Team Resilience: Scale Development and Validation

Abstract

Resilience is of great importance for teams working in complex and unstable environments. Team resilience is the ability of the teams/groups to bounce back and sustain in the facade of adverse conditions. Research reveals that resilient teams are more likely to be productive, agile and innovative during the turbulent times. However, despite the growing importance of the concept, there is lack of reliable and valid scale to measure team resilience in the literature. Keeping this gap in mind the study aims to design and develop a reliable and valid measure to assess the resilience capacity of the teams. Findings of the study reveal that team resilience is a hierarchical and multidimensional scale comprising of four primary dimensions along with 10 sub-dimensions. Psychometric evaluation and validation has been done using 160 responses from 12 IT companies located in India. The instrument may be used as a diagnostic tool for identifying team resilience capacity and thereby acts as a starting point for increasing team resilience. Moreover, identifying teams with lower resilience scores may assist organizations in tailoring strategies that might improve the teams’ effectiveness.

Keywords

Team Resilience Reflective Hierarchical and Multidimensional Scale VUCA (volatile uncertain complex and ambiguous)Psychometric Evaluation

Introduction

Global project teams, task-oriented groups are becoming the norm in the information technology (IT) industry. Members in such teams face number of challenges because of its highly output-driven nature, faster pace of technological advancement and peculiar work culture (Simoes, 2013). IT teams operate on tight deadlines, high client expectations, dissimilar time zones and stretched working hours (Budhwar, Luthar, & Bhatnagar, 2006; Sengupta & Singh, 2013). The present working style in IT industry contributes to high level of occupational stress among the employees (Sengupta & Singh, 2013). This is one of the industry where it is difficult to find any low-stress or stress-free jobs. Be it is a BPO employee or a software developer all are under the colossal jaws of stress. While working in these types of situations individuals begin to focus inward and start losing focus on the team tasks, and important interdependencies that exist within most work teams (Driskell & Salas, 1991). In teamwork environments that involve potential stressors the collective effects of the issues explained above likely act to reduce team morale, inhibit team satisfaction, and hold the potential to lead to increased conflict, high rates of absenteeism and attrition (Upadhya & Vasavi, 2006).

Team resilience may prove to be an important team-level capacity that protects a group of individuals from the potential negative effect of stressors they collectively encounter (Morgan, Fletcher, & Sarkar, 2013). Research revealed that teams that exhibit an ability to either flourish under difficult situations, manage and adapt to significant change or stress, or simply recuperate from a negative experience are less likely to come across the potentially detrimental effects of intimidating situations (West, Patera, & Carsten, 2009). It is the dynamic orchestration of each member’s resilience characteristics that helps in maintaining high level of productivity during the turbulent times and avoids symptoms of future shocks (Cooper, 2013). Further research divulged that individuals who are more resilient are more likely to remain physically and emotionally healthy while struggling with the uncertain circumstances (Cooper, 2013; Morgan et al., 2013).

Appreciating the growing importance of the construct, organizations and practitioners are struggling to develop a quantifiable measure that enables the empirical investigation on the domain of resilience in the work situation, factoring at team level. Several researchers and scholars have generated theories, developed frameworks to measure resilience, at individual and organizational level. Few such measures include the Dispositional Resilience Scale-15 (DRS-15; Bartone, 1995; 2007), Resilience at Work scale (RAW; Winwood, Colon & McEwen, 2013), Resilience Scale (RS; Wagnild & Young, 1993) Resilience Scale for Adults (RSA; Friborg, Barlaug, Martinussen, Rosenvinge & Hjemdal, 2005), The Brief Resilience Scale (BRS; Smith, Dalen, Wiggins, Tooley, Christopher & Bernard, 2008) the Connor–Davidson Resilience Scale (CD-RISC; Connor & Davidson, 2003), Conjoint Community Resiliency Assessment Measure (CCRAM; Leykin, Lahad, Cohen, Goldberg, & Aharonson-Daniel, 2013) Family Resilience Assessment Scale (FRAS; Sixbey, 2005) (Refer Table 1). But there is dearth of reliable and valid instruments to measure resilience from team perspective. Surprisingly, for measuring team resilience, authors usually rely on individual resilience measures and modify them in team context (Blatt, 2009; West et al., 2009; Molenaar, 2010). Moreover to authors’ knowledge no attempt has been made worldwide to assess the resilience capacity of teams operating in IT Industry that are supposed to work under extreme work pressures and deadlines. Keeping this gap in mind the study aims to develop and validate the team resilience scale (TeamRes). The specific objectives of the study are to infer the nature and dimension of team resilience construct, to methodically build an instrument to measure the resilience capacity of teams functioning in IT domain and to assess the psychometric properties of team resilience scale. The specific objectives of the study are to infer the nature and dimension of team resilience construct, to methodically build an instrument to measure the resilience capacity of teams functioning in IT domain and to assess the psychometric properties of team resilience scale.

Theoretical Framework

Definition of resilience may be drained from several fields including materials science, ecology, developmental psychology, organizational studies and the wider social sciences (Holling, 1973; Lengnick-Hall & Beck, 2005; Luthans, 2002a; Masten & Reed, 2002; Nash, 1998). Professor Fred Luthans and his colleagues introduced the concept of resilience in the domain of positive psychology via the core concept of psychological capital (PsyCap) (Luthans, Luthans, & Luthans., 2004, p. 46). PsyCap is defined as

An individual’s positive psychological state of development that is characterized by the following (a) having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (b) making a positive attribution (optimism) about succeeding now and in the future; (c) preserving toward goals and when necessary, redirecting paths to goals (hope) in order to succeed; and (d) when beset by problems and adversity, sustaining and bouncing back and even beyond (resiliency) to attain success. (Luthans, Youssef, & Avolio, 2007, p. 3)

At the core of the resilience capacity is the bouncing back (and beyond) from setbacks and positively coping and adapting to significant changes. Early research on resilience revealed various factors that helps individuals thrive in the face of adversity and these factors are termed as protective factors in the resilience literature and consists of qualities like adaptability, positive climate in the family, positive and secure relationships with others (Werner & Smith, 1992). It was after 1990s that the focus of envisaging resilience as a set of traits (Jacelon, 1997), changed to conceiving resilience as a dynamic process that developed over time (Olsson et al., 2003). The research reveals that individual capacity to face the adversity is dependent on the person–environment interactions and the nature of demands the individual encountered over a period of time (Egeland, Carlson, & Sroufe, 1993).

Defining Team Resilience

Recent resilience research in psychology and organizational behaviour has shifted the attention of researchers from individuals towards the study of groups and teams (Brodsky et al., 2011; Norris et al., 2008). Various psychosocial factors like concerned relationships, effective teamwork originated through cohesion, trust, resourcefulness, collective efficacy and relational schemas (Blatt, 2009; Gittell et al., 2006; Lengnick-Hall, Beck, & Lengnick-Hall, 2011) are considered to constitute the characteristics of team resilience. Furthermore, researchers have revealed that the teams with broader perspective when met with adversities come out successfully by adopting learning orientation and espousing challenging experiences (Bennett et al., 2010; West et al., 2009). Strengthening the need for team-level resilience research, Bennett et al. (2010) stated that ‘resilience may be viewed as much a social factor existing in teams as an individual trait’ (p. 225). The statement recommends that team members do not exist in isolation and that they may have an ability to adjust positively to their changing environment using facilitative shared interactions. Based on the notion, the study by Morgan et al. (2013) proposed team resilience as ‘a dynamic, psychosocial process which protects a group of individuals from the potential negative effect of stressors they collectively encounter. It comprises of processes whereby team members use their individual and collective resources to positively adapt when experiencing adversity’ (p. 552). Additionally, the study recognized group structure, mastery approaches, social capital and collective efficacy as the four main characteristics of elite sports team. The study extended the resilience research in sport psychology by furnishing theoretical precision of resilience at a team level. Various researchers have tried to extend the concept of resilience to team level supporting the argument that constructs in same content domain (e.g., resilience) are evinced in different manner at different levels of analysis (e.g., individual or team) (Chan, 1998). One such study by Lewis et al. (2011) talked about the theoretical interrelatedness between team resilience and social capital. Social capital broadly refers to social connections and the norms and trust that help the team members to chase their common goals and this may be the case of resilient teams (Putnam, 1995, p. 665). This idea echoes the concept of collective effectiveness given by Bandura (2000) that proposes that a social group can feel that they have the positive capacity to change their environment efficiently. Teams look for the help from their familiar ones to recover from adversity, and they tried to form a community while struggling with the negative circumstances. The study viewed team resilience as either a social process (existing in teams or groups) or an individual characteristic that can be articulated collectively (e.g., as a group or team effort). The study suggests that members of team do not exist in segregation and it is the collective capacity of the team members as a whole that helps them acclimatizing positively to their environment.

Table 1.

Description of Various Scales Measuring Resilience

Instruments	Author	Constructs and Items	Purpose of Measure	Target Population	Reliability Analysis	Validity Analysis
Resilience scale for adults (RSA)	Friborg, Hjemdal, Rosenvinge, and Martinussen (2003)	5(37) (personal competence, social competence, family coherence, social support, and personal structure)	Designed to measure the protective factors that contribute to adult resilience	Patients from an adult outpatient clinic	Internal consistencyCronbach’s alpha of sub-scale ranges from 0.67 to 0.90 Test-retest reliability(Four months) range from 0.69 to 0.84.	Convergent validityPresent and supported by the positive correlations between the RSA and the Sense of Coherence ScaleDiscriminant validityPresent and evidenced by negative correlations between the RSA and the Hopkins Symptom Check List-25
Resilience scale (RS)	Wagnild and Young (1993)	2(25) (personal competence and acceptance of life and self)	To identify individual resilience, a positive personality characteristic that enhances individual adaptation	Adults	Internal consistencyCronbach’s alpha for the full scale came out to be 0.91	Convergent validitySupported by positive correlations between the RS and life satisfaction and physical healthDiscriminant validity: evidenced by negative correlations between the RS and measures of depression
Resilience at work (RAW) scale	Winwood et al. (2011)	7(20) (living authentically, finding your calling, maintaining perspective, managing stress, interacting cooperatively, staying healthy, building networks	To understand the elements of workplace-resilience, a skill that could be taught, practiced, and developed	Multi-study sample (including health, manufacturing industry workers (various), teachers, bank offices, corrections officers.	Internal consistency reliability: Cronbach’s alpha for the total scale is 0.84, for individual subscales ranges b/w 0.60 to 0.89	Convergent and discriminant validity: Supported by negative correlation with maladaptive outcomes of work pressure such as chronic fatigue, poor sleep, physical and emotional health problems (GHQ 12).Positive high correlation is reported among RAW score and recovery, health and engagement in various samples
The dispositional resilience scale (3) (USA/English)	Bartone (1995, 2007)	3(15) (commitment, control and challenge)	Designed with an aim to measure psychological hardiness	Adults	Internal consistency: Cronbach’s alpha reported for the full scale to be 0.82Test-retest reliability: (3 weeks) estimated to be 0.78	Author reported the good criterion-related validity of the instrument across various samples
The brief resilience scale (USA/English)	Smith et al. (2008)	1(6)	Designed as an outcome measure to assess the ability to bounce back or recover from stress	Adults	Internal consistency: Cronbach’s alpha ranging from 0.80 to 0.91 across all the samples Test-retest reliability: (1 month) estimated to be of 0.69 and (3 months) estimated to be 0.62	Convergent validity supported by zero-order correlations between the brief resilience scale (BRS) and personal characteristics, social relations, coping and health outcomes for each sample and positive correlations among the resilience measures, optimism and purpose in life, and negative correlations with pessimism and alexithymiaDiscriminant validity: Supported by zero-order and partial correlations between each of the BRS, CD RISC, ego resiliency and the health outcomes in the undergraduate sample (Sample 1) zero-order and partial correlations between the BRS, optimism, social support, and type D and the health outcomes in the cardiac sample (sample 2)
Connor–Davidsonresilience scale(CD-RISC)	Connor and Davidson(2003)	5(25) (personal competence, trust/tolerance/strengthening effects of stress, acceptance of change and secure relationships, control, spiritual influence)	Designed for clinical practice as a measure of stress coping ability	Adults	Internal consistency: Cronbach’s alpha reported for full scale to be 0.89 Test–retest reliability assessed from subjects in groups four and five with intraclass correlation coefficient of 0.87	Convergent and discriminant validity Assessed by correlating the scores of CD-RISC scale with other more established instruments (e.g., Sheehan social support scale (SSS), Kobasa hardiness measure perceived stress scale (PSS-10))Convergent validity—PresentDiscriminant validity—not present
Conjoint community resiliency assessment measure (CCRAM)	Leykin et al. (2013)	5(21) (leadership, collective efficacy, preparedness, place attachment, social trust)	Designed to measure community resilience	Adults aged 18–86 years	Internal consistency: Cronbach’s alpha reported for the full scale to be 0.92.Cronbach’s alpha ranging from 0.75 to 0.85 across all dimensions and sub-dimensions	Divergent and concurrent validity of the scale was measured using the correlations of indicator, factor and overall scale scores generated from different measures. CFA was used as the statistical technique for accessing the same
Family resilience assessment scale (FRAS)	Sixbey (2005)	6(66) (family communication and problem solving (FCPS), utilizing social and economic resources (USER), maintaining a positive outlook (MPO), family connectedness (FC), family spirituality (FS), ability to make Meaning of adversity (AMMA)	Aimed to measure the family resilience	Adults	Internal consistency: Cronbach’s alpha reported for the full scale to be 0.96Cronbach’s alpha ranging from 0.70 to 0.96 across all dimensions and sub-dimensions	Concurrent and Divergent validity was assessed by correlating the scores of FRAS with more established instruments (e.g., family assessment device 1, family assessment device 2 and personality meaning index)
Benchmark resilience tool (BRT-53)	Stephenson (2010); Stephenson, Vargo and Seville (2010)	2(53) (planning and adaptive capacity)	Designed to measure the organizational level resilience, to monitor organizations’ progress over time, and to compare resilience strengths and weaknesses with other organizations	Multi-study sample including organizations from various sectors and industries	Internal consistency: Cronbach’s alpha reported for full scale to be 0.95 and Cronbach’s alpha ranging from 0.677 to 0.945 across all dimensions and sub-dimensions	Face validity: Checked using the reviews from 34 experts from 4 organizations in Auckland. Convergent and discriminant validity accessed using exploratory factor analysis
Benchmark resilience tool (BRT-13B)	Whitman et al., (2013)	2(13) (planning and adaptive capacity with same sub-dimensions as in BRT-53	Designed to bring the short version of BRT-53 to measure the organizational-level Resilience	Multi-study sample including organizations from various sectors and industries	Internal consistency: Cronbach’s alpha ranging from 0.67 to 0.75 across all the three samples	No specific information found on construct and discriminant validity. Authors mentioned that validity of the scale was measured using the correlations of indicator, factor and overall scale scores generated from different measures

Source: Author compilation of Literature.

The literature on resilience has strengthened the need to manifest resilience in different manner at team level (Flint-Taylor & Davda, 2013; Morgan et al., 2013). The challenge therefore has been to develop a measure to assess the resilience capacity of the teams. This study addresses these flaws by theoretically conceptualizing and experientially validating a team resilience scale in the IT context. The study explored the literature on resilience, and adopted the widely explored four essential facilitating factors of team resilience given by Morgan et al. (2013) for developing a measure of team resilience.

Scale Development Process

To develop a scale with an aim to measure the resilience capacity of teams functioning in IT domain, the study started by scrutinizing the most cited factors that influence team resilience, as delineated in previous section. Following an exhaustive and comprehensive literature review, the authors found the Morgan et al. (2013) framework of team resiliency as the most widely explored theoretical scaffold in the resilience literature and adopted the four essential facilitating factors named as group structure, mastery approaches, social capital and collective efficacy as the major indictors for developing the scale.

Firstly, group structure reflects the internal scaffold that defines members’ relations to one another over time (Wittenbaum & Moreland, 2008). The research on group structure has identified task design, task composition and group norms as three structural features fostering teamwork in the groups Wageman et al., 2005 Weick, 1993). Task design is a complete significant piece of work for which team members have the full sovereignty to apply work procedure judgements of their own, with regular and trustworthy feedback about the outcomes. Well-planned group tasks provide useful resources, flexibility and autonomy to the team members to take decisions of their own in the tough situations (Morgan et al., 2013; Wageman et al., 2005). Team composition reflects the team size, team diversity, skills that ensure that the team has sufficient number of members with ample knowledge and experience to achieve the team as well as organizational goal increases the ability of members to work collectively in the face of adversity (Gersick, 1988; Wageman et al., 2005). The third sub-dimension of group structure includes of group norms and stresses on the necessity of having a clear and well-defined norms of conduct for member behaviour that must be developed keeping the shared expectations of group members in mind. Clear specification of shared norms of conduct in a team provides a sense of purpose, sense of belongingness which is very much required while facing the negative potential stressors (Gersick, 1988; Morgan et al., 2013; Wageman et al., 2005).

Secondly, mastery approaches indicate ‘the shared attitudes and behaviors of the team members that promote an emphasis on team improvement’ (Morgan et al., 2013, p. 553). The author relied on team learning orientation and team flexibility as the two quantifiable sub-constructs to define the main indicator of mastery approaches. Team learning is defined as the ‘activities carried out by team members through which a team obtains and processes data that allow it to adapt and improve’ (Edmondson, 1999, p. 351). Research reveals that an open, accommodating, collaborative and learning-oriented work environment fosters resilience in the employees (Näswall et al., 2013). Further flexibility is defined as the capacity to modify both behaviour and structure as per the requirement with an aim to ensure survival in the face of adversity (Kaufman, 1985). Thus team flexibility can be seen as the capacity of the team members to collectively assess their behaviour and structure to make necessary adjustments, with an aim to function effectively in any adverse situation (Griffin, 1997). In this study authors focused on Evans’ (1991) strategic flexibility model that defined flexibility as an outcome of both proactive and reactive elements. Research reveals that flourishing teams encouraged members to proactively ameliorate their situation and react to sudden changes that take place in their work/task environment.

Third dimension comprises social capital which can be regarded as ‘features of social life-networks, norms, and trust, which enable participants to act together more effectively to pursue shared objectives’ (Putnam, 1995, p. 56). Social capital is treated as an asset or resource for increasing the team resilience (Fleming & Ledogar, 2008; Putnam, 2000) and thereby treated as an important constituent of our study. As per the social capital theory team social capital consist of structural, relational and cognitive as three broad dimensions defining the core construct as per the interaction and social relationships among the groups. The structural dimension depicts the overall pattern of associations among the members that can be defined through the major facet of network ties, network connections. Further the relational dimension describes the resources created because of inter-member relationships, including trust, norms. Finally, the third dimension talks about the shared language and stories including the organizational culture among the team members that helps in forming the social relationships. Network ties, shared language and trust were selected as the major constructs for measuring social capital in the study.

The final primary dimension taken for this study is collective efficacy that represents the ‘group’s shared belief in its ability to organize and execute the actions required to reach certain levels of achievement’ (Bandura, 1997, p. 477). Based on this notion, it becomes imperative to define two fundamental units of analysis self efficacy and group efficacy with a broader aim to study collective efficacy (Bandura, 1997). The construct suggests that a social group can feel that they have the required abilities to change their environment effectively; this may be the case with the resilient teams. For measuring collective efficacy author relied on two factors that describe a team’s perceived ability to undertake collective action/work as a unit, face adversities together and muddle issues within the team, and beliefs on the ability of team members.

Method

This study has focused on IT industry to represent service sector and to accomplish the various objectives set for the research. For sample size determination, rule of two, that is, the subjects-to-variables ratio no lower than two (Kline, 1979), is undertaken. To collect the primary data, 12 IT firms located in few select states of northern India and registered with NASSCOM were contacted and a sample of 160 IT executives including team leaders, project managers from various teams was selected using random sampling. Upon data entry and data cleaning only 152 correct and usable responses fit for data analysis were gathered, corresponding to a response rate of 95 per cent.

The data in Table 2 show spread across various demographic dimensions for the sample (104 males and 48 females). Maximum percentage of respondents (47.4 per cent) falls in the age group of 21–30 years with more number of respondents possessing professional qualifications like B.Tech. and M.Tech. than other graduation and postgraduation degrees. Table 2 also reveals that sample consists of more number of respondents (72 per cent) from entry level as compared to professional and senior level.

Table 2.

Distribution of Respondents

Category	Number of Respondents (N = 152)	Percentage	Category	Number of Respondents(N = 152)	Percentage
Age (years)			Education Qualification
21–30	72	47.4	Graduate	45	29.6
31–40	57	37.5	Postgraduate	31	20.4
41 and above	23	15.1	Professional education (B.Tech./M.Tech.)	59	38.8
Total	152	100	PHD	12	7.9
Gender			Others	5	3.3
Male	104	68.4	Total	152	100
Female	48	31.6	Total work Experience
Total	152	100	0–5 years	69	45.4
Position levels			5–10 years	51	33.5
Entry-level executives (Trainee Software Engineer, Trainee Software Developer, Software Architect, Design Engineer, Application Developer, Product Engineer)	109	71.7	10 years and more	32	21.1
Professional-level executives (Sr. Software Engineer, Sr. Software Developer (PHP), Sr. Software Developer (Dot Net), Software Analyst, Sr. Software Developer (Web design), Senior QA Analyst)	25	16.4	Total	152	100
Senior management-level executives (Product Head, Sr. Analyst, Tech Lead, Project Manager, Service Head)	12	7.9
Top management-level executives (Directors, CEOs)	6	3.9
Total	152	99.9

Source: Author compilation of primary data.

Table 3.

Items Development

S. No.	Dimension	Sub-dimensions	Item Quantity	Measuring Instrument
1	Group structure/enabling structure	Task design (TD), team composition (TC), group norms (GN)	TD = 8 itemsTC = 9 itemsGN = 3 items	Adapted from Wageman et al. (2005)
2	Mastery approaches	Team learning orientation (TL), team flexibility (TF)	TL = 19 itemsTF = 6 items	Adapted from Bresó, Gracia, Latorre and Peiró (2008) and McComb, Green and Dale Compton (2007)
3	Social capital	Network ties (NT), shared language (SL), trust (TR)	NT = 4 itemsSL = 4 itemsTR = 3 items	Adapted from Lee, Park and Lee (2013) and Cronin and Weingart (2006)
4	Collective efficacy	Perceived efficacy of team members (CM), perceived efficacy for collective team action (CC)	CM = 3 itemsCC = 8 items	Adapted from Fulgencio and David (2013)

Source: Author compilation of primary data.

Questionnaire Development

Team resilience scale was developed using four dimensions (group structure (GS), mastery approaches (MA), social capital (SC) and collective efficacy (CE)) and 10 sub-dimensions (task design, team composition, group norms, team learning orientation, team flexibility, network ties, shared language, trust, perceived efficacy of team members and perceived efficacy for collective team action) as identified from review of literature. To create an item pool for each indicator and its sub indicator, items were adapted from the existing scales. Table 3 depicts the measurement of all the constructs used in the study including the literature sources from where the items are adapted. The team resilience instrument was applied using five-point Likert scale ranging from strongly disagree to strongly agree. Based on the results of pretesting, context specific adjustments were made to the final version of the questionnaire.

Results

The data were analyzed using SPSS-21 and AMOS-21. The study focuses on the six stages for the scale development which starts with the generation of items based on the extensive literature review, following the data collection stage (Refer Figure 1). Further the data was analyzed for normality, multicollinearity followed by the initial item reduction using Exploratory Factor Analysis (EFA) and reliability analysis to suggest a set of indicators/factors to take ahead into confirmatory research.

Figure 1.

Source: Author compilation of primary data.

First of all the data were checked for normality, outliers and multicollinearity. Univariate outliers were checked via Z-score values in SPSS (z = ±3.29 (p < 0.001, two-tailed test)) (Tabachnick & Fidell, 2007), depicting no abnormal or outside the range of predictable values. Table 4 represents mean, standard deviation, skewness and kurtosis for each item. In terms of standard deviation, there was a range from 1.09 to 1.50. Skewness (≤ |0.93|) and kurtosis (≤ |1.387|) results confirmed that none of the items were greater than the suggested cut-off point’s of |3.00| and |8.00|, respectively, pointing that data are free from univariate non-normality (Kline, 1998). Further, data were screened for instances of multicollinearity via analysis of tolerance (TOL) and variance inflation factor (VIF). Multicollinearity was not present as all TOL indices were >0.10 and all VIF measures were < 3 (Hair et al., 2010).

Table 4.

Descriptive Statistics

Items	Mean	Standard Deviation	Skewness	Kurtosis	Items	Mean	Standard Deviation	Skewness	Kurtosis
TL_1	2.86	1.467	0.076	–1.387	TR_2	3.39	1.168	–0.294	–0.916
TL_2	3.03	1.339	–0.111	–1.162	TR_3	3.46	1.255	–0.521	–0.784
TL_3	2.98	1.421	–0.021	–1.331	TC_1	3.05	1.252	–0.129	–1.038
TL_4	2.99	1.400	–0.035	–1.283	TC_3	3.57	1.221	–0.546	–0.572
TL_5	2.94	1.358	–0.084	–1.258	TC_4	3.50	1.245	–0.459	–0.732
TL_7	2.95	1.432	0.011	–1.339	TC_6	3.15	1.144	–0.248	–0.776
TL_9	2.89	1.417	0.101	–1.334	TC_8	3.24	1.240	–0.094	–0.982
TL_11	2.85	1.233	0.078	–1.061	TC_9	3.41	1.204	–0.431	–0.593
TL_12	2.86	1.435	0.135	–1.367	TD_1	3.34	1.234	–0.322	–0.935
TL_13	3.00	1.395	–0.133	–1.283	TD_2	3.42	1.242	–0.300	–0.955
TL_14	2.38	1.395	0.492	–1.132	TD_3	3.39	1.246	–0.371	–0.897
TL_15	2.89	1.407	–0.001	–1.307	TD_5	3.41	1.289	–0.386	–0.945
TL_17	3.16	1.369	–0.255	–1.119	TD_6	3.39	1.262	–0.401	–0.849
TL_18	3.01	1.369	–0.090	–1.256	TD_8	3.46	1.144	–0.385	–0.706
TF_2	3.66	1.219	–0.719	–0.369	GN_1	3.52	1.173	–0.509	–0.576
TF_3	3.51	1.202	–0.735	–0.255	GN_2	3.68	1.159	–0.599	–0.435
TF_5	3.52	1.255	–0.636	–0.541	GN_3	3.10	1.244	–0.168	–0.950
TF_6	3.49	1.352	–0.581	–0.786	CM_1	3.72	1.329	–0.872	–0.420
NT_2	3.26	1.142	–0.519	–0.507	CM_2	3.68	1.304	–0.792	–0.569
NT_3	3.30	1.086	–0.534	–0.265	CM_3	3.71	1.290	–0.851	–0.369
NT_4	3.22	1.196	–0.452	–0.681	CC_1	3.67	1.316	–0.820	–0.461
SL_1	3.74	1.114	–0.685	–0.295	CC_2	3.63	1.291	–0.617	–0.736
SL_2	3.76	1.116	–0.577	–0.601	CC_3	3.71	1.269	–0.758	–0.519
SL_4	3.69	1.129	–0.569	–0.599	CC_6	3.63	1.290	–0.783	–0.476
TR_1	3.43	1.108	–0.542	–0.491	CC_8	3.79	1.285	–0.928	–0.171

Source: Author compilation of primary data.

Exploratory Factor Analysis

An exploratory principal component factor analysis with varimax rotation was conducted to assess the 67 items team resilience measurement scale. For measuring the appropriateness of factor analysis, Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity was used. The former one ensured the overall measure of sampling adequacy with a value of 0.772 (> 0.50) (Kaiser, 1974) and the latter statistics supported for the validity of the instrument with a value of 7284.184, df = 2145, significant at p = 0.000 (Stevens, 2012). Ten factors with eigenvalues >1 were extracted (Fabrigar et al., 1999) and after rotation their values were 10.333, 6.164, 4.402, 3.825, 3.507, 3.251, 2.633, 2.567, 2.542 and 2.504. Further the sum of squared loadings from the 10 components had the cumulative value of 63.224 per cent in elucidating the total variance in the data.

Throughout the progression of EFA items were deleted that did not load accurately on any factor (< 0.40) or depicts the cross loadings on other factors (Field, 2013). Moreover, researcher also kept in mind that the variables with communalities < 0.5 are troublesome items and need attention (Field, 2013). Based on this criterion, TL_6, TL_8, TL_10, TL_16, TL_19, TF_1, TF_4, NT_1, SL_3, TC_2, TC_5, TC_7, TD_4, TD_7, CC_4, CC_5 and CC_7 were deleted. The next round of EFA was conducted with the remaining 50 items. The EFA with varimax rotation successfully yielded 10 factors based on the Eigen value cut-off of one. The refined model explained 73.156 per cent of cumulative variance. The 50 items were split into 10 factors: learning orientation, adaptive capacity, network ties, shared language, trust, team composition, task design, group norms, perceived efficacy of team members, perceived efficacy for collective team action. The KMO (0.843) and Bartlett’s test of sphericity (p = 0.000) were significant. All the items exceed the cut-off of 0.5 for communalities as given by Field (2013). Further none of the items correlates too highly (r > 0.8 or r < −0.8) or too lowly (−0.3 < r < 0.3) (Field, 2013) with other items. The application of EFA on IT executives data resulted in same nine factors even after about 4–5 iterations (refer Table 5).

Reliability Analysis

The reliability analysis of refined model with 50 items and 10 factors was undertaken to verify how strongly the attributes are associated with each other (Hair et al., 2010). Cronbach’s alpha for the full scale came to be 0.835. The reliability test is deemed to be acceptable when the Cronbach’s alpha value exceeds the Nunnally’s reliability criterion of 0.70 level (Hair et al., 2010). Dimension-wise the value of Cronbach’s alpha came to be 0.754 for team learning orientation, 0.851 for team flexibility, 0.865 for network ties, 0.879 for shared language, 0.875 for trust, 0.737 for team composition, 0.815 for task design, 0.774 for group norms, 0.853 for perceived efficacy of team members and 0.723 for perceived efficacy for collective team action (refer Table 5).

Table 5.

Result of Exploratory Factor Analysis and Reliability Analysis (Cronbach’s Alpha)

Item Codes	Items	Factors	Loadings*	Cronbach’s Alpha
TL_1	Mistakes are openly discussed in the team in order to learn from them	Team Learning Orientation	0.739	0.754
TL_2	Differences between real and expected performance are critically and constructively analyzed in my team		0.800
TL_3	The lessons learned are made available to all the team members		0.772
TL_4	Actions are taken in the team to continuously improve the performance		0.736
TL_5	Even when an error is caught in time, team members are still told about it, so it does not happen again		0.870
TL_7	The same mistakes are made over and over again in the team		0.814
TL_9	Team members are encouraged to ask ‘why’, regardless of their rank		0.729
TL_11	We question each other when we think the work can done better		0.678
TL_12	We learn from each other in my team		0.751
TL_13	Knowledge is shared among the different team members		0.877
TL_14	Teamwork is encouraged as a way of learning from others		0.597
TL_15	In team discussions, everyone’s opinion is taken into consideration		0.831
TL_17	Our boss continuously looks for learning opportunities for him/herself or any team member		0.815
TL_18	Our boss uses different strategies to encourage team members to acquire new knowledge (e.g., assigning new tasks, sharing tasks …).		0.836
TF_2	Team members adjust their approach(es) to overcome obstacles	Team Flexibility	0.816	0.851
TF_3	Team members easily handle a variety of tasks		0.819
TF_5	The team frequently experiments with alternative ways we might accomplish our work		0.922
TF_6	The team is highly imaginative in thinking about new or better ways to complete our task(s)		0.848
NT_2	Team mates maintains close social relationships with each other	Network Ties	0.849	0.865
NT_3	Team members effectively communicate with one another		0.900
NT_4	Team members share necessary information with one another		0.867
SL_1	Team Members try to use common terms for work	Shared Language	0.891	0.879
SL_2	Team members use understandable communication patterns during discussions/meetings		0.897
SL_4	Team members try to understand each other during work cooperation		0.832
TR_1	I believe my team members trust each other	Trust	0.862	0.875
TR_2	I have little faith that my teammates will consider my needs when making decision		0.896
TR_3	I believe my teammates are truthful and honest		0.871
TC_1	My team is larger than it needs to be	Team Composition	0.581	0.737
TC_3	My team is just the right size to accomplish its purpose		0.697
TC_4	Members of my team are too dissimilar to work together well		0.770
TC_6	My team has a nearly ideal ‘mix’ of members—a diverse set of people who bring different perspectives and experiences to the work		0.791
TC_8	Everyone in my team has the special skills that are needed for teamwork		0.792
TC_9	Some members of my team lack the knowledge and skills that they need to do their parts of the teams work		0.811
TD_1	We do a whole, identifiable piece of work	Task Design	0.839	0.815
TD_2	My team does such a small part of the overall task that it is hard to point specifically to our special contribution		0.828
TD_3	My team’s work is inherently meaningful		0.858
TD_5	The work we do requires the team to make many ‘judgement calls’ as we carry it out		0.721
TD_6	Carrying out my team’s task automatically generates trustworthy indicators of how well we are		0.826
TD_8	The only way we can figure out how well we are performing is for other people in the organization to tell us		0.836
GN_1	Standards for members behaviour in my team are vague and unclear	Group Norms	0.824	0.774
GN_2	It is clear what is—and what is not—acceptable member behavior in my team		0.820
GN_3	Members of my team agree about how members are expected to behave		0.712
CM_1	I have confidence that my team members can perform tasks that are assigned to them	Perceived Efficacy of Team Members	0.856	.853
CM_2	The members of my team are capable of doing their share of work whenever asked		0.853
CM_3	Most of my team members are capable of handling responsibility		0.850
CC_1	My team is capable of helping a team member solves his/her problem	Perceived Efficacy for Collective Team Action	0.779	0.723
CC_2	My team can work together in order to accomplish a goal		0.839
CC_3	I believe in my team’s ability to do things together		0.868
CC_6	My team can handle the most difficult situations		0.835
CC_8	Together our team is able to solve problems		0.565

Source: Author compilation of primary data.

*Extraction method: Principal component analysis.

Rotation method: Varimax with Kaiser normalization. Rotation converged in 5 iterations

Proposed Hierarchical, Multidimensional Team Resilience Research Model

Based on the results of exploratory factor analysis, reliability analysis and qualitative findings, a conceptual model of team resilience is proposed (refer Figure 2) with the aim to measure its dimensions and sub-dimensions. The authors proposed team resilience as a hierarchical reflective model with four prime dimensions (i.e., mastery approach, social capital, group structure and collective efficacy) and 10 sub-dimensions (i.e., team learning orientation, team flexibility, network ties, shared language, team composition, task design, group norms, perceived efficacy of team members and perceived efficacy for collective team action). On the basis of decision criteria given by Jarvis, MacKenzie and Podsakoff (2003), Howell, Breivik and Wilcox (2007) and Petter, Straub and Rai (2007), the authors contented that the team resilience is a higher order, multidimensional reflective model and the supporting facts are given below.

The model is reflective in nature because all the constructs comprising team resilience are reflective, as the conjectural direction of causality is from construct to items (refer Figure 2 and Table 6). The model reveals that the indicators demonstrate construct that is all the items under a construct partake one common theme. For example, network ties is manifested by three items ‘team members effectively communicate with one another, team members share necessary information with one another and teammates work closely with each other and with supervisor’, share one common theme. Moreover, the results of exploratory factor analysis and reliability analysis substantiates the reflective nature of the team resilience model as the internal consistency was significant and inter-correlations between the items constituting a construct was highly positive.

Figure 2.

Source: Author compilation of primary data.

Confirmatory Factor Analysis

To confirm the higher order nature of measurement scale structural equation modelling using confirmatory factor analysis is utilized. Confirmatory factor analysis using maximum likelihood (ML) estimation was used to examine the hypothesis regarding number of factors their loadings and factor inter correlations. With an aim to evaluate the model fit the independence model was compared to the hypothesized model. Results of the study revealed that the independence model, which tests the hypothesis that all variables are uncorrelated, appeared to be a poor fit for the data and therefore should be rejected, χ² (1225, N = 152) = 6674.144, p < 0.005, χ²/df = 5.448 (refer Table 7). The literature reveals that the preferable value for χ² should be small and its associated probability value should be greater than the selected significance level. However, this statistics is extremely sensitive to sample size; it would reject almost every reasonable model in a great statistics power condition (Raykov, Tomer, & Nesselroade, 1991). Alternatively, acceptable model fit is specified by χ²/df values < 5 (Taylor & Todd, 1995). So the significance of the χ² test was discounted in the study and other goodness of fit indices were checked to access the model fit for hypothesized five-factor model (refer Figure 3). Because of lack of consensus on the preferred indices of fit in the literature (Bentler, 1990; Hu & Bentler, 1995; Kline, 1998), the researcher decided to rely on multiple goodness of fit indices, residual error terms, modification indices and the accompanying expected parameter change, as supported by the study of Arbuckle and Wothke (1999).

Table 6.

Nature of Reflective Team Resilience Model

Reflective Team Resilience Model	Explanation for Team Resilience as Reflective Model
X i = m i Y + f iwhere Xi = the ith indicatorY = the reflective construct(e.g., learning orientation)λi = coefficient which measures the expected effect of Y on the ith indicatorεi = the measurement error for the ith indicator	• All the constructs in the model are reflective in nature• Direction of causality is from construct to items• Indicators are the mirror of a construct• Any change in the construct causes relative change in the indicators and Indicators represents a common theme meaning thereby if one drops any indicator it should not change the conjectural domain of the construct• Furthermore, indicators are expected to covary with each other

Reflective Team Resilience Model

Explanation for Team Resilience as Reflective Model

X i = m i Y + f iwhere Xi = the ith indicatorY = the reflective construct(e.g., learning orientation)λi = coefficient which measures the expected effect of Y on the ith indicatorεi = the measurement error for the ith indicator

• All the constructs in the model are reflective in nature• Direction of causality is from construct to items• Indicators are the mirror of a construct• Any change in the construct causes relative change in the indicators and Indicators represents a common theme meaning thereby if one drops any indicator it should not change the conjectural domain of the construct• Furthermore, indicators are expected to covary with each other

Source: Adapted from Jarvis et al. (2003), Howell et al. (2007), Petter et al. (2007).

Table 7.

Summary of Goodness-of-Fit Indices for Alternate Models for Team Resilience Instrument

	Absolute Fit Indexes	Incremental Fit Indexes	Parsimony-adjusted Measures
	χ²(df)	P value	χ²/df	SRMR	CFI	TLI	RMSEA	PCLOSE
Optimal value	_	> 0.05^a	< 3.0^b	< 0.08^a	> 0.90^c	> 0.95^d	< 0.06^d	> 0.05^a
Hypothesized first order ten factor model	1,519.002(1,130)	0.000	1.344	0.059	0.929	0.923	0.048	0.722
Second-order 10-factor model	1,553.386(1,159)	0.000	1.340	0.069	0.928	0.924	0.047	0.748
Third-order 10-factor model	1,609.167(1,164)	0.000	1.382	0.072	0.918	0.918	0.050	0.459

Source: Author compilation of primary data.

Notes: NFI = Normed fit index; CFI = Comparison fit index; TLI = Tucker-Lewis index; RMSEA = Root mean square error of approximation; SRMR = Standardized root-mean square residual; PCLOSE = P of close fit.

^aHair, Black, Babin and Anderson (2010).

^bKline (1998).

^cBentler (1990).

^dHu and Bentler (1999).

Assessing First-order 10-factor Scale

The Confirmatory Factor Analysis (CFA) results stated in Table 7 reveals that the χ² (1130, N = 152) = 1519.002, p < 0.005, SRMR = 0.059, CFI = 0.929, TLI = 0.923, RMSEA = 0.048 and PCLOSE = 0.722 represents the good model fit. Furthermore, a closer look at standardized residuals and modification indices (MI) supported the model’s significant fit with no residual value > 2.58, a value above this is considered as large and an indicator of model misfit (Jöreskog & Sörbom, 1988). Additionally, the model reveals no large covariance between any of the error terms, which again supports the model fit results.

Further to access the convergent validity of the model, factor loadings, composite reliability and the average variance extracted (AVE) are estimated. Table 8 clearly reveals that factor loadings for all the items surpass the acceptable criteria of 0.5 (Fornell & Larcker, 1981; Hair et al., 2010). The composite reliability exceeds the acceptable criteria of 0.7 (Fornell & Larcker, 1981; Hair et al., 2010) for all the factors and the AVEs for all latent variables is greater than the threshold value of 0.5 (Fornell & Larcker, 1981). Beside this, it is seen that the composite reliability for all the factors are greater than the AVEs (Hair et al., 2010). Overall, the model shows no convergent validity issues, depicting the latent factors are well explained by its observed variables.

Figure 3.

Source: Author compilation of primary data.

Table 8.

Hierarchical Team Resilience Scale Psychometric Properties

Constructs	No. of Items	Factor Loading (s)	CR	AVE	MSV	ASV	Constructs	CR	AVE	MSV	ASV	Construct	CR	AVE
Optimal Value		>0.5^a	>0.7^a	>0.5 ^a	–	–		>0.7 ^a	>0.5 ^a	–	–		>0.7 ^a	>0.5^a
Team learning orientation	14	0.528–0.898	0.871	0.588	0.319	0.067	Mastery approach-es	0.814	0.687	0.066	0.050		0.866	0.62
Team flexibility	4	0.672–0.966	0.879	0.652	0.319	0.041
Network ties	3	0.825–0.866	0.891	0.732	0.279	0.080	Social capital	0.804	0.579	0.120	0.080
Shared language	3	0.831–0.972	0.948	0.859	0.328	0.117
Trust	3	0.813–0.866	0.871	0.692	0.328	0.076
Team composition	6	0.613–0.812	0.872	0.533	0.310	0.084	Group structure	0.838	0.633	0.120	0.085	Team Resilience
Task design	6	0.706–0.938	0.944	0.738	0.310	0.117
Group norms	3	0.711–0.977	0.863	0.682	0.262	0.071
Perceived efficacy of team members	3	0.790–0.860	0.871	0.693	0.195	0.049	Collective efficacy	0.766	0.622	0.088	0.079
Perceived efficacy for collective team action	5	0.539–0.935	0.862	0.576	0.195	0.039

Source: Author compilation of primary data.

Notes: CR = Composite reliability; AVE = average variance extracted; MSV = maximum shared variance; ASV = Average shared variance

^aHair et al. (2010).

Moreover, as suggested by Hair et al. (2010), there were no ‘cross-loadings’ in the factor structure obtained from EFA results (refer Table 5). Further, the authors suggested that the discriminant validity can be evaluated by comparing the maximum shared variance (MSV) with AVE (MSV < AVE) and by comparing average shared variance (ASV) with AVE (ASV < AVE) (Hair et al., 2010). The results in the table clearly shows that the MSV and the ASV, both are lower than the AVE for all of the constructs in the scale (refer Table 8). This means the indicators have more in common with the construct they are associated with than they do with other constructs thereby representing good discriminant validity in the model. From the results it can be interpreted that constructs are truly distinct from other constructs (i.e., unidimensional), in nature.

Assessing the Higher-order Scale

For the higher-order reflective model approximation, the study applies the manifest variables for the first-order, the second-order and lastly for the third-order loadings. The results from the table showed that the value of CR exceeds the acceptable criteria of 0.7 (Fornell & Larcker, 1981; Hair et al., 2010) for all the factors and the AVEs for all latent variables is greater than the threshold value of 0.5 (Fornell & Larcker, 1981) for both the second-order and third-order measures, thereby providing the evidence that team resilience is a reliable higher order measure (refer Table 8).

The results supported that team resilience is a third-order construct having a strong association with the second-order constructs of mastery approaches (β = 0.71), social capital (β = 0.80), group structure (β = 0.86) and collective efficacy (β = 0.77) that individually explained 50, 64, 74 and 59 per cent, the overall team resilience variance, respectively. Furthermore, the results confirmed the strong association of second-order constructs with their respective first-order constructs. For example, group structure was represented by team composition (β = 0.68), task design (β = 0.80) and group norms (β = 0.71) and task design plays a major role by explaining 64 per cent of group structure variance (refer Figure 3). CFA results also reveal that all the path coefficients from team resilience to second-order and third-order dimensions are statistically significant. Thus, the authors found that 50 items, clubbed into 10 factors could be used to evaluate the team resilience construct.

Discussion

With a broader aim to develop and validate an instrument for measuring team resilience, this empirical study has been conducted on the sample of IT executives. The team resilience measurement instrument that aims to discover the basic characteristics that protects the team members from negative stressors and help them adapting to these adversities, is based on the comprehensive analysis of individual, team and organizational resilience literature.

The study scrutinized the most cited factors that influence team resilience in the literature and found the Morgan et al. (2013) framework of team resiliency as the most widely explored theoretical scaffold and adopted the four facilitating factors named as group structure, mastery approaches, social capital and collective efficacy as the major indictors for developing the scale. The research on group structures identified task design, task composition and group norms as three structural features fostering teamwork in the groups (Wageman et al., 2005; Weick, 1993). The author relied on team learning orientation and team flexibility as the two quantifiable sub-constructs for defining the indicator of mastery approaches (Kaufman, 1985; Näswall et al., 2013). Based on social capital theory given by Putnam (1995), network ties, shared language and trust were selected as the major constructs for measuring team social capital in the study. For measuring collective efficacy, author relied on two factors that describe a team’s perceived ability to undertake collective action/work as a unit, face adversities together and muddle issues within the team, and beliefs on the ability of team members based on Bandura (1997) concept of self-efficacy and group efficacy as two major constituents of collective efficacy. These dimensions and sub-dimensions were operationalized into 67 items that was subsequently reduced to 50 items questionnaires to which 152 executives from 12 IT firms responded correctly. The items were adapted from the existing scales available in the literature.

The study proposed team resilience as a hierarchical, multidimensional, reflective model with four primary dimensions (i.e., mastery approach, social capital, group structure and collective efficacy) and 10 sub-dimensions (i.e., team learning orientation, team flexibility, network ties, shared language, team composition, task design, group norms, perceived efficacy of team members and perceived efficacy for collective team action). Various rounds of empirical validation including the exploratory factor analysis, confirmatory factor analysis and reliability analysis supported the third-order, hierarchical, reflective, 10-factor team resilience scale. The study demonstrated sound psychometric properties of the scale.

The results demonstrated that team resilience is a quantifiable construct, assessment of which is necessary for improving the team performance. The team resilience scale may prove most useful to the practitioners. The instru-ment may be used as a diagnostic tool for identifying the resilient team characteristics and thereby acts as a starting point for increasing resilience. The team resilience scale may be used repeatedly by the managers to access the effectiveness of any resilience building intervention being initiated in the organization. Moreover, identifying teams with lower resilience scores may assist organizations in tailoring strategies for improving the team effectiveness.

The questionnaire is administrated among the employees of one specific industry, that is, IT and sample is collected from a single country which may be treated as a limitation of the study but the questionnaire is designed in such a way that its application can be generalized to any domain and to any country. This study investigates the internal validity of the team resilience scale. Further research could scrutinize the link between team resilience and other resilience scales.

Footnotes

Acknowledgements

The authors sincerely thank Paul Morgan and Jill Flint-Taylor for their patience, constructive feedback and insightful advice on the topic.

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