Fostering Ambidextrous Innovation Strategies in Large Infrastructure Projects
A Team Heterogeneity Perspective
Zhang, Xinyue; Le, Yun; Liu, Yan; Chen, Xiaoyan DOI
10.1109/TEM.2021.3074431 Publication date
2021
Document Version
Accepted author manuscript Published in
IEEE Transactions on Engineering Management
Citation (APA)
Zhang, X., Le, Y., Liu, Y., & Chen, X. (2021). Fostering Ambidextrous Innovation Strategies in Large Infrastructure Projects: A Team Heterogeneity Perspective. IEEE Transactions on Engineering Management, 1-11. https://doi.org/10.1109/TEM.2021.3074431
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Fostering Ambidextrous Innovation Strategies in
Large Infrastructure Projects: A Team
Heterogeneity Perspective
1
2
3
Xinyue Zhang, Yun Le, Yan Liu
, and Xiaoyan Chen
4
Abstract—In emerging economies, infrastructure projects are 5
in full swing. There is a wealth of replicable experience for
ex-6
ploitation. Simultaneously, more technologies and methodologies
7
require further exploration. This makes fostering ambidextrous
8
innovation strategies (i.e., the tradeoff between exploitative and
9
exploratory innovation strategies) a common and vital practical
10
issue. Large infrastructure projects are unique one-off endeavors
11
but have somewhat repetitive and persistent characteristics. It is
12
a particular “intermediate” form between temporary projects and
13
permanent organizations. Previous research on fostering
ambidex-14
trous innovation strategies cannot simply be replicated in large
in-15
frastructure projects. To address this issue, this article investigates
16
the relationship between team heterogeneity and ambidextrous
17
innovation strategies and also the role of team learning and
identi-18
fication in large infrastructure projects. Data were collected from
19
269 responses from 31 large infrastructure project delivery teams
20
in China. The findings show that team heterogeneity has a positive
21
linear effect on exploratory and ambidextrous innovation strategies
22
and an inverted U-shaped effect on exploitative innovation
strate-23
gies; team heterogeneity can better foster ambidextrous innovation
24
strategies through improving team learning; the moderating role
25
of team identification in the overall mechanism differs from the
26
usual assumptions in permanent organizations. Overall, this article
27
extends the existing ambidexterity research in the “intermediate”
28
form between temporary projects and permanent organizations. It
29
provides insights and guidance on fostering ambidextrous
innova-30
tion strategies in large infrastructure projects.
31
Index Terms—Ambidextrous innovation strategies, large
32
infrastructure project, team heterogeneity, team identification
33
(TI), team learning (TL).
34
I. INTRODUCTION
35
T
HE vast majority of large infrastructure projects deal with36
universal human needs, including transport, energy, water
Q1
37
supply, and waste treatment in economic activities [1]. They
38
are characterized by being bespoke, one-off, and different
cul-39
tures merging together [2]. Innovation plays a unique role in
40
Manuscript received September 24, 2020; revised January 8, 2021 and March 5, 2021; accepted April 4, 2021. Review of this manuscript was arranged by Department Editor Y. H. Kwak. (Corresponding author: Yan Liu.)
Xinyue Zhang, Yun Le, and Xiaoyan Chen are with the School of Eco-nomics and Management, Tongji University, Shanghai 200092, China (e-mail:
Q2
xinyue_cinyea@163.com; leyun@kzcpm.com; chenxiaoyanfeiwu@163.com). Yan Liu is with the Faculty of Civil Engineering and Geosciences, Delft Uni-versity of Technology, CN 2628 Delft, Netherlands (e-mail: y.liu-9@tudelft.nl).
Digital Object Identifier 10.1109/TEM.2021.3074431
leading positive technical and managerial change during the 41
management of these projects [3]. However, there is a dilemma 42
of innovation in large infrastructure projects. Merely relying 43
on the incremental improvement of proven technologies and 44
routines may not satisfy the increasing design and construction 45
requirements [4], [5]. Substantial risks in the long term and 46
the one-off characteristic often make most parties reluctant to 47
introduce breakthrough innovations [2], [6], [7]. Especially in 48
emerging economies such as China, a large number of large 49
infrastructure projects are under construction, providing a great 50
deal of replicable experience. At the same time, the devel- 51
opment of breakthrough innovations requires facilitation and 52
exploration. This makes balancing exploration and exploitation 53
a common and vital practice issue. A recent notable case was 54
the Hong Kong-Zhuhai-Macao Bridge. It drew lessons from the 55
experience of previous cross-sea bridges, adopted and developed 56
new technical and managerial ideas due to the complex and 57
uncertain environment. It is essential but challenging to balance 58
exploitative and exploratory innovation strategies and maximize 59
their combined effects [8]–[10]. 60
There has been some discussion in permanent organizations 61
and temporary projects about fostering ambidextrous innova- 62
tion [11], [12]. Nevertheless, large infrastructure projects last 63
for years or even decades [13], making them different from 64
general temporary projects, with some repetitive characteristics 65
and some degree of persistence. Also, different from permanent 66
organizations, they are unique one-off endeavors [14]. Eriksson 67
[13] argued that large infrastructure projects could be conceived 68
as hybrids of temporary projects and permanent organizations. 69
Brookes et al. argued that large infrastructure projects, as long- 70
term projects, differ in many issues from temporary projects 71
and permanent organizations [15]. In fostering ambidextrous 72
innovation, temporary projects are often seen as an excellent 73
context for exploratory innovation due to their unique tasks 74
[11]. At the same time, permanent organizations benefit from the 75
accumulated knowledge base and are often considered beneficial 76
to exploitative innovation [15]. How ambidextrous innovation 77
can be fostered in large infrastructure projects that combine 78
the characteristics of both temporary projects and permanent 79
organizations cannot merely replicate the findings of previous 80
studies. 81
In the context of large infrastructure projects, much of 82
the existing research has focused on the importance of 83
0018-9391 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
IEEE Proof
ambidextrous innovation strategies [16] and their positiveim-84
pact on performance [17]. Ex-ante ambidextrous innovation
85
strategies during the project management process remain
under-86
explored. The formation of the project delivery team is critical in
87
how to foster ambidextrous innovation. We take this perspective
88
to bridge the gap in the ambidexterity literature in the large
89
infrastructure project context. Some organization studies have
90
explored the role of team heterogeneity in facilitating
ambidex-91
trous innovation [18], [19]. Haans et al. [20] offered the outlook
92
that the effect of team heterogeneity on ambidexterity is likely
93
not linear but inverted U-shaped. However, no empirical studies
94
have yet been conducted to prove this. Besides, ambidexterity
95
requires the coexistence of two essentially different strategies,
96
which creates paradoxical challenges. In this sense, team
hetero-97
geneity provides the conditions needed to achieve ambidexterity
98
and paradoxically encourages disagreements and conflicts [18],
99
especially in large infrastructure projects with complex tasks. In
100
addition to directly linking team heterogeneity to ambidextrous
101
innovation strategies, integrated team process and climate may
102
also be critical for team heterogeneity to foster effective
am-103
bidextrous innovation strategies. Such empirical evidence would
104
be a valuable contribution to the ambidexterity literature. Hence,
105
this article aims to answer the following research questions: (1)
106
what is the impact of team heterogeneity on ambidextrous
inno-107
vation strategies in large infrastructure projects? (2) Can team
108
heterogeneity better foster ambidextrous innovation strategies
109
through the integrated team process and climate?
110
This article collected data from large infrastructure projects
111
in China to address the above questions and first examined the
112
effects of team heterogeneity on exploratory, exploitative, and
113
ambidextrous innovation strategies. Given that team learning
114
(TL) is a meaningful construct that involves improving
work-115
flow, handling disagreements, obtaining information,
collab-116
oration, etc. [21], this article examined whether it mediates
117
between team heterogeneity and ambidextrous innovation
strate-118
gies as an integrated team process. Since large infrastructure
119
projects are undertaken by temporary inter-organizational teams,
120
we wanted to explore whether different team identification
121
(TI) (an integrated climate) affects the cultivation of
ambidex-122
trous innovation strategies in such a particular “intermediate”
123
form.
124
Considering the above, the article aims to provide insights
125
and empirical guidance on fostering ambidextrous innovation
126
strategies in large infrastructure projects. This article tests the
127
ambiguous relationship between team heterogeneity and
am-128
bidextrous innovation strategies by establishing two parallel
129
hypotheses and exploring the influence of TL and
identifica-130
tion. We argue that fostering ambidextrous innovation
strate-131
gies in large infrastructure projects is an essential expansion
132
of the existing ambidexterity theory in a particular
“interme-133
diate” form between temporary projects and permanent
orga-134
nizations. This article also provides new insights into
foster-135
ing ambidextrous innovation strategies in large infrastructure
136
projects in terms of team formation, process, and climate.
137
It provides empirical evidence for the ambiguous
relation-138
ship between team heterogeneity and ambidextrous innovation
139
strategies.
140
II. LITERATUREREVIEW ANDHYPOTHESES 141
A. Team Heterogeneity and Ambidextrous Innovation 142
Strategies 143
Team heterogeneity refers to the diversity of team members 144
and the differentiation of members in various aspects including 145
age, work experience, education level, and function diversity 146
[22]. Team heterogeneity is recognized as a critical antecedent 147
in predicting team outcomes, but it is controversial whether 148
the exact relationship is linear or curvilinear. One view holds 149
that team heterogeneity positively affects team outcomes (a 150
relatively linear relationship) [23]. They argue that team het- 151
erogeneity provides different types of knowledge and a wider 152
variety of professional perspectives, expands the scope of the 153
information collected, and inspires differences between solu- 154
tions, leading to more comprehensive decision making. Spe- 155
cific to ambidexterity, Koryak et al. [24] suggested that team 156
heterogeneity may positively impact ambidexterity. However, 157
another view claims that higher heterogeneity is not always 158
better, and the relationship between team heterogeneity and team 159
outcomes may be an inverted U-shaped relationship [25]. Teams 160
with high heterogeneity are more challenging to manage, and 161
their focus may become increasingly scattered [26]. When team 162
heterogeneity increases further, it may increase coordination 163
costs and decrease efficiency due to control losses and increasing 164
conflicts [27]. Given these dynamics, it can be considered that 165
the marginal costs of heterogeneity increase rapidly as it hits 166
high levels. 167
Further focusing on ambidexterity, Haans et al. [20] sug- 168
gested that the relationship between team heterogeneity and 169
ambidexterity is most likely an inverted U-shaped relationship. 170
When teams have to divide their attention and resources more 171
or less between exploration and exploitation, the coordination 172
cost of balancing the two is likely to be highest. In contrast, 173
the coordination cost of focusing on one or the other is much 174
lower [20]. This is because exploitation and exploration require 175
different structures, routines, and processes, and the integration 176
of the two involves tradeoffs across space and time [28]. The 177
coordination cost has been considered as a concave function 178
[20]. Especially in the interorganizational setting of large in- 179
frastructure one-off projects [29], the coordination cost of ex- 180
ploration and exploitation may increase faster. The relationship 181
between team heterogeneity and ambidexterity is likely to be an 182
inverted U-shaped relationship. However, there is no substantial 183
empirical evidence to support it, so we established two parallel 184
hypotheses: 185
H1a. There is a positive relationship between team het- 186
erogeneity and ambidextrous innovation strategies in 187
large infrastructure projects. 188
H1b. There is an inverted U-shaped relationship between 189
team heterogeneity and ambidextrous innovation strate- 190
gies in large infrastructure projects. 191
B. Mediating Role of TL 192
Ambidexterity is increasingly recognized as a means to 193
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ambidexterity is considered a dynamic capability that evolves195
through a continuous process of experiential learning,
decision-196
making, and implementation [30], [31]. March linked innovation
197
and internal processes to expound tensions surrounding
explo-198
ration and exploitation [32]. Therefore, this article adopts a team
199
process perspective and introduces TL to explain how team
200
heterogeneity promotes ambidextrous innovation strategies in
201
large infrastructure projects. TL refers to the process in which
202
team members seek to acquire, share, refine, or combine
task-203
relevant knowledge and experience through interaction within
204
the team [33]. This process may include seeking information,
205
communicating with each other, challenging assumptions,
seek-206
ing different perspectives, addressing differences of opinion, and
207
reflecting on past actions [34].
208
The difficulty of cultivating team ambidexterity lies in that
209
exploration and exploitation originate from different learning
210
capabilities. Specifically, exploration refers to learning through
211
planned experimentation, while exploitation means learning
212
through experience refinement and reuse of existing routines
213
[35]. To foster ambidextrous innovation strategies, teams need to
214
focus on excavating existing knowledge to generate exploitative
215
innovation and acquire new knowledge to generate exploratory
216
innovation [36]. Some scholars held that team heterogeneity can
217
lead to the collision and integration of different perspectives,
218
which, in turn, affects the team’s exploitative and exploratory
219
learning behaviors [9], [37]. Focusing further on the large
in-220
frastructure project, Li et al. [38] claimed that team outcomes
221
depend on TL among team members from different parties. We,
222
therefore, infer that TL is needed in large infrastructure projects
223
to bridge team heterogeneity and ambidexterity. Thus, we put
224
forward the following hypothesis:
225
H2. The relationship between team heterogeneity and 226
ambidextrous innovation strategies in large infrastructure
227
projects is mediated by team learning.
228
C. Moderating Role of TI
229
TI refers to the emotional significance that team members
230
attach to their team membership [39]. It is noted that TI differs
231
from constructs such as team cohesion because TI is concerned
232
with the degree to which an individual identifies with the
233
team rather than the individual’s relationship with other team
234
members. Large infrastructure projects have a lifespan with
235
multiorganizational interfaces with a specific end date. Project
236
delivery team members in large infrastructure projects come
237
from different parties with diverse functions. In the end,
mem-238
bers separate and do not always work together on subsequent
239
projects. However, during the project, they have the shared goal
240
of successfully delivering the project [38]. It is vital to study TI
241
in the large infrastructure project context.
242
Van Der Vegt and Bunderson [39] held that the effects of
243
team heterogeneity on team processes and team outcomes are
244
considered to be different in teams with high and low TI. In
245
other words, TI can moderate the relationship between team
246
structure, team processes, and team outcomes. According to
247
Social Identity Theory, TI can create a climate of collaboration.
248
Specifically, different perspectives and knowledge originating 249
from the team heterogeneity should be actively shared, construc- 250
tively debated, and integrated into team goals [40]. Focusing 251
further on ambidexterity, when a team has a high level of TI, 252
the highly heterogeneous team will exchange information, learn 253
across functional boundaries, and better balance exploration 254
and exploitation, thereby promoting ambidextrous innovation 255
strategies. We come up with the following hypotheses: 256 H3a. Team identification moderates (reinforces) the rela- 257
tionship between team heterogeneity and ambidextrous 258
innovation strategies in large infrastructure projects. 259 H3b. Team identification moderates (reinforces) the re- 260
lationship between team heterogeneity and team learning 261
in large infrastructure projects. 262
H3c. Team identification moderates (reinforces) the 263
relationship between team learning and ambidex- 264
trous innovation strategies in large infrastructure 265
projects. 266
III. METHODS 267
A. Sample and Data Collection 268
Our unit of analysis is project delivery teams in large infras- 269
tructure projects. On the one hand, project delivery teams consist 270
of engineers and managers from various parties. They are the 271
center of the large infrastructure project network that transcends 272
different functional departments. On the other hand, project 273
delivery teams play a crucial governance role in providing 274
decision-making support for senior executives and convey their 275
strategies to various functional departments [41], [42]. In this 276
study, the respondents are members of project delivery teams, 277
most of whom are the heads of different functional departments. 278
We adopt the “snowball” and “maximum variation” strategies 279
of the purposeful sampling approach to guide our sample collec- 280
tion. Specifically, we obtained access to senior managers from 281
many large infrastructure projects based on the reliable contact 282
information provided by the two authors of this article. We 283
asked them to distribute electronic questionnaires to their project 284
delivery teams and contact more senior managers involved 285
in other projects. This purposeful sampling makes effective 286
use of limited data sources and guarantees the respondents’ 287
appropriateness and willingness to participate in the survey. 288
The “maximum variation” strategy means that we intentionally 289
collect different types of projects to improve the generalizability 290
of current research results. Finally, the investigated infrastruc- 291
ture projects include transportation (airports, bridges, subways, 292
railways, and highways), energy and hydropower, education 293
and health, amenity and utility facilities (parks, scenic spots, 294
environmental governance, and underground pipe gallery). The 295
diversity of infrastructure project types has dramatically im- 296
proved the representativeness of samples. In addition to the 297
targeted electronic questionnaire, we also collected on-site ques- 298
tionnaires. From November 2019 to April 2020, we collected 299
312 responses from 42 project delivery teams. If a team had less 300
than three valid respondents, we removed the whole team data. 301
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TABLE I
PROFILES OFINFRASTRUCTUREPROJECTS ANDRESPONDENTS
considered valid (see Table I for their profiles), with an effective
303
response rate of 86.2%.
304
B. Measures
305
1) Ambidextrous Innovation Strategies: To generate the scale
306
items, we drew on Mohammadali et al.’s [43] research
307
on infrastructure innovation classification, studies on
in-308
frastructure innovation [4]–[7], [14], [43]–[47], and the
309
ambidexterity scale developed by He and Wong in the
310
manufacturing context [48]. Through these analyses, we
311
initially developed 24 items that reflect exploratory and
312
exploitative innovation strategies in the context of large
313
infrastructure projects, as detailed in Appendix. We invited
314
eleven functional department managers from the project
315
delivery team in Shanghai Pudong international airport
316
phase IV extension project and five scholars specializing
317
in large infrastructure project management to participate
318
in the pretest. Before starting the pretest, these participants
319
were informed of our research purpose and the background
320
knowledge related to ambidextrous innovation strategies.
321
They were first asked to filter the question items from these
322
24 items, and by deleting, merging, and modifying them,
323
eight items finally emerged. Besides, they were asked
324
to assess whether the measurements were well worded
325
and interpreted in the large infrastructure project context,
326
ensuring the content validity of the scale items. Based
327
on their feedback, we finalized eight items to measure
328
ambidextrous innovation strategies for the formal
investi-329
gation, as shown in Table II.
Q3 330
We assessed these items using a scale ranging from 1 “not
331
important” to 5 “very important.” Factor analysis was performed
332
to test the validity of the scale [48]. As shown in Table II,
333
the eight items were reduced to two variables through factor
334
TABLE II
FACTORANALYSIS FORAMBIDEXTROUSINNOVATIONSTRATEGIESSCALE
Note. i.s.: innovation strategies. Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser normalization. Explained variance: 58.91%.
analysis, which can be interpreted as exploratory and exploita- 335
tive innovation strategies (Cronbach alphas are acceptable, 336
0.776, and 0.718). 337
Following the research of He and Wong [48] and Cao et al. 338
[49], we consider that ambidextrous innovation strategies are 339
composed of the “balance dimension of ambidexterity” (BD) 340
and “combined dimension of ambidexterity” (CD). BD is 341
related to the balance or relative magnitudes of exploratory and 342
exploitative innovation strategies, and it can be calculated by the 343
formula BD = 5 − |exploratory innovation strategies − 344
exploitative innovation strategies|. While CD concerns 345
the combined magnitude of exploratory and exploitative 346
innovation strategies, and it can be calculated by the 347
formula CD = exploratory innovation strategies × 348
exploitative innovation strategies [48]. 349
1) Team heterogeneity: The heterogeneity of team members’ 350
age [18], work experience [50], education level [51], 351
and functional department [18] were taken into account 352
to calculate team heterogeneity. Age, work experience, 353
and education level were provided with several ranges 354
or category options in the questionnaire. The respondents 355
could choose the corresponding choices according to their 356
actual situation. The functional department needed to be 357
filled in manually. The team heterogeneity was calculated 358
using Blau’s heterogeneity index, which uses the formula 359
H = 1 −p2
i, where p is the proportion of a team 360
in the respective diversity categories, and i is the num- 361
ber of different categories represented on the team [52]. 362
Manual calculations are complex and error-prone, so we 363
developed a program to simplify team heterogeneity cal- 364
culations through Python. The Blau’s heterogeneity index 365
ranges from 0 to a theoretical maximum of 1. The higher 366
the index, the more significant the heterogeneity among 367
team members. It is noted that team heterogeneity is a 368
team-level variable. The calculated value is based on all 369
team members’ demographic characteristics, so the team 370
heterogeneity index of all members in the same team is 371
consistent. 372
2) TL: Seven items were adapted from Edmondson [34] to 373
measure the direction and intensity of the efforts made in 374
TL. All the items were measured on a Likert scale ranging 375
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TABLE III
MEASUREMENTMODEL: LOADINGS, CONSTRUCTRELIABILITY,AND
CONVERGENTVALIDITY
3) TI: Following the study of Van Der Vegt and Bunderson,
377
four items were used to measure TI [39]. We assessed these
378
items using a scale ranging from 1 “completely disagree”
379
to 7 “completely agree.”
380
C. Data Analysis Method
381
Hierarchical regression analysis was used to test our
hypothe-382
ses. This technique allows examining nonlinear evidence of
sta-383
tistical associations and has been widely used in organization and
384
management research to assess curvilinear relationships [53].
385
First, we assessed the reliability and validity of the measures
386
(outer model) [54]. Second, we applied STATA to analyze our
387
moderated mediation model (inner model) through hierarchical
388
regression analysis. In detail, we constructed the baseline model,
389
mediation model, and moderated mediation model, respectively.
390
Besides, we measured the curvilinear relationship by
construct-391
ing a quadratic term [20] and measured the moderating effect by
392
constructing interaction terms.
393
IV. RESULTS
394
A. Measurement Model
395
As shown in Table III, the measurement model’s validity and
396
reliability are satisfactory for individual items and constructs.
397
Standardized indicator loadings evaluated the reliability of
in-398
dividual items. Among the seventeen items, nine items’
stan-399
dardized loadings were significantly higher than 0.7 [54]. Eight
400
items were around 0.6, higher than the threshold of 0.5 [54].
401
Composite reliability (CR) can be used to evaluate construct
402
reliability. Each construct’s CR scores exceeded the threshold
403
of 0.7 [54], which indicate acceptable reliability. The average
404
TABLE IV
IMPACT OFTEAMHETEROGENEITY ONEXPLORATORY, EXPLOITATIVE,AND
AMBIDEXTROUSINNOVATIONSTRATEGIES
Note. i.s.: innovation strategies.∗<.05,∗∗<.01,∗∗∗<.001.
variance extracted (AVE) values exceeded the threshold of 0.5 405
[54], which indicated good convergent validity. 406
B. Structural Model 407
In the baseline model [see Fig. 1(a), the baseline model], 408
before measuring the impact of team heterogeneity on am- 409
bidextrous innovation strategies, we measured the impact of 410
team heterogeneity on exploitative and exploratory innovation 411
strategies. Second, we tested whether TL mediates the effect of 412
team heterogeneity on ambidextrous innovation strategies and 413
whether this mediation effect is partial or full (see Fig. 1(b), the 414
mediation model). Third, we tested whether the indirect effect 415
of team heterogeneity on ambidextrous innovation strategies 416
through TL is moderated by TI (see Fig. 1(c), the moderated 417
mediation model). 418
As shown in Table IV, team heterogeneity has a significant 419
positive effect on exploratory innovation strategies (β = .775, 420
p< .001), but the quadratic effect is also significant (β = .074, 421
p< .05). To check robustness, drawing on Lind and Mehlum’s 422
U-shaped relationship validation procedure [55], we found that 423
the curve turning point is outside the data range, not a U-shaped 424
relationship. The relationship between team heterogeneity and 425
exploratory innovation strategies is positive and linear. Team 426
heterogeneity has no significant linear effect on exploitative 427
innovation strategies (β = .008, n.s.), and the quadratic effect 428
is significant (β = −.218, p < .001). Robustness checks were 429
also carried out, and we found that the relationship between 430
team heterogeneity and exploitative innovation strategies was 431
indeed an inverted U-shaped relationship. Team heterogeneity 432
has a significant positive effect on ambidextrous innovation 433
strategies (β = .736, p < .001). However, just like exploratory 434
innovation strategies, the quadratic effect, although significant 435
(β = .067, p < .05), has not passed the U-shaped relationship 436
validation procedure recommended by Lind and Mehlum [55]. 437
This means that the relationship between team heterogeneity 438
and ambidextrous innovation strategies is not U-shaped but 439
positive and linear. H1a is supported, and H1b is rejected. To 440
further validate and compare the effects of team heterogeneity 441
on exploratory, exploitative, and ambidextrous innovation strate- 442
gies, as shown in Fig. 2, we performed quadratic curve regres- 443
sions, again verifying that only the relationship between team 444
heterogeneity and exploitative innovation strategies is inverted 445
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Fig. 1. Models used to test mediation and moderation. (Note. i.s.: innovation strategies.)
Fig. 2. Impact of team heterogeneity on exploratory, exploitative, and am-bidextrous innovation strategies. (Note. i.s.: innovation strategies.).
As depicted in Fig. 1(b), the relationships between team
447
heterogeneity and TL (β = .795, p < .001), TL and ambidextrous
448
innovation strategies (β = .265, p < .01) are significant, and the
449
effect of team heterogeneity is significantly reduced (β = .501,
450
p < .001, non-U-shaped relationship), providing evidence for
451
partial mediation, supporting H2.
452
As noted, H3a, 3b, 3c predicted that TI would moderate
453
the associations between team heterogeneity and ambidextrous
454
innovation strategies through TL in large infrastructure projects.
455
As shown in Table V, in model TL, we estimated the moderating
456
effect of the TI on the relationship between team heterogeneity
457
and TL (β = −.086, p < .05), H3b was rejected. When the
458
level of TI is high, the positive impact of team heterogeneity
459
TABLE V
TESTING THEMODERATEDMEDIATIONMODELWITHBOOTSTRAPPING
Note. 5000 bootstrap samples. TH: Team heterogeneity; TL: Team learning; TI: Team identification; AIS: Ambidextrous innovation strategies.∗ < .05,∗∗< .01,∗∗∗< .001.
on TL is weakened, and the moderating effect of TI is negative. 460
In model ambidextrous innovation strategies, we estimated the 461
moderating effect of the TI on the relationship between team het- 462
erogeneity and ambidextrous innovation strategies (β = −.066, 463
n.s.). Simultaneously, we estimated the moderating effect of TI 464
on the relationship between TL and ambidextrous innovation 465
strategies (β = .107, n.s.). H3a and H3c were not significant. 466
V. DISCUSSION 467
A. Impact of Team Heterogeneity on Exploratory, Exploitative, 468
and Ambidextrous Innovation Strategies 469
The effects of team heterogeneity on exploratory and ex- 470
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U-shaped, respectively. With increased team heterogeneity,ex-472
ploratory and exploitative innovation strategies both increase
473
in the first stage. However, in the second stage, as team
het-474
erogeneity further increases, exploratory innovation strategies
475
continue to increase, while exploitative innovation strategies
476
tend to decrease. This can be explained as the team
hetero-477
geneity grows further, more innovation is inspired by more
478
diversified knowledge, but more coordination costs are
asso-479
ciated with more conflicts and distractions. In the pursuit of
480
exploratory innovation strategies, more innovation inspired by
481
diversification may be more prominent than the increase in
482
coordination costs. However, in the pursuit of exploitative
inno-483
vation strategies, the coordination cost increase brought by high
484
team heterogeneity overweighs more innovations stimulated.
485
Thus, an inverted U-shaped relationship is formed. We consider
486
that this may stem from the fundamental difference between
487
the pursuits of exploitation and exploration, with exploration
488
pursuing significant change while exploitation pursuing greater
489
efficiency.
490
The effect of team heterogeneity on ambidextrous innovation
491
strategies is also positively linear. In this article, ambidextrous
492
innovation strategies are measured by the balance dimension
493
and the combined dimension of the two innovation
strate-494
gies. In the first stage, the effects of team heterogeneity on
495
both innovation strategies are positive. However, in the
sec-496
ond stage, these two strategies’ effects are the opposite: the
497
two innovation strategies become increasingly unbalanced. It
498
shows that high team heterogeneity promotes exploratory
inno-499
vation strategies for more than it inhibits exploitative innovation
500
strategies.
501
This article is contrary to the inverted U-shaped effect of
502
team heterogeneity on ambidexterity speculated by Haans et al.
503
[20]. One possible reason for this is that Haans et al. did not
504
conduct an empirical study but only proposed such speculation
505
[20]. This difference may stem from the peculiarities of the
506
large infrastructure project context. The large infrastructure
507
project is a vital innovation ecosystem [56] and has to strike the
508
right balance of open and closed innovation [47]. Researchers
509
have investigated how innovation improves the performance and
510
frame the future of large infrastructure projects and the industry
511
[7], [57]. As a result, in large infrastructure projects, compared
512
with the cost increase brought by high heterogeneity, the
break-513
through brought by knowledge diversification may be more
514
significant.
515
B. Mediating Role of TL
516
As a dynamic integration process, TL partially mediates the
517
relationship between team heterogeneity and ambidextrous
in-518
novation strategies (H2). Team heterogeneity can better foster
519
ambidextrous innovation strategies by improving TL.
Tempo-520
rary projects are often seen as an excellent context for knowledge
521
creation due to their unique tasks, but their relative
imper-522
manence negatively impacts TL [58]. TL is often considered
523
to occur in permanent organizations because various factors
524
of TL, including trust, interaction frequency, knowledge base
525
construction, etc., are all related to the organization’s long-term 526
existence [59]. Thus, as Sydow et al. [60] argued that, despite 527
a definite end date, large infrastructure projects may endure 528
for far more time than many organizations, and their learning 529
process maybe not very different from those of permanent 530
organizations. 531
We observed four project delivery team meetings in the 532
Shanghai Pudong Airport Phase IV extension project from 533
December 30, 2019, until January 13, 2020. A notable exam- 534
ple is that in one meeting, the head of the baggage working 535
group proposed to continue to invite the external consulting 536
company of Phase III to provide baggage consulting services, 537
which is a typical kind of exploitation. While other functional 538
department heads claimed a big difference between Phase IV 539
and Phase III, and more consulting companies could be invited 540
to obtain different proposals. Then the best proposal could be 541
selected. After the discussions, the exploitative “keeping the 542
previous consulting company” and the exploratory “comparing 543
the proposals of various consulting companies” were integrated. 544
Similarly, in many cases, we observed that exploratory and 545
exploitative innovation were better integrated during the TL 546
process. 547
C. Moderating Role of TI 548
Interestingly, TI’s moderating effect is significant only be- 549
tween team heterogeneity and TL (H3b), while it was not 550
significant in other paths (H3a and H3c). A possible reason 551
that H3a and H3c are not significant may be that TI can 552
create an integrated ambidextrous organizational culture [61]. 553
It can moderate the impact of team heterogeneity on team 554
processes (TL) but cannot directly moderate team outcomes 555
(ambidextrous innovation strategies). They are consistent with 556
the finding from Mesmer-Magnus et al. [62] that strong TI does 557
not guarantee a positive team effect. Another possible reason 558
is that the one-off and somewhat persistent nature of large 559
infrastructure projects affects TI’s moderating effect. Project 560
organizing has a different goal setting with permanent organi- 561
zations [63]. Mesmer-Magnus et al. [62] believed that whether 562
the team is temporary or long-term will affect TI’s moderating 563
role. 564
H3b was rejected, possibly due to the highly complex nature 565
of large infrastructure projects. Porck et al. [64] believed that TI 566
was negatively correlated with team outcomes when team tasks 567
were highly complex. Teams with high task complexity will 568
lead to more depletion when performing TI, whereas depletion 569
is negatively correlated with team innovation. Porck et al.’s [64] 570
view contradicted many studies on TI but is consistent with our 571
results. 572
This interesting finding could be a starting point for future 573
research about project climate. It is generally recognized that 574
organizational climate could be maintained and stable as time 575
goes. This may not be true in large infrastructure projects where 576
different parties with diverse cultures work together toward 577
a particular task [65]. On the one hand, it is challenging to 578
IEEE Proof
outcome quickly. On the other hand, there are possibilities of580
conflicts between different organizational climates from project
581
parties.
582
VI. CONCLUSION
583
Since large infrastructure projects are a particular
“intermedi-584
ate” form between temporary projects and permanent
organiza-585
tions, the results of previous research on fostering ambidexterity
586
cannot merely be replicated. This article addresses the research
587
gap of the ambiguous relationship between team heterogeneity
588
and ambidextrous innovation strategies in large infrastructure
589
projects. The findings showed that team heterogeneity has a
590
positive linear effect on exploratory and ambidextrous
inno-591
vation strategies and an inverted U-shaped effect on
exploita-592
tive innovation strategies; team heterogeneity can better foster
593
ambidextrous innovation strategies by improving TL; high TI
594
weakens the positive relationship between team heterogeneity
595
and TL.
596
A. Theoretical Contributions
597
This article contributes to the ambidexterity and large
in-598
frastructure project management literature fourfold. First,
un-599
like permanent organizations, large infrastructure projects are
600
unique one-off endeavors [14], while unlike general temporary
601
projects, they have specific repetitive characteristics and are
602
somewhat persistent. Thus, they are considered to be the hybrid
603
of temporary projects and permanent organizations. In fostering
604
ambidextrous innovation, temporary projects are often seen as an
605
excellent context for exploratory innovation due to their unique
606
tasks [11], while permanent organizations benefit from the
ac-607
cumulated knowledge base and are often considered beneficial
608
to exploitative innovation [15]. In this respect, we believe that
609
exploring how to foster ambidextrous innovation strategies in
610
large infrastructure projects is not a simple expansion of a new
611
context but an essential expansion of the existing ambidexterity
612
theory in the particular “intermediate” form between temporary
613
projects and permanent organizations. Second, there was some
614
literature on the balance of efficiency and innovation in large
615
infrastructure projects, which can be regarded as ambidexterity.
616
However, they have focused on the critical role of ambidexterity
617
[16] and its positive impact on performance [17]. Our study
618
focuses on fostering ambidextrous innovation strategies in large
619
infrastructure projects, making a complementary contribution
620
to the large infrastructure project management literature [15].
621
Furthermore, we provide new insights into fostering
ambidex-622
trous innovation strategies in large infrastructure projects in
623
terms of team formation, process, and climate. Third, by
es-624
tablishing two parallel hypotheses and exploring the influence
625
of integrated process and climate, this article provides
impli-626
cations and empirical evidence on the ambiguous relationship
627
between team heterogeneity and ambidextrous innovation
strate-628
gies. Fourth, we also explore the impact of team
heterogene-629
ity on exploration and exploitation, respectively, and analyze
630
the reasons for the two different results, which simultaneously
631
provide inspirations for the discussion related to exploration and 632
exploitation. 633
B. Managerial Implications 634
Our findings have practical implications for large infras- 635
tructure project managers. First, the different impact of team 636
heterogeneity on exploratory, exploitative, and ambidextrous 637
innovation strategies provides meaningful guidance for project 638
management. On the one hand, when forming a project deliv- 639
ery team, it is important to focus not only on the individual 640
characteristics and traits of team members but also on the team 641
heterogeneity as a whole. On the other hand, large infrastructure 642
projects have different requirements and needs for exploratory 643
and exploitative innovation. The formation of the project de- 644
livery team should be different accordingly. For infrastructure 645
projects with high exploratory requirements (such as technically 646
challenging benchmark infrastructure projects) or high am- 647
bidexterity requirements (there is a tradeoff between exploration 648
and exploitation), it is best to form highly heterogeneous project 649
delivery teams. It is better to form project delivery teams that are 650
not very heterogeneous for ones with high exploitative require- 651
ments (much successful experience for replicating and learning). 652
Second, TL also plays a key role in large infrastructure projects 653
that are both persistent and one-off, through improving TL, 654
team heterogeneity can better foster ambidextrous innovation 655
strategies. Thus, in large infrastructure projects, to leverage the 656
interplay between exploratory and exploitative innovation strate- 657
gies and to effectively allocate and integrate resources, project 658
delivery teams should hold both regular and ad hoc activities to 659
promote TL. Third, due to the task complexity and one-off char- 660
acteristics of large infrastructure projects, too much emphasis 661
on TI may bring more organizational losses, which may oblit- 662
erate ambidextrous innovation strategies in large infrastructure 663
projects. 664
C. Limitations and Future Research 665
This article suggests new directions for project management 666
studies. First, the measurement of team heterogeneity in this 667
article is based on demographic characteristics and is rela- 668
tively simplistic. It would be interesting to study team networks 669
through the social network approach or measure the deeper 670
psychological and cognitive team heterogeneity. Second, since 671
we focus on large infrastructure projects under construction 672
in this research, objective measurement in such a context is 673
quite challenging, so the more subjective data were adopted. 674
More objective measurements could be adopted to evaluate ex- 675
ploratory and exploitative strategies [66]. Third, the results of the 676
moderating effect of TI are different from most organizational 677
management literature. We guess that it may attribute to the 678
temporary and complex characteristics of large infrastructure 679
projects, so it is recommended to conduct more case studies 680
or in-depth interviews to extend our future findings. Fourth, 681
ambidextrous innovation strategies can be explored in other 682
specific project contexts in the future, for example, smart city 683
IEEE Proof
APPENDIX
DEVELOPMENT OFAMBIDEXTROUSINNOVATIONSTRATEGIESSCALE IN THEINFRASTRUCTUREPROJECTCONTEXTS
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slack: A panel data analysis,” IEEE Trans. Eng. Manage., vol. 66, no. 4, 884
pp. 613–620, 2019. 885
[67] A. Ferraris, N. Erhardt, and S. Bresciani, “Ambidextrous work in smart 886
city project alliances: Unpacking the role of human resource management 887
systems,” Int. J. Hum. Resour. Manage., vol. 30, no. 4, pp. 680–701, 2019. 888
Xinyue Zhang is currently working toward the Ph.D. 889
degree with the School of Economics and Manage- 890
ment, Tongji University, Shanghai, China. 891
Her research has been published in peer-reviewed 892
journals such as the Journal of Construction En- 893 gineering and Management. Her research interests 894
include megaproject ambidexterity and megaproject 895
innovation. 896
IEEE Proof
Yun Le is currently a Professor and the Executive
898
Vice Dean of the Institute of Complex
Engineer-899
ing Management with the School of Management
900
and Economics, Tongji University, Shanghai, China.
901
His publications have appeared in the IEEE TRANS
-902
ACTIONS ONENGINEERINGMANAGEMENT,
Interna-903
tional Journal of Project Management, Journal of 904
Construction Engineering and Management, Journal 905
of Management in Engineering, etc. His research 906
interests include megaproject organization issues and
907
megaproject management.
Q4
908 909
Yan Liu is a researcher with Section Infrastructure
910
Design and Management, Faculty of Civil
Engineer-911
ing and Geosciences, Delft University of
Technol-912
ogy, Netherlands. His research interests include
learn-913
ing and knowledge management, inter-organizational
914
collaboration, and digital innovation in large
infras-915
tructure projects. He has published peer-reviewed
916
articles in journals such as International Journal of
917
Project Management, Engineering, Construction and 918
Architectural Management, etc. 919
920
Xiaoyan Chen is currently working toward the Ph.D. 921
degree with the School of Economics and Manage- 922
ment, Tongji University, Shanghai, China. 923
She has authored several papers in the Journal 924 of Cleaner Production, etc. Her research interests 925
include collaborative innovation in megaprojects and 926
organizational citizenship behavior in megaprojects. 927 928
