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Team Performance Management: An International Journal Clean up your network: how a strike changed the social networks of a working team Kirsten Thommes, Agnes Akkerman,

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Clean up your network: how a strike changed the social networks of a working team Kirsten Thommes Institute of Management and Economics, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany, and

Agnes Akkerman

Clean up your network

43 Received 23 June 2017 Revised 26 September 2017 26 September 2017 Accepted 27 September 2017


Department of Economics and Business Economics, Radboud University, Nijmegen, The Netherlands

Abstract Purpose – This paper aims to analyse the impact of an intra-team conflict on the social relations within a team. The team conflict was triggered by a strike action which separated the team in two groups, the strikers and the worker, who continued to work. After the strike was settled, all had to work again cooperatively. This paper analyses how the strike action affects work and private social networks among workers. Design/methodology/approach – The authors combine a qualitative ethnographic approach with quantitative network data. Findings – The authors find that the strike action led to a separation between the former group of strikers and non-strikers. While the subgroups become more cohesive and their social network density increased, the links between both groups diminished. Research limitations/implications – This study reveals that strikes and the accompanying separation of the workforce can improve social relations within the team, if individuals behaved alike during the conflict. Practical implications – For managers, the results raise questions concerning typical managerial behaviour during strikes, as managers frequently trigger separation by trying to convince some individuals to continue to work. Instead, groups may even improve their performance after a strike, if they were allowed to behave alike by all joining the strike or refraining. Originality/value – This study is the first to analyse social relations after a conflict. The authors combine qualitative and quantitative data and show the evolution of a social network after a strike. Moreover, they separate private communication flows and work-related communication and show that both networks do not necessarily evolve equally after a conflict.

Keywords Social interaction, Team working, Conflict Paper type Research paper

Introduction In many teamwork situations, conflicts can arise among employees (Marques Santos and Margarida Passos, 2013; Leung, 2008) and between management and employees (Teague and Roche, 2012). Even when the fault line of a conflict is between employees and management, not all employees necessarily agree with the view of the employee-side or a willing to support their fellow employees during the conflict, which again may trigger conflicts among sub-groups of employees. This article reports on the relational consequences of strikes. During strikes, workforces are regularly falling apart as some employees join the strike action while others stay at work (Born et al., 2013, 2016). Our study shows how strikes segregate the workforce between those who participated in the strike and

Team Performance Management: An International Journal Vol. 24 No. 1/2, 2018 pp. 43-63 © Emerald Publishing Limited 1352-7592 DOI 10.1108/TPM-06-2017-0031

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those who did not. Our case study is an example of the perseverance of such segregation, months after the strike was officially settled. Although most strikes are resolved within a few days, the aftermath of strikes is often longlasting and involves personal and relational costs that may have profound long-term effects on production. Research concerned with the economic consequences of industrial conflicts finds that strikes reverberate (Gruber and Kleiner, 2010; Addison and Teixeira, 2009; Krueger and Mas, 2004; Mas, 2008). These studies mention obstruction and cooperation problems as probable causes of suboptimal production after strikes. Psychological studies report prolonged effects of strikes on workers’ psychological health and job satisfaction (Barling and Milligan, 1987; Kelloway et al., 1993; Fowler et al., 2009). They also show that strikes have strong negative effects on occupational health and indicate that employees experience stress long after the dispute – the stressor – was settled. Fowler et al. (2009) suggest that social factors are responsible for continuation of stress. Several case studies of severe strikes reveal strong and destructive cleavages between management and employees, as well as between groups of employees (MacDowell, 1993) and even overt hostilities, such as physical and verbal harassment, that persist after dispute settlement (Francis, 1985; Brunsden and Hill, 2009; Waddington et al., 1994). Strike-breakers are put aside as blacklegs or scabs in union terminology, effectively splitting teams, organizations and sometimes whole communities (Francis, 1985; Getman and Marshall, 1993; Waddington et al., 1994; Getman, 1999). In an indepth case study of the labour conflict between International Paper Company and the union in the USA in 1987, which resulted in a 16-month strike, Getman and Marshall (1993) report severe consequences for social cohesion after the strike: I saw one brother on one side of the coin and the other as a striker and they literally would fight each other because one was working for the company and one was not [. . . .] The company pitted one against the other and some of this is never going to go away. [. . .] (mill worker quoted in Getman and Marshall, 1993, p. 1844 et seq.)

Getman and Marshall further describe “[d]uring the initial post-strike period, the former strikers limited their social interaction almost exclusively to each other [the former strikers]” (Getman and Marshall, 1993, p. 1836). The lack of solidarity demonstrated by strikebreakers induces sentiments of betrayal in those who strike, thereby creating sharp fault lines between employees. Over time, conflicting groups on each side become more cohesive internally and develop negative stereotypes of the other group, triggering polarization, disruptive conflicts (Nelson, 1989) and incivilities, which might result in aggression (Andersson and Pearson, 1999; Pearson et al., 2001; Kelliher, 2017). The ultimate result is persisting social cleavages and severe productivity losses over a long period. However, as the case studies illustrate, segregation of networks of strikers and non-strikers is not complete: they still interact and probably need to interact to do their jobs and what is more important, non-strikers fail to escape punishment by cutting network ties with disapproving actors. Thus, it is not always possible for actors to change their ties and the breaking of ties does not necessarily prevent punishment. To manage teams and organizations effectively, then, we need to understand why and how social relations are altered after strikes. The present social network study is a case study of a cleaning brigade that faced a collective action problem when their union called for a strike. Some of the cleaners joined the strike while others refrained from the strike and continued working. With our strategic network approach, we provide a theoretical explanation for the sometimes-persistent cleavages within teams and in organizations after labour conflicts. Moreover, we add to this explanation the condition of elasticity – the changeability of workers’ social networks – and explain that some ties are more adaptable to optimization of the network than others are,


depending on the discretion individuals have in changing their ties. Our study provides a unique opportunity to study strategic network changes under differing network elasticity. To the best of our knowledge, our case study is the first systematic empirical test of such a condition with real-life data. Network elasticity is operationalized by comparing network changes in two different relations. The cleaners in our network study maintained work-related ties and private ties. We expect that work-related ties are costlier to delete, as their deletion may cause cooperation problems, which in turn may lead to employer reprimands and eventually to unemployment. On the contrary, deleting private ties will – generally speaking – be less consequential with regard to the responsibilities and obligations required by the organization. Put differently, a worker faces less discretion to establish and break social relations with respect to her work-related network, while she has much more freedom of choice with regard to establishing and breaking private ties. The discretion available to construct one’s network structure in a social system is known as “network elasticity” (Lazer, 2001). Our study makes several contributions to social network research, industrial and labour relations theory and the team literature. Our study contributes to social network theory by testing a theory on strategic network change. We use a real-world collective action problem and are therefore able to test our hypotheses with real-life data. Second, we are able to test changes in the intensity of ties. This enables us to conduct a more refined investigation: in addition to the previously investigated tie deletion and tie creation, we are able to study tie intensification and tie de-intensification. Third, we find that there are different levels of elasticity of certain relations within the same network, suggesting that elasticity is a network attribute on the level of ties, in addition to being a characteristic of a network as a whole (Lazer, 2001). With respect to the labour relations literature, we contribute by filling the gap between strikes and later observed productivity losses. We argue that altered social relations after the strikes can explain the productivity losses. Strikes trigger network dynamics resulting from an individual’s desire to maximize social approval (or more specifically, avoid social disapproval). We contribute to a deeper understanding of long-lasting segregation between groups of employees after a strike. Thus, our social network study contributes to labour relations theory by providing and testing a theoretical mechanism that explains communication problems and productivity losses after labour conflicts. Finally, we also contribute to the diversity stream in the team literature: employees’ decisions to participate or not to participate in a strike are a result of deep-level diversity, which is defined as “differences among members’ attitudes, beliefs and values” (Harrison et al., 1998, p. 98). In most studies, deep-level diversity is found to be one of the most relevant predictors for team effectiveness (Phillips et al., 2006; Guillaume et al., 2012; Meyer and Glenz, 2013; Shemla et al., 2016). However, in most studies, deep-level diversity characteristics are assumed to be stable over time and equally relevant at all times. Contrary to this between-subject design applied by most studies, we observe a situation in which deep-level characteristics become very central and observe the accompanying changes in social relations. Our study informs the team literature that the importance of deep-level characteristics may vary over time. Theory Strikes, free-riders and network ties In teams and other groups, social control is executed to influence the behaviour of others through group norms. Actors who behave similarly to the group norm are rewarded and actors who behave differently from the focal actor are punished (Takács et al., 2008). This mechanism is at play when team members’ behaviour affects the well-being or the utility of the group and its members, especially during public good dilemmas (Heckathorn, 1990; Leibbrandt et al., 2015). Strikes represent a public good dilemma for a team because strikers


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incur considerable costs (e.g. loss of income) and risk future repercussions by the employer, while the gains of a strike usually apply to every employee. Thus, the potential gains of a strike are non-exclusive. Therefore, a rational individual (homo economicus) would choose to free ride on the efforts and costs of their striking colleagues. However, during strikes, individual behaviour is particularly easy to judge, as everyone crossing the picket line can be observed. Those who continue working are considered free-riders by team members who hold the norm that team members should contribute to the strike. On the other hand, team members who disagree with that norm may judge the strikers for not contributing to the team’s tasks while they are on strike. Thus, behavioural diversity between subgroups in terms of resisting against the management might cause great clashes between subgroups. Diversity in terms of norms and beliefs is usually found to have negative consequences for social cohesion among teams (Guillaume et al., 2012; Meyer et al., 2016). In terms of the social network within a work team, norm establishment and enforcement as well as trust are coordination mechanisms that necessarily rely on a network structure to emerge and directly affect a network’s productivity (Granovetter, 2005). Following these ideas, we argue that the network dynamics resulting from an individual’s desire to maximize social approval (or more specifically, avoid social disapproval) contribute to a deeper understanding of longlasting segregation between groups of employees after a strike. Maximizing social approval, minimizing disapproval If an actor aims to maximize utility resulting from group membership, one potential strategy is to comply with the group norm to get social approval and avoid social disapproval from the other group members. Another strategy is to change the network by trying to create ties with members of a group that approves their own behaviour (Kitts et al., 1999). Breaking a tie with a team member can be regarded as a strategic action of punishment to enforce norms as well as a means to maximize individual utility by minimizing contact with nonconforming others. Vice versa, establishing ties with others who behaved similar to one’s own action can be regarded as a reward for complying with the norm and at the same time as maximizing one’s own utility by receiving approval for the choice of action. An individual’s utility resulting from group membership is especially subject to change if his action is subject to approval or disapproval by the other group members. When faced with a collective action problem, all individuals in a group have to decide about a behavioural action that will also change the utility of group membership. When one or more group members deviate from the group norm on what behavioural action is preferred, severe changes in the composition of group membership can be expected. Therefore, individual utility does not depend solely on one’s own individual action, but on an individual’s action in relation to the (preferred) action of the relevant others in the group (Takács et al., 2008). Because of the individual utility changes caused by social approval or disapproval that an individual gets out of relations with group members, “[p]eople might choose their structural relations strategically in order to maximize rewards and minimize punishment” (Takács et al., 2008, p.178), which is an individual’s strategy to maximize utility from the network. Contrary to approaches in which behaviour is seen as a result of (network) structure (Ibarra and Andrews, 1993; Rice and Aydin, 1991; Giguère and Lalonde, 2010), the core idea is that structure is also a result of behaviour and, therefore, decisions give rise to change the (network) structure. Thereby, social control in terms of possible rewards and punishments after a strike can cause changes in intra-team relations. “In order to avoid unpleasant influence and to enjoy more rewards of social control, individuals might strategically revise their network relations” (Kitts et al., 1999, p. 130; see also Takács et al., 2008, p. 185). We, therefore, argue that adaptations of the intragroup links in social networks

are guided by an actor’s own and other group members’ behaviour during the strike. We hypothesize that an individual’s decision to participate in the strike and the consequential punishment of defection from the group norm cause changes in the employees’ network structure. Different and similar behaviour of two actors with respect to the strike will alter their relations. Based on these considerations, our working hypotheses are as follows:


H1. Actors cut links to people who chose the opposite behaviour to theirs during the strike. H2. Actors weaken links to other subjects who chose the opposite behaviour to theirs during the strike. H3. Actors strengthen their ties to other subjects who behaved like they did during the strike.


H4. Actors establish links to other subjects who behaved like they did during the strike. However, not all network relations are equally easy to change. Especially in work relations, employees probably do not have the authority to change team composition autonomously. We argue that while it may be easier to establish new work-related relations, it will be difficult, if not impossible, to disengage cooperation with colleagues totally. In other words, work-related networks might not be infinitely elastic. We, therefore, argue that strategic network adaption depends on network elasticity. In order to test our theoretical argument on network elasticity, we analyse two networks among the same group of people, but of potentially different elasticities. While in the private network ties can be managed by the subject relatively autonomously and are subject to an individual’s choice, work-related communication ties cannot be changed at an employee’s free will as they are subject to the organization of work responsibilities and obligations regarding with whom one must cooperate and communicate. Therefore, we expect more severe changes in the private network than in the work-related network. H5. The changes in the private communication network are larger than in the work communication network. Clean enough! The case study Background Our study was conducted with employees of a cleaning company that was affected by a sector strike in 2012 under the motto “Clean enough!” (Schoon genoeg!, which is a figure of speech for “We’ve had enough!”). The strike lasted 105 days (from 2 January 2012 to 17 April 2012) and was the longest strike in The Netherlands since 1933. The strike’s goals included a wage increase of about 5 per cent and it was labelled a strike for respect; compared to the national minimum wage, even experienced and older cleaners in The Netherlands earned only a maximum of about 120 per cent of the minimum wage (= 10.59e per hour compared to the 8.72e per hour national minimum wage in 2011). Further, the collective agreement transferred some risks to the employees: in times of sickness, the first two days of absenteeism were not compensated by the insurance company or the employer. Employees and unions wanted to change especially this part of the former collective agreement and labelled the issue about the payments in time of sickness an issue of “respect”. They asked for the same regulations that apply to almost all other Dutch employees, who receive their whole salary during the first days of sickness absenteeism. The strike resulted in a 4.85 per cent increase in salaries, better training and a general agreement for supervision of cleaners concerning their stress at work and improved working conditions for temporary agency workers. Further, employer and union agreed to set up an experiment and to pay the first days of sickness leaves for a certain period.

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Employers agreed to choose 6,000 workplaces for a trial period of one year to find out how absenteeism and sick leave would develop (CAO, 2012). The cleaning firm participating in the study belongs to one of the five biggest cleaning companies in The Netherlands. In particular, one on-site managed cleaning brigade in one Dutch firm was analysed. To ensure anonymity of the affected parties, the firm will be called “Office”. At the time of the study, the cleaning company at Office provided an exceptional working sphere compared to other cleaning companies. While fluctuation is usually pretty high in the cleaning sector, at about 15 to 35 per cent each year (EFCI, 2010; FNV Bondgenoten, 2011), the fluctuation rate at the cleaning company analysed was below 5 per cent. In total, 60 employees worked at the cleaning company at Office and six additional employees were hired via a temporary employment agency. Cleaners are organized in five teams: four teams are responsible for particular respective groups of buildings; one team serves as a cross-departmental brigade and operates heavy electrical machines and devices. Mostly, individual cleaners have specific tasks, e.g. clean certain rooms on their own routes. However, most buildings also require some teamwork in the sense that halls or other bigger conference rooms are cleaned up jointly by several cleaners. Further, cleaners reported that they frequently help each other out and sometimes have to take over each other’s tasks if actual circumstances are extraordinarily troublesome for someone, as for instance in the case of extraordinarily messy offices in some parts of the building. All five teams have established common daily routines; to coordinate joint tasks, all meet on a daily basis, for example, for a coffee break, to divide joint tasks among team members, discuss interaction with the customers and share information about their work, work circumstances and requirements. Thus, although every cleaner can fulfil most of the tasks independently, some coordination and cooperation are required. The strike and its aftermath Punishment and group segregation The strike caused changes in the social relations between the cleaners. While usually the strike is considered to be a public good, especially strikers would have a reason to punish their free-riding colleagues who benefit from the strike yields. However, in Office, the nonstrikers also found reason to punish their striking colleagues because they felt the strikers were free-riding and enjoying free time while the non-strikers had to continue to work. This is mainly because the cleaners work in teams and simply had to put more effort into cleaning up as some of the rooms were only cleaned up half as frequently as usual. The strikers were said to have met only once a week and no real strike action was visible. This caused many negative emotions amongst the non-strikers: But the thing is: They are not really striking! They are meeting once a week BEHIND the [OfficeBuilding] and that’s it. They do absolutely nothing! (non-striker at t1) I told them that I don’t agree with their way of striking: They should be present, visible to all colleagues and the client’s employees. Instead, I haven’t seen them for the last weeks. (non-striker at t1)

Before the strike, it was a general habit at most places to come together for a coffee break. After the strike, the former strikers refused to participate because they said they had too much work and no time for breaks anymore. Since they [the strikers] are back, things are different: We don’t meet anymore. The strikers think that they have to make up for the time they were absent. They are complaining that we didn’t work enough during their absence and that they now have more work than before. (non-striker at t2)

We argue a lot since they [the strikers] are back. They reproach us not to have worked enough. Can you imagine? They were the ones sitting at home and enjoying their four-month holidays!” (non-striker at t2)


Look, of course it is easier to clean an office when it was cleaned up yesterday. But since we were out of work for some weeks, nobody took over my workplace. I have expected that my colleagues would show at least some solidarity and either have taken over while I was fighting for our rights or help me out now. Instead, they are just pointing fingers [. . .] this is not a giving and receiving anymore [. . .]. (striker at t2)

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They accuse us not to have worked! They simply don’t get it [. . . .] If someone comes from a foreign country and simply does not understand [. . .] ok. But Dutch citizens should know their rights and understand strike! I don’t get it! (striker at t2)

At the two places with mixed participation in the strike, the fault lines between former strikers and non-strikers became more sclerotic over time and even five months after the strike, both groups avoided shared coffee breaks. However, not only worsened social relations became apparent after the strike but also the first lines of sabotage. After the strike, it became apparent how the strike influenced the working sphere in the cleaning brigade. Two sorts of problems became visible: first, older interpersonal conflicts revived and second, new problems emerged in the two groups consisting of strikers and non-strikers. Concerning the revived conflicts, cleaners complained about missing products. The cleaners at Office tried to establish some rules of work behaviour. Most cleaners had their own cleaning trolley and did not want others to use their trolley or cleaning materials stored on the trolley. However, from time to time, conflicts arose when cleaners accused each other of having stolen cleaning materials from their trolley. The manager suspected these conflicts were not really work-related conflicts, but a vehicle for frustration and interpersonal conflicts instead. He tried to solve the situation by enabling cleaners to lock their cleaning materials when they went home. After the strike, the old conflict popped up again: cleaners accused each other of having stolen cleaning materials from their trolleys. Some people were accused of hoarding materials in their lockers. This conflict especially manifested itself at a place where half of the group was on strike. The former strikers accused a former non-striker of sabotaging them by stealing cleaning tools and hoarding cleaning materials in her cabinet. Every time I come to work, I have to go to the cleaning tools storage and refill my trolley. . .. This is sabotage and I know who is responsible for this. . .. It is [name of a former non-striker], because she is jealous. [She is jealous because] she was afraid of participating in the strike and I was brave enough to join it. (striker at t2)

The description of the work climate during and after the strike illustrates how differences in participation were the starting point for changes in network ties and sparked free-rider punishment by strikers as well as non-strikers. Data collection and measurement According to the managers’ information, of the 66 cleaners at the cleaning company at Office, 14 participated in the strike. However, two of them stopped while the strike was still going on and returned to work after 8 and 12 weeks, respectively. Semi-structured interviews were conducted at two points: the first wave of interviews was conducted from 19 March 2012 until 24 April[1]. Fifty-nine employees were interviewed while seven cleaners refused to participate. Among the cleaners who responded, 45 did not participate in the strike action, 13 participated and one first participated and then returned to work before the strike was settled. Fourteen cleaners were male, 45 cleaners were female. The average age was 41.36 years, 76 per cent of all

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interviewed cleaners were born in The Netherlands and 24 per cent were born outside The Netherlands (the main countries of origin were Turkey, Morocco and Poland). The second wave of interviews was conducted two months after the strike. All 59 employees from the first wave were interviewed again. The second wave of interviews took place between 25 June and 13 July 2012. Most questions from the questionnaire were asked again to be able to compare answers. The questionnaire consisted of polar and open questions. In both waves, we collected polar questions on demographics (e.g. age, gender, education), employment experiences and work (e.g. tenure, working hours, deployment site) and strike participation (e.g. whether the respondent participated). Moreover, we surveyed the network data in polar questions by giving the respondents a list of all co-workers and asking them to judge their private and work-related communication with each on a scale from 0 (no interaction at all) to 7 (very frequent interaction). We also included open questions on their attitude and feelings about the strike action and their social relations at work by asking them how they felt about the strike and how they would characterize their private and work relations. In addition to these two waves of interviews, we observed two staff meetings after the strike. These meetings were set up by the manager to settle conflicts after the strike. The meetings took place on 19 April 2012, which was the day of the cleaners’ return to their workplace and on 15 May 2012. The authors were not allowed to record the meetings; however, field notes were written down. Although these qualitative data are not suitable for an in-depth case study analysis, we use these field notes and also the open questions to illustrate the general work climate during and after the strike before we turn to the quantitative network analysis. Findings Network analysis – measurements All cleaners were personally invited to participate in the study and personally interviewed wrote down their answers with a standardized questionnaire. At the first measurement point, we asked respondents about their work situation three months before the strike, to anchor their reports (which would reflect the situation in October 2011); at the second measurement point, we asked for their working situation after the strike, which reflects the situation in June-July 2012. Concerning the possible network relations, we first openly asked which of their fellow colleagues from a list of all cleaners the respondent had contact with and had talked about either work or private issues. After having received the full list of contacts, the intensity of contacts was ascertained. Private ties were measured by asking the respondent, “How often do you discuss private matters with Alter?” Work-related ties were measured by asking the respondent, “How often do you discuss work-related matters with Alter?” Both ties were measured between every potential dyad of actors and could take on a value between “1” (= minor communication) and “7” (= very frequent communication). Non-existing ties were coded with “0”. Further, the questionnaires contained questions about: individual characteristics like gender, education and age; organizational embeddedness like working position, tasks, conflict and cohesion within the group and with the supervisor; strike behaviour, attitudes and (participation) norms; and network questions.


Network analysis – results In the next step, we undertake an analysis of network changes after the strike on individual, dyad and group levels. Our starting points are the networks as displayed in Figures 1 to 4, which show the communication networks in the cleaning firm before and after the strike. In the graphs, we show the directed network connections valued equal to or higher than “3”, as this value was found to serve as a barrier separating the non-existing or less intense communication and more intense communication.


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Work ties The graph in Figure 1 shows that there are quite a number (9) of isolates in the work-related communication network. Broadly speaking, two subgroups of future strikers can be distinguished in the before strike network. These groups are connected by a future nonstriker who is a linking pin between a smaller network (of future strikers) and a larger network of both future strikers and non-strikers. Two members of the larger network (#40 and #28), will go on strike without having yet established membership in the group of future strikers. Note that the future converted striker is only connected to the smaller network of future strikers through linking #26. Figure 2 shows the work communication network two months after the strike was formally settled. Visual inspection already shows changes in the network. The converted striker is now an isolate, while some of the isolates have started talking to each other. Moreover, we observe that cleaners now have ties with colleagues who did not exist before the strike. For example, Cleaner #40 (a former striker) now has a direct tie to #53, also a former striker. Former ‘isolated strikers’ #40 and #28 now have work-related ties to the other strikers. Thus, former strikers have created new ties to former strikers. As for the deletion of ties, the strike has not (yet) led to a total deletion of ties between strikers and non-strikers. Private ties We also analyse the private communication between strikers. Our starting points are the networks before (Figure 3) and after (Figure 4) the strike.

Figure 1. Work-related communication before strike

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Figure 2. Work-related communication two months after strike

Figure 3. Private communication before strike

Figure 3 shows that there were two distinct private communication networks before the strike: a large network and a smaller network, both consisting of future strikers and future non-strikers. While after the strike the larger network still consists of former strikers and non-strikers, the smaller private communication network now consists of former strikers exclusively. As for the larger private communication network, we observe that strikers established ties with other strikers after the strike. The number of isolates increased from 11 to 15 after the strike and we can clearly observe one cluster of former strikers having cut all links (at least all links above “3”) to others. The cohesive network of former strikers in the upper



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Figure 4. Private communication after strike

right corner of the network graph is one of the main reasons for the increase of isolates as six of the isolates (25, 26, 32, 33, 27, 39) at the second measurement point were formerly connected to this network. However, they lost all links not only to the former strikers but also among each other (e.g. the connections between #26-37-32 and #33-39). Concerning the descriptive network statistics of our networks, the following table gives an overview. The whole network shows a relatively low density[2] that can be mainly explained by the five different teams, which seldom met each other at work. Concerning private communication, the density even decreases after the strike, while it increases for work-related communication. If we analyse the direction of the ties, we see that we have a relatively high rate of reciprocity: at least 74.67 per cent of all ties are reciprocal. The level of reciprocity increases for private communication after the strike and decreases for work-related communication. When analysing the density of work-related communication within and between the two subgroups (Table I), we see that the in-group density in both in-groups increased after the strike while the out-group communication decreased. Simultaneously, reciprocity towards the out-group decreases over time. If we analyse the subgroups concerning private communication (Table II), we find that the in-group density for strikers increased after the strike, while out-group communication from striker to non-striker and from non-striker to striker decreased after the strike. The re-scaled EI-Index is always significant. Concerning the private communication network, the overall EI-Index in t1 was 0.397 (non-striker: 0.612, striker: 0.342) and in t2 the EI-Index was 0.489 (non-striker: 0.674, striker: 0.174) suggesting that the share of ingroup communication increased after the strike. Concerning work communication, the EI-index in t1 was 0.399 (striker: 0.606, non-striker: 0.263) and in t2 it was 0.444 (striker: 0.641, non-striker: 0.244), suggesting as well that the share of in-group communication increased after the strike. To test our hypotheses, we run a probit regression analysis to analyse the probabilities that a dyad of two cleaners in the network (1) established, (2) intensified, (3) restricted or (4) dropped a tie with another person in the network. Our main explanatory variable to explain

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network changes is a difference in strike behaviour (1 = same strike behaviour, 0 = different strike behaviour). As we analyse directed ties, we have 59*58 (= 3422) cases in our data set. We, therefore, take into account that a link between two actors can be dropped from one actor’s point of view while it is only established recently from the other actor’s point of view. As controls, we include two different sets of variables. We follow the recommendation by Snijders et al. (2010) and include outdegree, transitivity and reciprocity at measurement t1 in our analysis. Outdegree reflects the basic tendency of one actor to have ties. Transitivity measures the number of intermediaries forming transitive triples. Reciprocity measures the number of reciprocated ties by one actor. Further, we include those control variables, which are usually used to explain diversity and, therefore, might impact changes in network relations. We control for endogenous social structure by controlling for similarities by making binary variables that indicate whether the actors in the dyads are similar (1) or not (0). We did this for gender and nationality (same country of birth = 1). During our survey, we learned that some of the buildings are spatially relatively distant from each other so that two cleaners in different buildings might never meet each other at work. Therefore, we also control for spatial propinquity (1 = same building). Table III gives an overview of the variables used in the analysis. The following tables display our results. Table IV contains the results for network changes concerning work-related communication and Table V contains network changes with respect to private communication ties. Our regression analysis confirms that the strike had a major impact on the network communication structure. Table IV reports the regression analysis concerning work-related communication. Same behaviour during the strike, which means that the actors in a dyad either both participated in the strike or both did not join the strike, significantly increases the probability that new network relations between them are established. If a relation already existed, same behaviour during the strike also significantly increases the likelihood that the actors will intensify their work-related communication after the strike. Further, similar actors are significantly less likely to decrease their level of interaction. However, similarity in strike behaviour has no significant effect on tie deletion. Concerning workrelated network ties, we can confirm our H1-H3 when controlling for network characteristics and other sources of actor similarity. The hypothesis that dissimilar actors are more likely to delete ties cannot be confirmed for the work-related network. Although we

t1

Table I. Descriptive statistics concerning workrelated communication

t2

Individual

Non-striker

Striker

Non-striker

Striker

Non-striker Striker Non-striker Striker

0.151 0.135 0.7043 0.9242

0.135 0.269 0.9104 0.9500

0.185 0.123 0.7113 0.8929

0.123 0.308 0.6849 0.9091

Individual

Non-striker

Striker

Non-striker

Striker

Non-striker Striker Non-striker Striker

0.212 0.168 0.7156 0.9437

0.168 0.308 0.7128 0.9787

0.184 0.121 0.7619 0.8448

0.121 0.321 0.7903 0.9362

t1

Table II. Descriptive statistics concerning private communication

t2


Variable

Mean

SD

Minimum

Maximum

Work New tie Dropped tie More intense tie Less intense tie Outdegree Transitivity Reciprocity Same sex Same origin Same place Same strike

0.0248393 0.0111046 0.0286382 0.0295149 20.62712 1.825322 0.2215593 0.6136762 0.6317943 0.9722385 0.6241964

0.1556578 0.104807 0.1668119 0.1692695 16.64819 1.099072 0.2310658 0.4869774 0.4823881 0.1643129 0.4844005

0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 92 4.167 1 1 1 1 1

Private New tie Dropped tie More intense tie Less intense tie Outdegree Transitivity Reciprocity Same sex Same origin Same place Same strike

0.0131502 0.037405 0.047633 0.0330216 22.40678 1.462288 0.2089831 0.6136762 0.6317943 0.9722385 0.6241964

0.1139345 0.1897799 0.2130195 0.1787191 18.76632 0.6977747 0.1880106 0.4869774 0.4823881 0.1643129 0.4844005

0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 92 3.067 0.625 1 1 1 1

got a negative sign indicating that same behaviour during the strike decreases the probability of tie deletion, the effect is not significant. We assume that this is because tie deletion is more difficult to achieve for actors in their work network, as some interaction is required, even if actors do not prefer to interact. Table V reports the regression results of the private communication network flows. Again, we can confirm most of our hypotheses: similar actors are more likely to establish links after the strike and are further more likely to intensify already existing links after the strike than dissimilar actors. Consequently, the probabilities that network ties are deleted are significantly more likely if two actors behaved differently during the strike. The only exception we find is the probability of decreasing a tie. Although we find a negative causal relationship implying that similar actors are less likely to decrease contact intensity after the strike, this relation is not significant. Therefore, we can only confirm H1, H2 and H4 for the private network. Our explanation for the non-significance of decreasing network ties would be that the private network can be managed by the actors more freely and, therefore, dissimilarity might lead directly to link deletion instead of decreasing intensity. This “all-ornothing” strategy might not be open to actors in their work-related network, as work circumstances might not allow tie deletion to the same extent as private networks. Our results suggest, therefore, that private networks are much more subject to actors’ attempts to maximize their network utility and the dynamics of network changes evolve more across the fault line of dissimilar behaviour during the strike. If we compare our results concerning the private and the work-related communication networks, our results overall confirm that private communication networks are more subject to a maximization of utility behaviour than work-related networks. Actors engage in tie


55

Table III. Descriptive statistics (N = 3422)

Table IV. Changes in workrelated communication

1.472*** (0.244) 3422 378.01238 40.05 0.0000

0.00359 (0.00319) 0.0527 (0.0442) 0.944*** (0.283) 0.234* (0.104) 0.547** (0.193)

More

More

Less

Less

0.00292 (0.00316) 0.0111*** (0.00229) 0.0114*** (0.00231) 0.0175*** (0.00214) 0.0174*** (0.00215) 0.0426 (0.0446) 0.0898* (0.0439) 0.0654 (0.0446) 0.0188 (0.0465) 0.00504 (0.0471) 0.761** (0.280) 0.0366 (0.207) 0.181 (0.211) 0.689* (0.301) 0.829** (0.315) 0.249* (0.106) 0.354*** (0.101) 0.361*** (0.102) 0.0910 (0.0967) 0.0848 (0.0970) 0.503* (0.198) 0.0151 (0.268) 0.0538 (0.272) 0.224 (0.237) 0.252 (0.237) 0.607*** (0.134) 0.408*** (0.107) 0.201* (0.0955) 2.062*** (0.284) 2.303*** (0.307) 2.720*** (0.331) 2.069*** (0.278) 1.861*** (0.293) 3422 3422 3422 3422 3422 365.32835 427.0567 419.07621 416.50746 414.31209 65.42 35.44 51.40 77.59 81.98 0.0000 0.0000 0.0000 0.0000 0.0000

New

Notes: *p < 0.1; **p < 0.05; ***p < 0.01

Outdegree Transitivity Reciprocity Same sex Same place Same strike Constant N Log likelihood LR x 2 Prob > x 2

New

2.206*** (0.421) 3422 200.20428 17.20 0.0086

0.00525 (0.00327) 0.0833 (0.0609) 0.425 (0.385) 0.417** (0.130) 0.0901 (0.376)

Dropped

56

Variables


0.00518 (0.00328) 0.0805 (0.0612) 0.460 (0.395) 0.421** (0.130) 0.0982 (0.376) 0.0627 (0.133) 2.144*** (0.440) 3422 200.09356 17.42 0.0149

Dropped

TPM 24,1/2

0.00923* (0.00395) 0.111 (0.0856) 0.892** (0.332) 0.225 (0.121) 0.287* (0.141) 0.623* (0.244) — 1.204*** (0.323) 3422 225.45613 28.31 0.0001

New

More

More

Less

Less

Dropped

Dropped

0.00928* (0.00395) 0.00769*** (0.00193) 0.00815*** (0.00194) 0.0193*** (0.00221) 0.0195*** (0.00223) 0.0170*** (0.00180) 0.0172*** (0.00181) 0.0932 (0.0845) 0.169** (0.0584) 0.189** (0.0585) 0.217** (0.0745) 0.216** (0.0753) 0.519*** (0.0789) 0.571*** (0.0823) 0.917** (0.339) 0.0135 (0.235) 0.104 (0.241) 0.0803 (0.320) 0.127 (0.323) 0.494 (0.274) 0.558* (0.276) 0.222 (0.123) 0.0328 (0.0791) 0.0388 (0.0800) 0.227* (0.0980) 0.222* (0.0981) 0.512*** (0.0947) 0.519*** (0.0954) 0.315* (0.143) 0.104 (0.0766) 0.0893 (0.0776) 0.00618 (0.0929) 0.00499 (0.0932) 0.0434 (0.0966) 0.0522 (0.0975) 0.576* (0.248) 0.213 (0.192) 0.168 (0.195) 0.359 (0.308) 0.338 (0.307) 0.272 (0.397) 0.198 (0.393) 0.387** (0.150) — 0.428*** (0.0871) — 0.123 (0.0903) — 0.315** (0.0962) 1.573*** (0.356) 1.872*** (0.248) 2.288*** (0.266) 3.231*** (0.376) 3.130*** (0.382) 1.523*** (0.435) 1.189** (0.443) 3422 3422 3422 3422 3422 3422 3422 415.22701 221.66987 64079465 62763875 449.3163 448.39717 420.6123 35.88 28.94 55.25 94.39 96.23 251.13 261.90 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000

New

Notes: *p < 0.1; **p < 0.05; ***p < 0.01

Outdegree Transitivity Reciprocity Same sex Same origin Same place Same strike Constant N Log likelihood LR x 2 Prob > x 2

Variables



57

Table V. Changes in private communication

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58

formation and tie intensification with similar actors and simultaneously weaken or delete their ties to dissimilar actors concerning their private network. Concerning their workrelated network, we also find attempts to maximize individual network utility by tie formation and intensification to similar actors. However, the individual’s possibilities to delete ties might be restricted by the organization of work and are, therefore, not as unambiguous as in the private network. To investigate the question whether private and work-related networks unfold different dynamics, we compare both networks directly. First, we investigate whether private network changes and work-related network changes occur interrelatedly. This would be the case, for instance, if both private and network connections were deleted likewise. The McNemar test shows that work-related and private network changes occur independently from each other with tie intensity decrease being the only exception. We, therefore, conclude that the private and work-related network changes occur mainly independently. Our explanation for the one correlation we find is that less communication between subjects than before the strike is always a feasible answer to strike-related fault lines and can be achieved in private and work-related networks as a means to optimize social approval. On the contrary, the strike seems to unfold positive dynamics in the sense that people who were not connected before the strike established connections after the strike – and that these dynamics occur independently in the private and in the work-related networks[3]. Discussion and conclusion In our study, we set out to question what kind of changes in the social structure of working teams occur after a strike. While the social network literature frequently stresses that behaviour is a result of network structure, our question is whether the reverse is true, as we are interested in the network dynamics triggered from a behavioural decision that reflects deep-level diversity. When some employees do not support others in a conflict with the management, than deep-level diversity with regard to differences in solidarity norms becomes apparent (Thommes et al., 2014). The difference in norms between strikers and non-strikers during the collective action triggered segregation within the group. The segregation can be explained by the general idea of utility maximization (Takács et al., 2008). We investigated whether individuals engage in utility maximization activities which in turn go along with network changes after a conflict. Maximization of network utility is understood as: intensifying or establishing ties to similar actors in order to maximize social approval and at the same time; and reducing or dropping ties to dissimilar actors in order to minimize possible punishment or social disapproval. We, therefore, implicitly assume that the mechanism of social control is vivid in work teams. Using data from a case study, we find that workers indeed engage in maximization activities; in both their work-related communication flows and their private communication flows actors engage in the establishment of new links with similar actors and intensification of links to similar others. However, we cannot fully confirm the hypotheses of tie deletion or diminishing. Work-related networks might experience diminishing ties between dissimilar actors after a strike, whereas tie deletion is not significantly more likely between dissimilar actors. On the contrary, in the private network, tie deletion is significantly more likely between dissimilar actors while tie diminishing is not. Our explanation for this finding is that the costs and benefits of tie destruction in general may vary. In the work environment, an actor’s embeddedness in the formal organizational


structure might force her to still collaborate with dissimilar actors and a total tie deletion to dissimilar actors would correspond to a total refusal to work. Therefore, the absolute costs of tie deletion might be (perceived as) prohibitively high in the work environment as this might end up in job loss at the extreme. Therefore, the reduction of exchange intensity with dissimilar actors or, on the other side, tie establishment or tie intensification with similar actors might be less costly and more feasible for actors. Contrary to that, private ties can be seen as the friendship ties between actors and, therefore, tie deletion is more feasible than a reduction of intensity. If friendship involves the conviction that the other and ego share common things like attitudes or behaviours and this conviction is shaken, trust between these actors might be dissolved. From an individual’s point of view, tie deletion might be less costly than rebuilding trust and investing in the private tie. The difference we find between private and work-related networks confirms Takács et al.’s (2008) suggestion that the costs and benefits of strategic choices in a network might vary. In particular, we find that the costs of tie destruction vary between private and workrelated networks, while network growth seems to follow similar patterns in private and work-related cases. Our findings can explain the productivity loss after strikes, observed by the industrial relations literature. While the sparse literature in the sphere of industrial relations on the economic consequences of strikes for organizations suggests a lack of cooperation and stress after strikes (Gruber and Kleiner, 2010; Addison and Teixeira, 2009; Krueger and Mas, 2004; Mas, 2008), we are the first to demonstrate systematically how work-related communication networks deteriorate after the strike. Assuming that communication on work matters eases cooperation and collaboration, we found (one of) the underlying mechanism(s) of the observed productivity loss. Moreover, the network changes we found can also explain the prolonged elevated stress levels found after strikes (Barling and Milligan, 1987; Kelloway et al., 1993; Fowler et al., 2009). Thus, our study contributes to labour relations theory by providing the underlying mechanisms that explain observed but not fully explained organizational problems after labour conflicts. We argue that productivity losses that occur after a strike has been settled are caused by deep-level diversity which becomes salient during the strike: team members differ in cooperation norms to contribute to the public good of a strike action. This difference in norm substance and adherence causes lower team effectiveness (Chuang et al., 2004; Phillips et al., 2006; Guillaume et al., 2012; Meyer and Glenz, 2013; Shemla et al., 2016). Our analysis of the social networks within the firm reveals that social relations are altered after the strike, which may be well explained by maximizing the utility of the social network. The utility is maximized if relations with non-conforming others are minimized and relations with conforming others are maximized. This is in line with the idea of social identity (Tajfel and Turner, 1979), which postulates that individuals seek intensified social relations to others who are alike and are less cooperative to members of the out-group. As a direct management implication, our findings suggest that typical management behaviour during strikes, that is persuading some employees to continue to work during the strike, is counterproductive in the long run, as splitting teams does not only affect private social relations but also affects work-related communication negatively. If the management aims to avoid productivity losses after strikes, they should in turn refrain from actions that may cause segregation during the strike. Limitations and directions for future research Finally, we address some important limitations to our study. During the first wave of the interview, we asked respondents with whom they had contact three months before, thus trying to capture the network before the strike started. This method of reconstructing the


59

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60

network before the strike may have created biased answers, as employees had to remember with whom they had contact and might simply have blurred memories or ex post rationalized their current network. This potential bias may have affected our results. At the same time, it is important to note that it is impossible to have a clear measure of the network before any strike as one cannot anticipate strike actions until the union already threatens to step into action. A threat may also already alter the social network. Thus, any pre-strike measure of a strike is likely to encounter potentially biased data. As a second limitation of our study, we point at the potential influence of cultural norms on ending social relations. Comparative research on ideas and attitudes towards social relations points, for instance, at cultures in which saving face and upholding good face to face relations ships (no matter what) is an important value (Argyle et al., 1986; Galinha et al., 2016). These differences between cultures may have important consequences for the generalization of our findings on social tie deletion, in particular our findings on deleting private ties. The effect of inter-team conflict on team social networks in different cultural settings may be a highly interesting and relevant route for future research. Notes 1. Although we asked respondents to recall the situation in December, before the strike started, we cannot exclude the possibility of any retrospective biases in answers. Having a clear before measure is tricky in this kind of research anyway as strikes cannot be anticipated way before. If a strike threat becomes concrete, however, a potential before measure may be already blurred by the concrete threat of a strike. 2. See online supplement (Appendix). 3. The idea of independent network dynamics unfolds for the private and for the work-related network after the strike is further supported by the results of the Kruskal–Wallis test, which suggests independency of the work-related and private network dynamics after the strike. We use non-parametric tests as the dependent variable categorical (yes/no relation); the independent variable has two levels (before and after the strike) and observations are matched. Online Supplement B encapsulates the results of the non-parametric tests we performed.

References Addison, J.T. and Teixeira, P. (2009), “Are good industrial relations good for the economy?”, German Economic Review, Vol. 10 No. 3, pp. 253-269. Andersson, L.M. and Pearson, C.M. (1999), “Tit for Tat? The Spiraling effect of incivility in the workplace”, Academy of Management Review, Vol. 24 No. 3, pp. 452-471. Argyle, M., Henderson, M., Bond, M., Iizuka, Y. and Contarello, A. (1986), “Cross-cultural variations in relationship rules”, International Journal of Psychology, Vol. 21 Nos 1/4, pp. 287-315. Barling, J. and Milligan, J. (1987), “Some psychological consequences of striking: a six month, longitudinal study”, Journal of Organizational Behavior, Vol. 8 No. 2, pp. 127-137. Born, M., Akkerman, A. and Thommes, K. (2016), “Peer influence on protest participation: communication and trust between co-workers as inhibitors or facilitators of mobilization”, Social Science Research, Vol. 56, pp. 58-72. Born, M., Akkerman, A. and Torenvlied, R. (2013), “Trust your boss or listen to the union? Information, social identification, trust and strike participation”, Mobilization: An International Quarterly, Vol. 18 No. 2, pp. 161-178. Brunsden, V. and Hill, R. (2009), “Firefighters‘experience of strike: an interpretative phenomenological analysis case study”, Irish Journal of Psychology, Vol. 30 Nos 1/2, pp. 99-115.


CAO (2012), “Schoonmaak- en glazenwassersbedrijf”, available at: www.ras.nl/arbeidsvoorwaarden-% 28cao%29/cao/cao-01-algemene-bepalingen/ Chuang, Y.T., Church, R. and Zikic, J. (2004), “Organizational culture, group diversity and intra-group conflict”, Team Performance Management: An International Journal, Vol. 10 Nos 1/2, pp. 26-34. EFCI (2010), “The cleaning industry in Europe”, European Federation of Cleaning Industries, update 2012, available at: www.efci.eu/index.php?id=19 FNV Bondgenoten (2011), “Let’s Get Real”, available at: www.fnvbondgenoten.nl/site/branches/ zakelijke_diensten/schoonmaak/schoongenoeg/downloadblokken/witboek_schoonmaak_ 2011_lets_get_real Fowler, J.L., Gudmundsson, A.J. and Whicker, L.M. (2009), “The psychological impact of industrial strikes: does involvement in union activity during strikes make a difference?”, Journal of Industrial Relations, Vol. 51 No. 2, pp. 227-243. Francis, H. (1985), “The law, oral tradition and the mining community”, Journal of Law and Society, Vol. 12 No. 3, pp. 267-271. Galinha, I.C., Garcia-Martin, M.Á., Oishi, S., Wirtz, D. and Esteves, F. (2016), “Cross-cultural comparison of personality traits, attachment security and satisfaction with relationships as predictors of subjective well-being in India, Sweden and the United States”, Journal of CrossCultural Psychology, Vol. 47 No. 8, pp. 1033-1052. Getman, J.G. (1999), The Betrayal of Local 14, Cornell University Press, Ithaca, New York. Getman, J.G. and Marshall, F.R. (1993), “Industrial relations in transition: the paper industry example”, Yale Law Journal, Vol. 102 No. 8, pp. 1803-1895. Giguère, B. and Lalonde, R.N. (2010), “Why do students strike? Direct and indirect determinants of collective action participation”, Political Psychology, Vol. 31 No. 2, pp. 227-247. Granovetter, M. (2005), “The impact of social structure on economic outcomes”, Journal of Economic Perspectives, Vol. 19 No. 1, pp. 33-50. Gruber, J. and Kleiner, S.A. (2010), “Do strikes kill? Evidence from New York State”, NBER Working Paper No. 15855. Guillaume, Y.R., Brodbeck, F.C. and Riketta, M. (2012), “Surface-and deep-level dissimilarity effects on social integration and individual effectiveness related outcomes in work groups: a metaanalytic integration”, Journal of Occupational and Organizational Psychology, Vol. 85 No. 1, pp. 80-115. Harrison, D.A., Price, K.H. and Bell, M.P. (1998), “Beyond relational demography: time and the effects of surface-and deep-level diversity on work group cohesion”, Academy of Management Journal, Vol. 41 No. 1, pp. 96-107. Heckathorn, D.D. (1990), “Collective sanctions and compliance norms: a formal theory of groupmediated social control”, American Sociological Review, Vol. 55 No. 3, pp. 366-384. Ibarra, H. and Andrews, S.B. (1993), “Power, social influence and sense making: effects of network centrality and proximity on employee perceptions”, Administrative Science Quarterly, Vol. 38 No. 2, pp. 277-303. Kelliher, D. (2017), “Solidarity, class and labour agency: mapping networks of support between London and the coalfields during the 1984-5 miners’ strike”, Doctoral dissertation, University of Glasgow. Kelloway, E.K., Barling, J. and Shah, A. (1993), “Industrial relations stress and job satisfaction: concurrent effects and mediation”, Journal of Organizational Behavior, Vol. 14 No. 5, pp. 447-457. Kitts, J.A., Macy, M.W. and Flache, A. (1999), “Structural learning: attraction and conformity in taskoriented groups”, Computational & Mathematical Organization Theory, Vol. 5 No. 2, pp. 129-145. Krueger, A.B. and Mas, A. (2004), “Strikes, scabs and tread separations: labor strife and the production of defective bridgestone/firestone tires”, Journal of Political Economy, Vol. 112, pp. 253-289.


61

TPM 24,1/2


62

Lazer, D. (2001), “The co-evolution of individual and network”, Journal of Mathematical Sociology, Vol. 25 No. 1, pp. 69-108. Leibbrandt, A., Ramalingam, A., Sääksvuori, L. and Walker, J.M. (2015), “Incomplete punishment networks in public goods games: experimental evidence”, Experimental Economics, Vol. 18 No. 1, pp. 15-37. Leung, A.S. (2008), “Interpersonal conflict and resolution strategies: an examination of Hong Kong employees”, Team Performance Management: An International Journal, Vol. 43 Nos 3/4, pp. 165-178. MacDowell, L.S. (1993), “After the strike. Labour relations in Oshawa, 1937-1939”, Industrial Relations, Vol. 48 No. 4, pp. 691-711. Marques Santos, C. and Margarida Passos, A. (2013), “Team mental models, relationship conflict and effectiveness over time”, Team Performance Management: An International Journal, Vol. 19 Nos 7/8, pp. 363-385. Mas, A. (2008), “Labor unrest and the quality of production: evidence from the construction equipment resale market”, Review of Economic Studies, Vol. 75 No. 1, pp. 229-258. Meyer, B. and Glenz, A. (2013), “Team faultline measures: a computational comparison and a new approach to multiple subgroups”, Organizational Research Methods, Vol. 16 No. 3, pp. 393-424. Meyer, B., Schermuly, C.C. and Kauffeld, S. (2016), “That’s not my place: the interacting effects of faultlines, subgroup size and social competence on social loafing behaviour in work groups”, European Journal of Work and Organizational Psychology, Vol. 25 No. 1, pp. 31-49. Nelson, R.E. (1989), “The strength of strong ties: social networks and intergroup conflict in organizations”, Academy of Management Journal, Vol. 32 No. 2, pp. 377-401. Pearson, C.M., Andersson, L.M. and Wegner, J.W. (2001), “When workers flout convention: a study of workplace incivility”, Human Relations, Vol. 54 No. 11, pp. 1387-1419. Phillips, K.W., Northcraft, G.B. and Neale, M.A. (2006), “Surface-level diversity and decision-making in groups: when does deep-level similarity help?”, Group Processes & Intergroup Relations, Vol. 9 No. 4, pp. 467-482. Rice, R.E. and Aydin, C. (1991), “Attitudes toward new organizational technology: network proximity as a mechanism for social information processing”, Administrative Science Quarterly, Vol. 36 No. 2, pp. 219-244. Shemla, M., Meyer, B., Greer, L. and Jehn, K.A. (2016), “A review of perceived diversity in teams: does how members perceive their team’s composition affect team processes and outcomes?”, Journal of Organizational Behavior, Vol. 37 No. S1, pp. 89-106. Snijders, T.A.B., van de Bunt, G.G. and Steglich, C.E.G. (2010), “Introduction to stochastic actor-based models for network dynamics”, Social Networks, Vol. 32 No. 1, pp. 44-60. Tajfel, H. and Turner, J.C. (1979), “An integrative theory of intergroup conflict”, in Austin, W.G. and Worchel, S. (Eds), The Social Psychology of Intergroup Relations, Chapter 3, Brooks/Cole, Monterey, pp. 33-47. Takács, K., Janky, B. and Flache, A. (2008), “Collective action and network change”, Social Networks, Vol. 30 No. 3, pp. 177-189. Teague, P. and Roche, W.K. (2012), “Line managers and the management of workplace conflict: evidence from Ireland”, Human Resource Management Journal, Vol. 22 No. 3, pp. 235-251. Thommes, K., Akkerman, A., Torenvlied, R. and Born, M. (2014), “The dark side of solidarity: social norms and social relations in the aftermath of strikes”, Industrial Relations Journal, Vol. 45 No. 4, pp. 348-367. Waddington, D., Dicks, B. and Critcher, C. (1994), “Community responses to pit closure in the poststrike era”, Community Development Journal, Vol. 29 No. 2, pp. 141-150.


Appendix. Online supplement


Criterion

Density

Average degree

No. of ties

Arc-based reciprocity

Work communication Overall_t1 0.196 Overall_t2 0.162

670.000 556.000

11.356 9.424

0.7661 0.7939

Private communication Overall_t1 0.142 Overall_t2 0.168

486.000 574.000

8.237 9.729

0.8000 0.7467

Work ties New ties More intense ties Less intense ties Dropped ties

No No Yes No Yes No Yes No Yes

Private ties Yes

3,305 32 3,189 135 3,223 98 3,261 123

72 13 70 28 86 15 33 5

McNemar test (p)

Kruskal–Wallis test (p)

0.0001

0.0001

0.0000

0.0001

0.4175

0.0001

0.0000

0.0021

Corresponding author Kirsten Thommes can be contacted at: [email protected]

For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]

63 Table AI. Descriptive statistics for the networks

Table AII. Tests for interdependency of private- and workrelated networks