a significant positive correlation exists for both job autonomy and job responsibility with respect to individual performance, asking and giving advice, and openness.
The number of years in the organization relates significantly positive with openness, and significantly negative with disagreement.
Females have significantly higher values for individual performance and for openness. Age relates significantly positive with individual performance and disagreement. Educational level relates significantly negative with individual performance and also significantly negative with disagreement.
Team size relates significantly positive with perceived individual performance, but significantly negative with asking and giving advice, openness, cohesiveness, and disagreement.
The results of the regression analyses with individual performance as dependent variable are reported in Table 3. Hypothesis 4a concerned the mediating role of asking and giving advice in the relationship between cohesiveness and perceived individual performance. Furthermore, Hypothesis 4c concerning the mediating role of openness in the relationship between cohesiveness and perceived individual performance and Hypothesis 4d concerning the mediating role of openness in the relationship between disagreement and individual performance cannot be confirmed as there is no effect from openness on individual performance.
In this study, we investigated the relationships between disagreement and cohesiveness in teams, on the one hand, and knowledge sharing in teams and individual performance, on the other. Although many authors report a positive effect from disagreement on group processes such as knowledge sharing and team learning Ellis et al. The results show that there is a significant negative main effect from disagreement on openness for sharing ideas and suggestions.
Our results are, however, in line with a meta-analysis conducted by De Dreu and Weingart , in which strong and negative instead of the predicted positive correlations between task conflict and team performance and a less negative effect of task conflict on team performance when task conflict and relation conflict were weakly correlated were found.
De Dreu and Weingart suggest that the fact that results showed no differential relationship between type of conflict and team performance might be caused by a measurement problem, since most research included in their analysis relied on the scale developed by Jehn , However, as we used a self-developed scale for disagreement, and, as we found negative effects from this variable on knowledge sharing within teams, this explanation seems less plausible.
More plausible is that high levels of disagreement can stifle knowledge sharing by creating barriers in communication Tjepkema , harming interpersonal understanding Druskat and Kayes , or producing affective conflicts Devine Another explanation might be that the relation between disagreement and knowledge sharing is an inverted U-shaped relationship Stock Studies Jehn ; Stock based on nonlinear models have suggested that both too few and too intensive task-related conflicts reduce team performance.
The same may apply to knowledge sharing: only when there are neither too many nor too few diverging views in a team will each team member be open to new ideas and enter into a cognitive mode that allows for the questioning of assumptions and the generation of new insights Driver Our study did confirm the importance of cohesiveness for knowledge sharing and the importance of knowledge sharing as an intermediate variable between cohesiveness, on the one hand, and team performance, on the other hand.
Our study shows that asking and giving advice within teams can be seen as a pure mediating variable in the relationship between cohesiveness and the individual performance of team members. Two of the remaining papers both address an important philosophical issue through the lens of a detailed scientific case study. David M. Frank argues that while this criticism is sometimes legitimate, in other cases the disagreement is grounded in an ethical difference in opinion that should not be classified as science denialism.
In other instances, however, the disagreement between those who defend the consensus position in invasive biology and those who criticize it is ultimately grounded in a difference in opinion concerning non-epistemic values of various sorts. Frank acknowledges that non-epistemic disagreements of this kind do come with certain risks, but he also suggests that there are other ways in which such disagreements can bring important epistemic and non-epistemic benefits.
Here and elsewhere e. Simply put, a scientific perspective thus includes scientific theories, the evidence for these theories, and the epistemic principles on the basis of which the evidence is taken to support the theories. Massimi puts these ideas to work in analyzing a historical case from theoretical physics at the turn of the twentieth century, viz. Massimi considers how J. According to Massimi, these physicists ended up agreeing on how to answer this question even while employing different justificatory principles on the basis of which they reached their shared conclusion.
They were able to reach this agreement despite their different perspectives, suggests Massimi, because each perspective latches onto a real lawlike dependency that supports counterfactual inferences and thus enables the wielder of the perspective to draw correct, albeit fallible, conclusions from other aspects of their perspective. The remaining four papers in this volume on Disagreement in Science form a natural grouping as they are all concerned with agent - based models of scientific and lay communities.
According to some modellers, e. Douven and de Langhe , being steadfast can increase the likelihood of converging on a true theory. Very roughly, this is because false beliefs spread more quickly in better connected communities. Several philosophers have suggested, in different ways, that it can be epistemically beneficial for a scientific community to be composed of scientists that maintain their position on a given theory even if their total evidence suggests that a competing theory is more likely to be correct.
In particular, it has been argued that scientists should be moved by motives such as whether the theory in question is adopted by other scientists, since that in turn increases the spread of theories that are being explored at a given time. In his paper, Santana first criticizes this idea but then also presents a novel agent-based model that provides a qualified type of support for it.
However, Santana goes on to construct an agent-based model that is designed to test whether the epistemic value of stubbornness can indeed be achieved by other means, viz. Their aim is to investigate a type of polarization that occurs when individuals who disagree about one subject are statistically more likely to disagree about an unrelated subject as well.
The common occurrence of this phenomenon is often explained as being due to some further or more general epistemic commitment, e. This effect occurs not just when the agents start out disagreeing on one issue and subsequently come to disagree on another issue, but also when the agents start out with randomly assigned credences on both issues and update by the mechanism described above.
Furthermore, the same effect occurs when the model is extended to consider three rather than just two unrelated claims simultaneously. Finally, David Anzola examines the role of disagreement in the emergence of a new discipline of computational social science , in which agent-based models are used to study social phenomena. As Anzola points out, a distinctive feature of this new discipline is that it can be defined in terms of its use of a particular method, viz.
Since this method is not used in other disciplines that fall under social science, this creates a tension—a disagreement of sorts—between it and its nearby fields. Although agent-based modelers have in practice so far aligned themselves more with the quantitative methods, and generally steered clear of the theoretical commitments typically involved in qualitative research e.
According to received wisdom, KISS favors simple agent-based models that facilitate understanding at the expense of prediction, while KIDS conversely favors empirically accurate models that facilitate reliable prediction from these models.
Anzola suggests that computational social scientists do not really try to eliminate this divide between KISS and KIDS, which would presumably involve finding a conciliatory position between these extremes. Rather, the two approaches live happily side by side in computational social science.
The papers collected in this special issue indicate the diversity of philosophical questions arising from the well-known phenomenon of disagreement in science. They also open up further avenues of investigations on scientific disagreement and related topics. There has been little explicit discussion of scientific disagreement thus far in the literature on scientific inference and theory choice, suggesting that this might provide a fruitful perspective on such issues.
Although there have been some recent studies on what role such values in fact play e. The four papers on agent-based models also point to important directions for future research. These are just some of the questions for further research that are raised by the papers collected in this volume.
There will doubtless be significant disagreement about how these types of questions should themselves be answered—but that, as we are now acute aware, is par for the course. The disagreements over correct interpretations of quantum mechanics is a notorious case in point see, e. Baghramian, M. London: Routledge. Google Scholar. Beebe, J.
Divergent perspectives on expert disagreement: Preliminary evidence from climate science, climate policy, astrophysics, and public opinion. Environmental Communication, 13, 35— Biddle, J. Climate skepticism and the manufacture of doubt: Can dissent in science be epistemically detrimental? European Journal for Philosophy of Science, 5, — Borg, A. Epistemic effects of scientific interaction: Approaching the question with an argumentative agent-based model.
Historical Social Research, 43, — Christensen, D. Epistemology of disagreement: The good news. Philosophical Review, , — Disagreement as evidence: The epistemology of controversy.
Philosophy Compass, 4, — Higher-order evidence. Philosophy and Phenomenological Research, 81, — Cretu, A. Diagnosing disagreements: The authentication of the positron — De Cruz, H. The value of epistemic disagreement in scientific practice. The case of Homo floresiensis. For example, different techniques to isolate chromatin accessible to binding by regulatory factors, paired with alternative sequencing techniques, may shed more light on the binding preferences of certain TFs.
Alternatively, refined computational tools may be better able to detect footprints. In a Nature Methods Analysis currently available online, Costa and colleagues compare ten methods for footprint analysis and provide guidance as to the strengths and weaknesses of each.
The authors also point to the need for further improvements to discern footprints from TFs with short residency times. Dissenting opinions can bring to light confirmation bias and prompt researchers to take a second look at evidence that is not in agreement with their hypothesis, rather than dismiss it as artifacts. As researchers working in cognitive science have observed, we tend to provide better arguments when we make a case against an opponent than when we present an unopposed finding Behav.
Brain Sci. In dialogue with those who hold opposing views, scientists have the opportunity to reexamine their hypotheses and the evidence, find new methods, and put forward a convincing case that moves us one step closer to answering puzzling biological questions. Reprints and Permissions. The power of disagreement. Nat Methods 13, Download citation. Sperber and Mercier argue that, looked at through the interactionist lens, confirmation bias is actually a feature, not a bug of human cognition.
It maximises the contribution that each individual makes to a group, by motivating them to generate new information and new arguments. You might be doing so for selfish or emotional reasons — to justify yourself or prove how smart you are. When you bring your opinions to the table and I bring mine, and we both feel compelled to make the best case we can, the answers that emerge will be stronger for having been forged in the crucible of our disagreement.
When a company is considering a takeover bid, it often hires an investment banking firm to advise on the acquisition. But this raises a conflict of interest: the bankers have a strong incentive to persuade the board to do the deal. After all: no deal, no fee. The genius of this approach lies in the fee. Because by doing so, the directors can harness the power of biased thinking, even as they guard against their own.
The second advisor is now strongly motivated to think of as many good reasons as it can that the deal should not go through. The board will then have generated a set of arguments for and a set of arguments against, and be in a stronger position to make the right call.
Tribalism — the desire to see our group win — is usually portrayed, for good reason, as the enemy of reasoned thought. But it can also be an aid to it. In , a team of scientists led by James Evans, a sociologist at the University of Chicago, published their study of a vast database of disagreements: the edits made to Wikipedia pages.
But their arguments improved the quality of the resulting page. When everyone feels compelled to generate arguments and knock down competing arguments, the weakest arguments get dismissed while the strongest arguments survive, bolstered with more evidence and better reasons. The result is a deeper and more rigorous process of reasoning than any one person could have carried out alone.
By allowing their arguments to run hot, the Wrights were able to beat all the experts in the world. Open and wholehearted argument can raise the collective intelligence of a group, but the chemistry of a disagreement is inherently unstable. That kind of debate can be enormously productive; it can also, of course, tip over into an ego battle that generates more heat than light.
The first condition, of course, is to openly disagree. The members of the group must bring their own opinions and insights to the table, rather than just adopting those of whomever they like the most or nodding along with the dominant voices in the room. The more diverse the pool of reasons and information, the greater the chance of truly powerful arguments emerging.
A second condition is that the debate should be allowed to become passionate without becoming a shouting match. How did the Wrights get hot without getting mad? The tougher that Wilbur and Orville fought, the more intently they listened. Good listening can be a function of close and respectful personal relationships, as in the case of the Wrights, or from tightly structured discussions that force everyone to attend to other viewpoints, as in the case of the Wikipedians.
Third, the members of the group must share a common goal — whether that be solving a puzzle, making a great Wikipedia page, or figuring out how to get a plane in the air and keep it there.
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