Predictors of Knowledge-Sharing Behavior for Teams in Extreme Environments

Team Coordination in Steel Production

To accomplish the main tasks—producing steel by monitoring and steering the process—the team members work highly interdependently. Figure 1 illustrates the interdependence of team members’ actions within one process step (the so-called cast-on). A crane operator (not displayed in the figure) has to deliver a ladle (2) with about 400 tons of liquid steel to the casting plant; the operator’s cabin is situated at a height of about 15 m above the turret (1). To insert the ladle precisely into the two hooks of the turret, close coordination and nonverbal communication (predefined hand signals; see Figure 2) between the crane operator and at least one shop floor worker is required. The crane operator depends strongly on the visual perceptions and judgments of the shop floor workers to ensure that the ladle is correctly inserted into the two hooks.

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Figure 2.

Nonverbal communication regarding predefined crane signals. Reprinted with permission of the COMMON GmbH.

In the case of poor coordination, the ladle is not accurately inserted in the turret, and about 400 tons of 1500°C liquid steel might be overturned on the shop floor (called the casting platform), where coworkers can be harmed. The crane operator has to interpret and react to the hand signals of the shop floor worker adequately. Within the next process step, the interdependencies and the requirements for close communication and coordination are maintained. For example, one worker (not displayed in the figure) standing on the ladle operator stand (6) has to open the ladle and cast the liquid steel into the tundish (3). This action has to be coordinated with the team member standing on the mold (4). When the tundish has reached a predefined level of tons, the worker on the ladle operator stand has to communicate this to the team member on the level below. One of them has to open the tundish through the manual steering of a lever (5) to cast the liquid steel into the mold; a second team member monitors the height of the steel in the mold. At the moment when the steel increases too much, the worker who is monitoring this action has to inform and warn the person who is filling it.

These are critical moments, because minimal changes in the working material (e.g., when the steel starts to bubble extraordinarily strongly) or equipment (e.g., the production line itself makes an unusual noise or abnormal vibrations), as well as deviations from predefined system states (e.g., temperature becomes too high), can give rise to critical situations for the process—or worse, for the teams steering and maintaining the plants. Heavy and uncontrollable leakage of the liquid steel (so-called breakdowns), fire, and electric shocks or explosions due to malfunctions or faulty operations are potential risks with which the work teams can be faced. To control the process and maintain the plants in normal operations, and in particular to avoid the critical situations, the teams need to coordinate their actions within their own team and between other teams in order to achieve safe and efficient production.

The interteam and intrateam coordination is challenged by the staggered work in rotating shifts, which leads to difficulties in sharing the current knowledge and information necessary for coordination (e.g., Bikfalvi, 2011). Coordination is further hindered by environmental factors, such as noise level and strong dust and smoke formation, which impair verbal and nonverbal communication. To cope with these challenges and hindrances, team-related experienced-based knowledge-sharing behavior (KSB) is assumed to be beneficial to implicit team coordination and, consequently, team performance. KSB is the central focus of investigation in the present paper.

Similarities to and Differences From Other High-Risk Teams in Extreme Environments

Similarities to and differences from other teams in extreme environments (nuclear power plants, military combat or command-and-control teams, air traffic control [ATC], offshore oil and petroleum industries) might not be immediately evident. In this section, we point out similarities in terms of team and task coordination requirements and working conditions, for example, similarities with respect to stressors.

The teams in steel mills under investigation in this paper are composed of teams in four roles (shift supervisor, team leader, control room operator, shop floor worker) and in two locations (control room, plant)—comparable to nuclear power plant teams (see O’Connor, O’Dea, & Flin, 2008). The shift supervisor is responsible for the production plants, coordinates the team tasks, and ensures safe casting production and maintenance procedures, respectively. In the control room, several workers monitor the plants, setting a variety of parameters or informing the workers steering the plant units on the shop floor level. The workers who steer the casting plants are closely involved in the process and are faced with a loud environment with dust and smoke formation while being separated from the hot liquid steel by only a few meters. The division of tasks (monitoring and steering) and a centralized hierarchical leadership are comparable to teams in aviation (ATC and pilot) process control or some military teams, although steel mill teams are spatially less widely distributed. Steel mill teams have a long life span that is similar to power plant teams but differs from military or ATC teams. Unlike in military, aviation, or even nuclear power plant control room teams, in steel mills, there are no standardized communication procedures, like closed-loop communication or a specific phraseology. The amount and quality of communication and information exchange between team members depends much more on personal willingness and cooperativeness.

In a steel mill, workers are faced with several environmental conditions and especially stressors, meaning that a steel mill can be equated to an extreme environment. Similar to teams working in nuclear power plants, military combat or command-and-control teams, ATC, or offshore oil and petroleum industries, the steel mill teams are faced with transient stressors, like high workload, time pressure, and imminent danger in the face of critical system failures, inadequate or ambiguous information, or novel events with ambiguous outcomes (Orasanu & Lieberman, 2011; Paris, Salas, & Cannon-Bowers, 2000).

In addition to the transient stressors, Orasanu and Lieberman (2011) summarized four further stressors with which teams in extreme environments are confronted: physical, habitability, psychological, and interpersonal stressors. The habitability stressors to which Orasanu and Lieberman refer arise from space vehicles and habitat, for example, constant noise, vibrations, temperature, lighting, and air quality (Orasanu & Lieberman, 2011, p. 5). These aspects are also applicable to a steel mill environment: Workers are confronted with constant background noise, extreme switches in temperature, poor air quality due to the strong dust and smoke formation, and low lighting conditions. Tones or alarms ring all around, for example, when measurements have been completed or when crane and forklift operators signal that they are moving or want to deliver a product. Added to this background noise are horn sounds, alarm warnings, or the sound of a crane dropping a large piece of steel from a height of several meters. However, whereas space flight teams remain in this difficult habitat for months or even years, workers in the steel mill can leave this environment when their shift is over.

Two aspects of the aforementioned psychological stressors can be applied to the steel mill teams: the ever-present danger and the potential for life-threatening system failures. Of course, the teams do not require life-support systems, like space suits, but when the process or the material becomes out of control, the workers’ lives are in danger.

The Study’s Organizational Context

The organizational context of the present study consists of a 3-year research project at a German steel manufacturer. This steel manufacturer became aware of the relevance of the knowledge and experience of employees with decades of tenure, while at the same time acknowledging that age-related withdrawals of up to 40% were expected.

The relevance of knowledge-based work is reflected, for instance, in hazard identification and avoidance or in fault diagnosis. The project began when the management recognized the value of preserving this experience-based knowledge, for example, the experiences of situations that seldom occur or knowledge about faults that are difficult to diagnose. The overall research question of the 3-year research project was how to design an intervention to share knowledge and experience of workers with decades of experience (which is outlined in the Discussion section) with all team members. In particular, the task- and equipment-related knowledge and experience that are required to handle critical situations were central to the research.

In order to answer the leading research question, it was deemed necessary to investigate the preconditions relevant for successful knowledge transfer in order to design an intervention to support KSB. The measurement of the KSB antecedents is introduced in the present paper. In this respect, our research is grounded in the assumption that the complexities of the modern workplace require an increased cooperation across and between the organizational and human factors tradition (Hodgkinson & Healy, 2008; Kluge, 2014) by applying a macroergonomics approach.

To provide an answer to the question of how to evaluate the effectiveness of a knowledge-sharing intervention, shared-cognition research was utilized (e.g., Cooke et al., 2000; Marks, Zaccaro, & Mathieu, 2000; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000; Stout, Cannon-Bowers, Salas, & Milanovich, 1999). Shared cognition does not refer to a unitary concept (Cannon-Bowers & Salas, 2001) but, rather, comprises several related concepts, theories, and methodological frameworks with similar assumptions (Cooke et al., 2013, p. 258). Cannon-Bowers and Salas (2001) identified about 20 labels that have been used to describe the various types of shared cognition, such as collective cognition, team knowledge, team mental models, shared knowledge, and transactive memory or shared mental models (SMMs).

The SMM concept, as one form of shared cognition, comes closest to the overall aim of the research project and relates the SMM of a team to the perception and interpretation of situational cues in a similar fashion, as stated by Klimoski and Mohammed (1994): “A team with a shared mental model is one where most, if not all, of the people involved think about a phenomenon or situation in a very similar manner” (p. 421). SMMs are assumed to explain variance in team development, team performance, strategic problem definition, strategic decision making, and even organizational performance (e.g., Cooke et al., 2003; Klimoski & Mohammed, 1994). They are deemed to constitute a detailed cognitive structuring concept for the team knowledge base (Ilgen, Hollenbeck, Johnson, & Jundt, 2005). Although they have mainly been investigated in military (e.g., Espevik, Johnsen, & Eid, 2011; Lim & Klein, 2006; Rentsch & Klimoski, 2001) or aviation contexts (e.g., Mathieu, Rapp, Maynard, & Mangos, 2010; Smith-Jentsch, Mathieu, & Kraiger, 2005), in view of the identical elements, it can be assumed that SMMs also have relevant applications in industries, too (Langan-Fox, Anglim, & Wilson, 2004). Although the SMM concept has undergone several developments over the years, in recent publications, SMMs are assumed to be not stored team-level representations but, rather, emergent states within team members’ mental models (Cooke et al., 2013, p. 258).

For the present study, the decision was made to use the comprehensively investigated concept of SMMs. The concept is linked to shared team knowledge and effective coordination in work teams (e.g., Cannon-Bowers, Salas, & Converse, 1993; Klimoski & Mohammed, 1994; Mathieu et al., 2000; Uitdewilligen, Waller, & Zijlstra, 2010), which are of relevance in the teamwork context under investigation.

In turn, KSB is assumed to be a mechanism that supports the building of SMMs, enabling workers to interpret cues in a similar manner, make compatible decisions, and take appropriate actions (Johnson & Lee, 2008; Klimoski & Mohammed, 1994). Sikorski, Johnson, and Ruscher (2012), for example, found that the similarity of team- and task-related SMMs is greater within teams that have received a team-related knowledge-sharing intervention.

In the present study, and referring back to the study’s organizational context, we operationalized the shared knowledge corresponding to the four types of SMMs as a dependent variable affected by a knowledge transfer intervention. We used a theoretically derived questionnaire to represent the level of agreement with a series of statements. This questionnaire has been used in previous research by Lim and Klein (2006), Johnson et al. (2007), and Nandkeolyar (2008). As will be outlined later, the instrument measures the subjective perception of the shared knowledge related to the task, equipment, interaction, and team knowledge.

As introduced in the section on the study’s context, the main objectives of the present study was to develop a theoretical model explaining the antecedents that impact KSB, to test our hypothesis regarding the assumed relationships, and to assess whether the shared knowledge corresponding to the four SMM types can be used as a dependent variable in order to evaluate a team knowledge transfer intervention.

KSB

Empirical evidence (Sikorski et al., 2012) has shown that KSB is assumed to enhance team coordination and help work teams to develop a team knowledge base (Wang, Ashleigh, & Meyer, 2006). KSB is defined as the process of sharing explanations and interpretations between the team members or the team as a whole (Fiore et al., 2010). Bartol and Srvivasta (2002) identified four mechanisms for how individuals share their knowledge: (a) by contributing to organizational databases (e.g., incident management systems), (b) in formal interactions within or across work teams or units (e.g., regular meetings of the teams, so-called occupational safety and health talks), (c) in informal interactions among individuals (e.g., in the changing rooms and break rooms), and (d) within communities of practice (e.g., Stahlinstitut VDEh, committee of experts in steel). Although these mechanisms are available within organizations, an important reason why knowledge is accumulated but not shared lies in “the lack of consideration of how the organization and interpersonal context as well as individual characteristics influence knowledge sharing” (Wang & Noe, 2010).

Reasons why people do not share what they know are multifaceted: The team members may be afraid of losing ownership of their individual crucial knowledge and hard-won superiority, they may fear they will not be sufficiently rewarded, or they are perhaps unwilling to devote time to knowledge sharing (Szulanski, 1996) for several reasons.

Wang and Noe (2010) proposed five areas as important in knowledge sharing research:

In the present study, we consider this interplay of people’s personal beliefs and organizational structures as potential antecedents that both influence KSB.

Personal Belief Structures: Individual Antecedents Affecting Knowledge-Sharing Intention and Behavior

Without shared attitudes and beliefs among the members of a team, the communication and coordination within the team is assumed to be less successful (Wilson, Salas, & Andrews, 2010). In this respect, the impact of attitudes, subjective norms, and perceived behavioral control on behavioral intentions, which in turn affect specific behavior, has been demonstrated in several studies and summarized in the theory of planned behavior (TPB; Ajzen, 1991). It has been shown that the TPB is also suitable for predicting KSB (e.g., Aulawi, Sudirman, Suryadi, & Govindaraju, 2009; Bock, Zmud, Kim, & Lee, 2005; Chen, Chen, & Kinshuk, 2009; Kuo & Young, 2008). When team members believe that sharing their knowledge is rewarding, they are more likely to do so (Kuo & Young, 2008). According to Bock et al. (2005), in the context of KSB, (a) attitude is defined as “the degree of one’s positive feelings about sharing one’s knowledge” (p. 107). Moreover, (b) subjective norms include the person’s beliefs that the team should comply with the desired behavior (Ajzen, 1991). The concern is with the perceived social pressure to perform or not to perform the KSB in question (Kuo & Young, 2008). Finally, (c) perceived behavioral control refers to Bandura’s (1977, 1982) concept of perceived self-efficacy, which means the perception of the ease or difficulty of performing a behavior (Ajzen, 1991).

A central predictor in the TPB is the individual’s intention to perform a given behavior. Intention is assumed to include the motivational factors that affect a given behavior by describing how hard people are trying to perform a certain behavior (Ajzen, 1991), which in this context means how intensively team members are willing to engage in a knowledge-sharing act. This definition gives rise to the following set of hypotheses:

Organizational Structures: Organizational Antecedents Affecting KSB

In addition to individual antecedents proposed by the TPB, organizational antecedents have also been found to affect KSB (e.g., Bock et al., 2005; Chen et al., 2009; Nandkeolyar, 2008; Yi, 2009). In this study, we use the term organizational antecedents to refer to subjective perceptions of processes, possibilities, and activities that organizations might use to facilitate the KSB of their employees.

Five organizational antecedents that might impact KSB were selected in the present study: team communication, perceived appraisal, organizational communication, organizational support, and social ties. The selection of the antecedents was based on shift observations that were conducted prior to the planning of the whole study. All teams were accompanied during their work according to Hacker’s (2008) approach of information flow analysis. On an observation sheet that contained the main subtasks to steer the whole casting process (from the setting up of the plant to the cleanup), the workers were asked what knowledge is relevant to them in terms of accomplishing the particular subtask and what knowledge is relevant to them pertaining to their work in general. If this expected knowledge was shared incompletely, shared too late, or not shared at all, the workers were asked whether they could imagine reasons why it was not shared by others but also why they did not share it themselves. These statements regarding why knowledge was not shared provided first helpful hints for knowledge-sharing antecedents. Some examples of statements that were mentioned are listed next.

In addition, and complementary to the shift observation, a literature review was conducted, which led to the selection of the five organizational antecedents mentioned previously: team communication, perceived appraisal, organizational communication, organizational support, and social ties. These potentially relevant antecedents will be considered further for the design of the intervention (see previous) to support KSB, which will then be outlined in the Discussion section.

In summary, we assume that intention as well as five organizational antecedents will affect KSB.

These assumptions lead to our second set of hypotheses:

Shared Knowledge Within Teams: SMMs

The concept of SMMs, which refers to shared knowledge of teams, seems to be promising for measuring the effectiveness of a knowledge-sharing intervention. SMMs are defined as

knowledge structures held by team members of a team that enable them to form accurate explanations and expectations for the task, and in turn, to coordinate their actions and adapt their behavior to demands of the task and other team members. (Cannon-Bowers et al., 1993, p. 228)

According to Cannon-Bowers et al. (1993), four types of mental models are distinguished: the task mental model (common knowledge about task procedures, task strategies, scenarios, environmental constraints, task component relationships), the equipment mental model (common knowledge about equipment functioning, operating procedures, system limitations, failures), the interaction mental model (common knowledge about roles and responsibilities, interaction patterns, communication channels, role interdependencies, information flow and sources), and the team mental model (teammates’ knowledge, skills, attitudes, preferences and tendencies).

The concept of SMMs in the heavy industry represents a promising avenue due to the ability to measure their similarity (e.g., Cooke et al., 2000; Mathieu et al., 2000; for an overview, cf. DeChurch & Mesmer-Magnus, 2010) as well as their trainability (e.g., Cannon-Bowers, 2007; Cooke et al., 2003; Marks, Sabella, Burke, & Zaccaro, 2002).

Similarity can be described as the degrees of sharedness, which means the extent to which team members’ mental models are consistent with one another (Mathieu, Heffner, Goodwin, Cannon-Bowers, & Salas, 2005, p. 38). The positive effects of similar SMMs on team performance have been shown in several studies (Cooke et al., 2003; Edwards, Day, Arthur, & Bell, 2006; Lim & Klein, 2006; Marks et al., 2000; Mathieu et al., 2000, 2005). Marks et al. (2000), for example, found that SMM similarity was positively related to the quality of team communication and team performance (Uitdewilligen et al., 2010). In sum, SMMs are a configurable type of team construct (Uitdewilligen et al., 2010), and forming a similar SMM serves as an important mechanism for enhanced team performance (Stout et al., 1999). Of course, this description might be criticized, as more similar, or more shared, mental models do not necessarily mean that the SMMs are “good,” correct, or adequate. The aspect of heterogeneity within teams plays an important role, for example, to prevent groupthink (e.g., Cooke et al., 2000; Cooke, Shope, & Kiekel, 2001). In the context of the present study, it is assumed that sharedness supports hazard identification and avoidance or enhances fault diagnosis. In order to reach the objective of avoiding hazardous situations, a team should possess similar knowledge structures related to perceptions of situations that seldom occur or that are linked to special knowledge and experience.

Wilson, Salas, Priest, and Andrews (2007) developed a considerable framework of factors that explain a team’s performance breakdowns due to teammates’ failure to develop shared cognition on the team(-work) level. Wilson et al. identified the team’s communication (with information exchange, phraseology, closed-loop communication), coordination (with knowledge requirements, mutual performance monitoring, backup behavior, adaptability), and cooperation (with team orientation, collective efficacy, mutual trust, team cohesion), but they concluded that individual, organizational, task, technological, and environmental factors also impact shared cognition. Therefore, KSB, with its individual and organizational antecedents as introduced earlier, might offer an additional avenue, leading one to ask the following question: “What kind of individual or organizational factor failed, leading people to not share what they know and in turn impeding shared cognition?”

Fiore and colleagues (2010) defined the formation of shared knowledge, like SMMs, as “team knowledge building,” described as a process that includes actions taken by the members of a team to distribute information and to transform it into actionable knowledge for team members. Therefore, the perceived KSB is assumed to be one subcomponent of this process and is thus central to our research presented here. This leads to the third set of hypotheses as follows:

Figure 3 summarizes the assumed hypotheses concerning the relationships between individual and organizational factors affecting KSB, and KSB affecting the SMM.

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Figure 3.

The integrated research model.

Sample

The study was conducted at a German steel manufacturer within a project aiming at increasing process quality by means of knowledge management interventions. To test the relationships among the antecedents shown in Figure 3, a total of 123 male shop floor workers of a steel production company, Hüttenwerke Krupp Mannesmann (HKM), in Duisburg, Germany, participated in this study. Demographic data, for example, the participants’ age, could not be collected for reasons of anonymity. As the German steel industry has a powerful work council responsible for representing the employees, approval for any field study needs to be obtained from the work council. In the current case, the work council granted permission only for information on participants’ job tenure to be collected and did not allow any other demographic data to be obtained.

HKM produces start materials (slab and round continuous cast), including iron production, coking plant, and sintering plant, to steel production and the continuous casting process. Altogether, approximately 3,000 employees produce 5.6 million tons per year. The shop floor workers are responsible for the continuous slab-casting process and for the maintenance in production. The production teams (five teams with 18 team members each) have to ensure stable processing times as well as a high quality of the slabs in defined dimensions and at various grades. The maintenance teams (five teams with 10 members each) have to provide a high degree of plant availability and a continuous casting performance within regular maintenance procedures and rapid assistance in the case of malfunctions. In this study, the production and maintenance teams working together on the same shift were combined into five units (for example, Production Team 1 and Maintenance Team 1 are combined into Unit 1). The median job tenure of participants was more than 5 but less than 10 years. The team members were informed that the study had been initiated within a project to implement knowledge and experienced management and were told that it was a cooperative endeavor between the company and the University of Duisburg-Essen. Figure 4 schematically depicts the size of a casting plant and repeats the necessary process steps, including the continuous casting.

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Figure 4.

Schematic representation of slab cast plant (Hüttenwerke Krupp Mannesmann) and the main process steps.

Instrument Development

Following a comprehensive literature review, a first instrument draft to measure KSB, its antecedents, and SMMs was developed. With the exception of the items measuring team communication, items were translated from English into German, as no German scales were found. The initial draft of the KSB questionnaire consisted of four items. The initial instrument to measure antecedents for KSB incorporated 38 items. To measure SMM, a first draft of a questionnaire was developed, consisting of 37 items. For content validation, the three instruments were passed on to external subject matter experts (SMEs). The SMEs were seven human factors and industrial and organizational psychology researchers at the University of Duisburg-Essen, four engineers working in an industrial engineering team, and four experts working in the steelworks (two engineers and two shift supervisors).

The SMEs analyzed the items of the inventories in terms of clarity, comprehensiveness, meaningfulness, and overall logic. On the basis of their feedback, some items were rewritten and three items were removed (two items from the antecedents of KSB and one from the SMM instrument). The final version of the instrument measuring KSB consisted of four items; the antecedents consisted of 38 items assessing intention and the individual and organizational antecedents, and the final version of the instrument measuring the four SMM types comprised 36 items.

Instrument Description

Procedure

The paper-and-pencil questionnaires were handed out during a regular monthly meeting held by the 10 work teams in summer 2011. Beforehand, the teams had been informed by the production management that they would be asked to fill in the questionnaires and to support the study voluntarily. Due to holidays and other reasons of absence, not all employees of the 10 teams were available. Nevertheless, the response rate was high (approximately 97%); only four members who attended the meeting did not fill in the questionnaires.

Data Analysis

The data on job tenure were tested for normal distribution and significant differences between the units. The factor structure of the measurement inventories was tested by running a confirmatory factor analysis (CFA) to determine the convergent and discriminant validity. To test our hypotheses regarding the relationships between individual and organizational antecedents in terms of their impact on KSB, we conducted a series of correlation and hierarchical regression analyses. To test the hypothesized interplay of KSB and SMMs, we modeled and tested a structural equation model (SEM) in addition to the descriptive and correlational analyses. To calculate the similarity of the SMMs, we used the coefficient alpha indices. In line with Webber et al. (2000), we transposed the raw data set such that team members were treated as items and ratings were treated as cases. The production and maintenance team working together on the same shift were aggregated to form a unit. We calculated a single score for each unit over all four types of SMMs. The values can range from 0 (absolute disagreement) to 1 (absolute agreement); thus, “higher levels of alpha indicate higher levels of within team interrater consistency” (Webber et al., 2000, p. 314). The similarity scores were calculated by aggregating descriptive statistics, and visual inspection was used to analyze the relationship between KSB and SMM similarity.

Statistics Concerning the Averaged Job Tenure and the Validity of Instruments

A Shapiro-Wilk test showed that job tenure was not normally distributed (W = 0.857, p = .000). Due to possible differences concerning demographic patterns, and taking into account the violation of the assumption of normal distribution, the Kruskal-Wallis H test was conducted. This test showed no significant differences between the production and maintenance teams working on the same shift as a unit (χ² = 5.762, p = .218).

The statistical analysis was conducted with 123 fully completed questionnaires. The results of a CFA, displayed in Table 1, show the assessment of the convergent and discriminant validity of KSB, its antecedents, and SMMs. Recommended quality criteria for convergent validity are the composite reliability (CR) and the average variance extracted (AVE; Bock, 2005, according to Hair, Black, Babin, Anderson, & Tatham, 1998). The reported values are the raw values without any adjustments in order to delete poor items and thus enhance the measurement quality. Bagozzi and Yi (1988, p. 82) assumed CR values greater than about 0.60 to be desirable. All CR values of the present study exceed the recommended value; they range from 0.7 to 0.9 and can be described as satisfactory. The AVE ranges from 0.34 to 0.75, which means that not all of the AVE range meets the recommendation to exceed the value of 0.5 (Fornell & Larcker, 1981; Bagozzi & Yi, 1988).

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Table 1:

Means, CR, and AVE Values as Indicators for Convergent Validity: KSB Antecedents, KSB, and Shared MMs

Variable	CR (Recommended ≥ 0.6)	AVE (Recommended ≥ 0.5)
Attitude	0.75	0.44
Subjective norms	0.73	0.75
Self-efficacy	0.78	0.48
Intention	Underidentified (2 items)	Underidentified (2 items)
Team communication	0.80	0.58
Perceived appraisal	0.78	0.48
Organizational communication	0.88	0.56
Organizational support	0.71	0.46
Social ties	0.77	0.34
KSB	0.72	0.39
Task MM	0.89	0.42
Equipment MM	0.74	0.49
Interaction MM	0.90	0.61
Team MM	0.88	0.37

Note. CR = composite reliability; AVE = average variance extracted; KSB = knowledge-sharing behavior; MM = mental model.

The AVE value can also be used to evaluate discriminant validity. To fully satisfy the discriminant validity of the KSB, the antecedents, and the SMM instruments, the AVE has to be greater than the squared correlations extracted for each construct (Fornell & Larcker, 1981, p. 46). The results of the squared interconstruct correlations compared to the AVE are shown in Table 2 for KSB and its individual and organizational antecedents and in Table 3 for SMMs. Although not all AVE values meet the recommended value, the AVE for each construct related to KSB and the antecedents was greater than the squared correlations involving the construct. This finding can be classified as satisfactory insofar as discriminant validity can be assumed. In terms of the comparison measures for SMMs, the results are unsatisfactory. The AVE for task MM (AVE = 0.42) was smaller than the squared correlations for equipment MM (r² = .639) and interaction MM (r² = .630). Moreover, the AVE for team MM (AVE = 0.37) was smaller than the squared correlations for interaction MM (r² = .516). Hence, the discriminant validity was not optimal, and the assumed factor structure needs to be revised.

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Table 2:

Squared Correlations in Comparison to the AVE Values for KSB Antecedents and KSB

Variable	1	2	3	4	5	6	7	8	9	10
1. Attitude	.44
2. Subjective Norms	.045	.75
3. Self-efficacy	.015	.077	.48
4. Intention	.005	.101	.061	—
5. Team communication	.045	.250	.022	.083	.58
6. Perceived appraisal	.074	.412	.030	.173	.422	.48
7. Organizational  communication	.000	.160	.051	.117	.383	.230	.56
8. Organizational support	.000	.161	.040	.093	.295	.227	.321	.46
9. Social ties	.052	.305	.095	.192	.387	.321	.334	.265	.34
10. KSB	.023	.107	.180	.158	.089	.092	.211	.066	.208	.39

Note. AVE = average variance extracted; KSB = knowledge-sharing behavior. The values on the diagonal are the AVE results. For intention, the AVE could not be calculated because the construct was measured by two items only.

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Table 3:

Squared Correlations in Comparison to the AVE Values for Shared MMs

Variable	1	2	3	4
1. Task MM	0.42
2. Equipment MM	0.639	0.49
3. Interaction MM	0.630	0.490	0.61
4. Team MM	0.347	0.280	0.516	0.37

Note. AVE = average variance extracted; MM = mental model. The values on the diagonal are the AVE results.

Testing the Hypotheses

Individual antecedents and knowledge-sharing intention

It was hypothesized that the more positive the attitude (Hypothesis 1a), subjective norms (Hypothesis 1b), and self-efficacy (Hypothesis 1c) are, the stronger an individual’s knowledge-sharing intention is. Table 4 displays the number of items of the scales measuring individuals’ antecedents, Cronbach’s alpha, means and standard deviations, and the correlations between attitude, subjective norms, and self-efficacy with intention. Only self-efficacy and subjective norms showed significant correlations with intention, with moderate effect sizes.

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Table 4:

Number of Items per Scale, Internal Consistencies, Means and Standard Deviations, and Pearson Correlations of the Individual Antecedents With Intention (N = 123)

Scale	Items	α	M (SD)	r
Attitude	4	.72	4.67 (0.64)	.07
Subjective  norms	4	.73	3.71 (0.48)	.25**
Self-efficacy	4	.71	4.10 (0.75)	.32**
Intention	2	.68	3.77 (0.62)

*

p < .05. **p < .01 (two tailed).

The relationship between the individual antecedents and the intention to share knowledge was analyzed by hierarchical regression analysis. The results are displayed in Table 5.

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Table 5:

Hierarchical Regression of Attitude, Subjective Norms, and Self-Efficacy on Intention

Variable	B	SE B	β
Constant	1.915	.795
Attitudes	−0.005	.155	−.003
Subjective norms	0.285	.098	.27**
Self-efficacy	0.199	.106	.17

Note. R² = .128 (p < .01).

*

p < .05. **p < .01.

Table 5 indicates that only subjective norms significantly predicted the intention to share knowledge. Subjective norms explained 13% of the variance of knowledge-sharing intention.

Organizational antecedents and KSB

It was hypothesized that KSB is affected by intention (Hypothesis 2a) but also by the organizational antecedents perceived appraisal (Hypothesis 2b), team communication (Hypothesis 2c), organizational communication (Hypothesis 2d), organizational support (Hypothesis 2e), and social ties (Hypothesis 2f). Table 6 presents the number of items per scale, Cronbach’s alpha, means and standard deviations of the scales, and the correlations of intention and the organizational antecedents with KSB.

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Table 6:

Number of Items per Scale, Internal Consistencies, Means and Standard Deviations, and Pearson Correlations of Intention and the Organizational Antecedents With Knowledge-Sharing Behavior (N = 123)

Scale	Items	α	M (SD)	r
Intention	2	.68	3.77 (0.62)	.40**
Perceived appraisal	4	.78	3.13 (0.89)	.30**
Team communication	3	.77	4.32 (0.50)	.30**
Organizational communication	6	.88	3.87 (0.68)	.46**
Organizational support	3	.70	3.86 (0.59)	.26**
Social ties	8	.77	3.72 (0.74)	.46**
Knowledge-sharing behavior	4	.72	4.26 (0.75)

Note. In this analysis, only the values of the items measuring knowledge-sharing behavior are included and not the aggregated team values.

*

p < .05. **p < .01 (two tailed).

As displayed in Table 6, intention, organizational communication, and social ties showed significant correlations with KSB, with medium to large effect sizes. Correlations of perceived appraisal, team communication, and organizational support with KSB were also significant although less strongly so than for the other three organizational antecedents and the intention.

Regression analysis was also applied to test the hypothesized relationships between intention, the organizational antecedents, and KSB (Table 7). Organizational communication, social ties, and intention significantly predicted KSB and explained 31% of the variance. Perceived appraisal, team communication, and organizational support did not have an impact on KSB.

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Table 7:

Hierarchical Regression of Intention and the Organizational Antecedents With Knowledge-Sharing Behavior

Scale	B	SE B	β
Constant	2.438	.266
Intention	0.147	.061	.22*
Perceived appraisal	0.015	.068	.02
Team communication	−0.055	.069	−.09
Organizational communication	0.220	.071	.34**
Organizational support	−0.056	.06	−.09
Social ties	0.196	.088	.25*

Note. R² = .310 (p < .01).

*

p < .05. **p < .01.

In summary, the hypotheses can be partially supported. Whereas subjective norms and self-efficacy showed a significant positive correlation with knowledge-sharing intention, attitudes did not. Nevertheless, the regression analysis showed that only subjective norms (Hypothesis 1b) can significantly predict an individual’s intention to share what he or she knows. Therefore, Hypotheses 1a and 1c could be considered as not supported.

The same applies for intention and the organizational antecedents that were assumed to impact KSB. The hypotheses can be partially supported. Intention and all five organizational antecedents correlated significantly positively with KSB. However, only intention (Hypothesis 2a), organizational communication (Hypothesis 2d), and social ties (Hypothesis 2f) significantly predict KSB.

Testing Hypothesis 3a Between KSB and SMM

Hypothesis 3a suggests that the four types of SMMs (task, equipment, interaction, and team) are positively affected by KSB. Table 8 shows the number of items per scale, Cronbach’s alpha, means and standard deviations, and Pearson correlations of KSB and SMMs.

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Table 8:

Number of Items per Scale, Internal Consistencies, Means and Standard Deviations, and Pearson Correlations of Knowledge-Sharing Behavior With Shared MMs

Scale	Items	α	M (SD)	r
Knowledge-sharing behavior	4	.72	4.26 (0.75)
Task MM	12	.89	4.05 (0.56)	.29**
Equipment MM	3	.72	4.07 (0.73)	.25**
Interaction MM	8	.90	4.04 (0.73)	.33**
Team MM	15	.87	3.73 (0.62)	.25**

Note. MM = mental model.

*

p < .05. **p < .01 (two tailed).

KSB shows a significant correlation with all four types of SMM, with moderate effect sizes. To test the hypothesized assumption, SEM was performed. Table 9 shows the values of the SEM that tested the relationships between KSB and the four types of SMMs.

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Table 9:

Structural Equation Model: Fit Indices

	χ2	df	p	χ2/df (Recommended < 3)	RMSEA (Recommended < 0.08)	CFI (Recommended > 0.9)	TLI (Recommended > 0.9)
KSB on  SMMs	1571.191	736	.000	2.135	0.09	0.70	0.67

Note. Recommended values are based on Hair, Black, Babin, Anderson, and Tatham (1998) and Bagozzi and Yi (1988). RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis index; KSB = knowledge-sharing behavior; SMM = shared mental model.

An SEM provides a good fit when the chi-square value is relatively small and the p value is not significant (>.05). However, with “real-world data,” particularly in combination with large sample sizes, “the chi-square statistic is frequently significant even if the model provides a good fit” (Yi, 2009, p. 73). The Cronbach’s alphas to measure internal consistency showed acceptable (.68) to excellent (.90) values, but the fit of our present model was not optimal. This finding might be explained by the real-world setting and its peculiarities.

Testing Hypothesis 3b Between KSB and SMM Similarity

Hypothesis 3b posited that teams with a higher KSB will have a higher SMM similarity. To test our assumption of the interplay between KSB and SMM similarity, descriptive analysis was carried out. Inferential statistical methods require variance, and the variance within five scores (one score for each unit) was not met. Table 10 shows the mean values of KSB and the coefficient alpha indices as SMM similarity scores for each unit.

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Table 10:

Descriptive Relations Between KSB and SMM Similarity

Unit (Production and Maintenance)	Mean for KSB (Ranking)	α (Ranking)
1	4.150 (5)	.801 (5)
2	4.296 (1)	.843 (1)
3	4.278 (3)	.816 (3)
4	4.250 (4)	.820 (2)
5	4.292 (2)	.809 (4)

Note. For reasons of anonymity, the classification of the unit numbers does not reflect reality, but was randomized by the authors. KSB = knowledge-sharing behavior; SMM = shared mental model. KSB should raise high mean values; SMM similarity should also raise high values (higher values of alpha indicate higher similarity). The values in brackets represent the ranking within the five units, whereby 1 represents the highest and 5 the lowest rank.

As displayed in Table 10, the assumed relationships can be detected through careful visual inspection. Units 1, 2, and 3 follow the assumption that a higher KSB leads to a higher SMM similarity. Unit 1 reached the lowest values in KSB and had the lowest SMM similarity; Unit 2 reached the highest KSB and had the highest SMM similarity. Thus, taking the limited number of teams into account, there are signs of some support for Hypothesis 3b, which posited that a team’s KSB and the shared knowledge concerning SMMs are related. Figure 5 displays the results of all underlying hypotheses of the present research.

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Figure 5.

Results for the integrated research model.

Application to Other Teamwork Settings

As introduced earlier, teams in a steel mill environment share several characteristics with teams in other extreme environments, like nuclear power plants, military combat or command-and-control teams, ATC, and offshore oil or the petroleum industries. It needs to be considered that team members bring with them individual antecedents and are embedded in an organizational context. Referring back to the framework of Wilson et al. (2007), we propose that several organizational and individual factors impact KSB and, in turn, KSB impacts the SMMs, one specific concept of shared cognition within teams.

As introduced earlier, unlike in other teams—for example, from military, aviation, or even nuclear power plant control rooms—in steel mills, there are no standardized communication procedures, like closed-loop communication or a specific phraseology. As in the steel mill context, the amount and quality of communication and information exchange between team members depends much more on personal willingness and cooperativeness. This description in turn leads to the assumption that in contexts that are less “formalized” and in which team behavior is less directly guided by brevity codes or standardized communication and cooperation procedures, the individual and organizational antecedents become more important than in other settings. The personal intention to share knowledge is therefore much more important than in team contexts in which intention is replaced by orders or by traditions, such as briefing and debriefing techniques, such as in military contexts.

The findings are additionally useful insofar as they offer KSB as an additional precondition that can be enhanced by the organizational factors that need to be contextualized to the particular team task and organization under investigation or analysis. From this study, it can be learned that merely looking at the team process of shared cognition might lead to a “tunnel vision” that masks the significant organizational variables, such as communication on the organizational level, and the individual preconditions, for example, self-efficacy and subjective norms.

With respect to the generalizability of results, our findings therefore address the aspects of organizational ergonomics or macroergonomics that focus on the organization of sociotechnical systems, such as the structure, policies, and processes (Karwowski 2006, p. 4; Kluge, 2014). It is assumed that the complexities of the modern workplace require an increased cooperation across and between the organizational and human factors tradition (Hodgkinson & Healy 2008). We see our findings as representing an attempt to integrate both traditions by demonstrating the impact of organizational variables that influence the willingness to share knowledge.

To sum up, the addressed individual and organizational factors and their impact are of additional value for shared cognition research and practice and might be applicable to other teams in further research.

Limitations of the Study

Due to the fact that in the present study, we employed only subjective ratings, further research is needed in order to analyze the impact of KSB and SMM on more objective and “hard” data, such as process-relevant organizational success criteria. The steel production context offers several possible measurement criteria for using this type of performance data. These data were collected but only on a team level, meaning that the sample was reduced from 123 shop floor workers to 10 teams and five units, respectively, which makes inference statistical analysis challenging. Moreover, from a measurement perspective, the relationship between subjective ratings of SMM and the relationship with similarity of the subjectively rated SMM require further consideration.

Additionally, so far, the measures are limited to subjective ratings based on individual perceptions. More objective measures could be added in future studies, which could also assess, for instance, whether the team members’ knowledge of available organizational antecedents, such as communication channels, is related to their actual frequency of using them.

Further limitations of this study include the sampling, which has so far been restricted to one company from one particular industry. Thus, the findings might not be easily generalized to other industries. Moreover, as we measured the antecedents of KSB and SMMs using the same survey, the occurrence of common method bias needs to be taken into account when interpreting the relationships among these variables (e.g., Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). However, our analyses did indicate that these concepts were perceived as distinct from each other. As mentioned earlier, the fit of our present model is not optimal, and further investigations are needed in this regard. This goal could be accomplished, for example, by addressing the number of items or variables used in the model in order to improve the model fit.

Furthermore, the present study focuses exclusively on the similarity of the SMMs and not on qualitative characteristics, such as the correctness or the quality of the shared knowledge, which might in turn influence SMM accuracy. Hence, we did not take into consideration that high SMM similarity does not imply the appropriateness of the models (Mathieu et al., 2000) and does not necessarily result in improved performance (Burtscher & Manser, 2012). For example, Edwards et al. (2006) found that accuracy partially mediated the relationship between the accuracy of SMMs and team performance. Thus, authors of future studies should consider additional aspects, for instance, whether there are effects on team composition (cf. Edwards et al., 2006), especially concerning the individual antecedents. Moreover, additional interaction patterns should be examined more closely (cf. Zijlstra, Waller, & Phillips, 2012). For purposes of satisfying reliability, appropriate validity and the pretest-posttest field design, the applied measurement inventories can be regarded as sufficient for the objective of the present study. However, for future studies, an improvement of the item structure should be considered.

Practical Consideration in the Study’s Context

Based on the empirical findings, a knowledge transfer intervention called poWER was developed, which serves as an acronym for process-oriented knowledge (in German, Wissen) and experience (in German, Erfahrung) transfer. The organizational antecedents that significantly influence KSB were considered as the main design criteria for developing the intervention for supporting KSB in order to facilitate the development and perception of organizational antecedents. Subjective norms and self-efficacy serve as relevant predictors of intention, and organizational communication and social ties were found to be predictors for KSB as well as intention.

The overall objective of the poWER intervention, which is currently in the implementation phase, is to support the KSB process and facilitate SMM development within the teams. The poWER intervention was developed as a formalized method that links several characteristics of the team decision-making process: “Filter ‘raw’ data, apply individual expertise, communicate relevant information, and (often) make recommendations to other members” (Cannon-Bowers et al., 1993, p. 222). It is a group-oriented procedure that consists of three phases: (a) development, (b) check, and (c) transfer.

Within Phase 1, the development phase, experts (one to two per team, with long-standing expertise, hierarchy independent) from the five production and maintenance teams come together. A neutral third party moderates throughout this phase, following several substeps. The outcomes are then converted into a structured job aid (tailored to the production and maintenance teams’ tasks) according to the four aspects of the recognition-primed decision-making model (Klein, 1998): cues, expectancies, Actions 1 . . . n, and goals. These job aids are supposed to serve as expert decision-making protocols and are validated within the check phase by the production management.

Within the check phase, the developed job aids are selected and scheduled for the monthly transfer phase, which is embedded within a regular team meeting. There, the selected job aids are discussed and made available for the whole teams (in electronic and paper form). This method addresses the organizational antecedents of “organizational communication” and “social ties.” The social ties should be taken into account by the mixed expert teams, which have to elaborate a common strategy, get to know one another better, exchange their own experiences, and promote the method within their own teams.

The “organizational communication” also needs to ensure that knowledge and experiences are made available for all members of the process and is also embedded within the regular meetings of the teams. Complementary to this, the use of the job aids is assumed to address the individual antecedent “self-efficacy.” On the basis of these job aids, young and less experienced employees in particular are to be encouraged to ask questions that they would not have otherwise asked and to share what they perceived.

The impact of the poWER intervention is currently being assessed by means of the same instruments introduced earlier to measure whether the proposed antecedents and the KSB were positively influenced and the perceived sharedness was increased (by using a repeated-measures design).

The findings of the present study highlight the potential inherent in also applying the shared knowledge regarding to the SMM concept to explain teamwork coordination in traditional industrial production and maintenance settings. However, a great deal of more systematic research is needed to gain an understanding of the complex interplay of individual and organizational factors that affect or interact with KSB and the development of an SMM.