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SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS
Received March 12, 2019, accepted March 25, 2019, date of publication April 4, 2019, date of current version April 23, 2019.
Digital Object Identifier 10.1109/ACCESS.2019.2909400
The Role of Wearable Technologies in
Supply Chain Collaboration: A Case
of Pharmaceutical Industry
MUHAMMAD NOMAN SHAFIQUE 1, MUHAMMAD MAHBOOB KHURSHID 2,
HAJI RAHMAN 3, ASHISH KHANNA4, DEEPAK GUPTA 4,
AND JOEL J. P. C. RODRIGUES 5,6,7 , (Senior Member, IEEE)
1Dongbei University of Finance and Economics, Dalian 116023, China
2Department of Examinations, Virtual University of Pakistan, Lahore 54000, Pakistan
3University of Buner, Buner 17290, Pakistan
4Maharaja Agrasen Institute of Technology, GGSIP University, Delhi 110078, India
5National Institute of Telecommunications (Inatel), Santa Rita do Sapucaıì 37540-000, Brazil
6Instituto de Telecomunicações, 1049-001 Lisbon, Portugal
7Federal University of Piauí, Teresina 64049-550, Brazil
Corresponding authors: Muhammad Mahboob Khurshid (mehboob.khursheed@vu.edu.pk) and Joel J. P. C. Rodrigues (joeljr@ieee.org)
This work was supported in part by the National Funding from the FCT -Fundação para a Ciência e a Tecnologia through the Project
UID/EEA/50008/2019, in part by the RNP, with resources from MCTIC, through the Centro de Referência em Radiocomunições (CRR)
project of the Instituto Nacional de Telecomunicações (Inatel), Brazil, under Grant 01250.075413/2018-04, and in part by the Brazilian
National Council for Research and Development (CNPq) under Grant 309335/2017-5.
ABSTRACT The technological transformation has led to the introduction of mini computers as wearable
computers which facilitate users to use the Internet, different applications, messaging, and for calls all on
one platform. The focus of these wearable computers in earlier studies has been on consumers and technical
aspects, and their application has not been explored from an organizational perspective. This means that
the recent introduction of wearable technology has great potential, which is untapped in the organizational
context, especially for supply chain collaboration (SC). In this study, wearable technology is thought to
offer huge potential for organizations in SC. Therefore, the purpose of this study is to empirically examine
the role of wearable technology i.e. Smartwatch in enhancing collaboration among supply chain members
with mediating roles of green training (GT) and emotional intelligence (EI). The population of this study is
the pharmaceutical industry in Pakistan. Simple random sampling method has been used to collect data
from 150 sample size. Data have been collected through offline and online survey method. Data have
been analyzed through partial least square–structure equation modeling (PLS–SEM) technique. The results
of this study have empirically tested the established conceptual framework. Continuance intention to use
smartwatch has a positive effect on SC. Furthermore, GT and EI have mediating roles between continuance
intention to use smartwatch and SC. It is concluded that the continuance intention to use smartwatch will
increase SC. GT and EI will strengthen the relationship between continuance intention to use smartwatch
and SC. The use of wearable technology will motivate employees to communicate rapidly and to monitor
their supply chain activities using web-based and other applications. This study has practical implications in
organizations and theoretical contribution to literature.
INDEX TERMS Internet of things, emotional intelligence, green training, smart watch, supply chain
collaboration, theory of planned behavior, wearable technologies.
I. INTRODUCTION
The advancement in information technology has increased
the demand for wearable technologies very rapidly. In 2014,
The associate editor coordinating the review of this manuscript and
approving it for publication was Sherali Zeadally.
22 million units of wearable technologies were sold, which
increased to over 250 million in 2018 [1], [3]. In addition,
it has been estimated to reach wearable devices market to
be of worth $25 billion by 2019 [2]. The most common
wearable devices are wrist bands, smartwatches, wearable
cameras and smart eye-wears. In China, Xiaomi has sold
49014
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M. N. Shafique et al.: Role of Wearable Technologies in Supply Chain Collaboration
wearable wrist bands more than of any country in the world.
This sales trend predicts the increase of sale of wristwatches
included Samsung, Apple, and Pebble in the future more than
previous years. The wrist-worn wearable devices will be more
dominant in the coming years and most of the market share
will be captured by smartwatches [3], [4].
Currently, smartwatches can perform multiple operations
at one time. Smartwatches have different display and include
different resolution schemes. They can be connected to
mobile phones as well as Bluetooth devices to enable wear-
ers to respond to calls and messages. Furthermore, different
social and communication applications can be used in smart-
watches [5], [6]. Smartwatches can also track health related
activities to monitor work, count steps, sleep time, swimming
and other activities. They can track and store health record of
users. These smartwatch features have attracted the attention
of users to make their life more convenient [4], [7].
In previous literature, wearable technologies have been
focused on the perspective of sales, market growth [8], bat-
tery life, power absorption capability, wireless communi-
cation [9], [10], smartwatch interfaces and resolution [11],
diffusion of innovation (DOI) [12], and use of wearable tech-
nologies [3]. However, literature has ignored the importance
of wearable devices, especially smartwatches, to increase
organizational performance and especially supply chain col-
laboration. Therefore, this study has changed the trend of
usage of wearable devices from the user and technolog-
ical perspective to organizational perspective. This study
has focused on wearable technology to use smartwatch and
increase the supply chain collaboration, which is a more
practical issue, and to fill the literature gap.
II. LITERATURE REVIEW
A. THEORY OF PLANNED BEHAVIOR (TPB)
Theory of planned behavior is the extension of theory of
reasoned action (TRA). In information technology literature,
theory of planned behavior was considered to evaluate the
user’s behavior on adoption of technologies [13]. Theory
of planned behavior was proposed by Ajzen in 1991 [14].
In this study, theory of planned behavior has been employed
as a lens to establish the conceptual framework for study.
Three antecedents which are attitude, subjective norms and
perceived behavior are used to measure the user’s continu-
ance intention to use smartwatch. In previous literature, the-
ory of planned behavior has grounded the explanation about
individual adoption behavior in wearable technologies [15],
healthcare, technology, politics and different fields [15]–[17].
Wearable technology i.e. smartwatch is a new innovation
that can play a significant role in enhancing supply chain
collaboration and TPB model with three constructs of atti-
tude, subjective norms, and perceived behavior is expected
to give a more satisfactory explanation of adoption intention.
TPB model asserts that human behavior to use a technology
is a direct function of human behavioral intention. However,
behavioral intention is the function of attitude, subjective
norms as well as perceived behavior instead of attitude
and subjective norms only [14] used in theory of reason
action (TRA).
In previous literature, attitude has been defined as ‘‘the
effect of positive or negative feelings of individuals in per-
forming a particular behavior’’ [18]. Thus, attitude is the
one of major psychological antecedents of intention. In pre-
vious literature of theory of planned behavior in different
fields, attitude has been found to have a positive effect on
intention. In this study, consistent pattern has been followed,
as attitude has positive effect on continuance intention to use
smartwatch [18].
Subjective norms construct is the second major factor con-
sidered in theory of planned behavior to measure intention.
Subjective norms construct has been defined as ‘‘the individ-
ual’s perception of the likelihood that the potential referent
group or individuals approve or disapprove of performing the
given behavior’’ [18]. In previous literature, subjective norms
have positive effect to evaluate the user intention. In this
study, same pattern has been followed and positive relation-
ship between subjective norms and continuance intention to
use smartwatch has been considered [5].
Perceived behavior is described as ‘‘the perceived
ease or difficulty of performing the behavior, and it is
assumed to reflect experience as well as anticipated imped-
iments and obstacles’’ [14]. Perceived behavior is the third
factor which influences on user’s intention. In previous lit-
erature, perceived behavior has positive effect on the inten-
tion. In this study, the direct effect of perceived behavior
on continuance intention to use smartwatch has been
focused [18].
B. SMARTWATCHES
Smartwatches are mini computers because they do more
than only show the time as do traditional watches. Smart-
watches are wrist wearing technology, which is the advance-
ment in wrist wearing technology from smart wristbands
to smart watches. Smartwatches have reduced the usage
of smartphones because user can use their smartphones on
their wrist. So, it will increase the attention of consumers
towards smartwatches. In previous literature, smartwatches
have been considered a luxury product which can show email,
calls, messages, and other apps on the wrist, which is more
convenient [5], [19].
Smartwatches have numerous functions which will
facilitate users to do their work. For example, smart-
watches or wrist-worn devices like wristbands, smart
bracelets or smart watches can trace human pulse to measure
blood pressure and other health related information. Smart-
watches can also be used to collect, store and retrieve all
recorded health-related information on laptops and smart-
phones which can be utilized anytime and whenever it is
needed [5]. Furthermore, those applications can also be
installed in smartwatches which cannot be installed on wrist-
bands e.g. Fitbit Surge and Nike Fuelband. So, smartwatches
are better than other wrist-worn devices and have greater
functionality [15], [20].
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M. N. Shafique et al.: Role of Wearable Technologies in Supply Chain Collaboration
Smartwatches can be connected to the Internet through
Bluetooth and Wi-Fi devices. More than 10,000 apps can be
installed on iOS smartwatch, and 4000 apps can be installed
on Android smartwatch. Battery life and smartwatch design
is improving day by day to attract the attention of users.
Furthermore, smartwatches also offer large size and touch-
screen display. These functions are not available on smart
wristbands [5]. In this study, smartwatches have been used
for the benefit of organizations, to communicate in real time,
and present a more cost effective and convenient method of
operation, which will enhance supply chain collaboration.
C. GREEN TRAINING
Green human-resource practices are the emerging trends in
human resource management. Green human resource man-
agement practices have a positive effect on operational per-
formance and Green Supply Chain Performance (GSCP).
In this study, green training has focused as one of the
prominent practices of green human resource management.
Green training or environmental training can be defined as
‘‘on-job education and training of organizational employ-
ees to achieve organizational environment management
goals’’ [21]. So, the green training will create green aware-
ness and environmentally friendly thinking among the
employees through green education, which will be imple-
mented in the organizations [22], [23].
In green human resource management literature, green
training can develop the environmental understanding in all
level of employees (from the top, senior, middle to lower
level) within the organization. Furthermore, employees can
implement their green knowledge into their operational activ-
ities. Green training will enable organizations to integrate
their routine activities to green practices and play their role
to protect the environment. So, the ultimate goal of green
training is to achieve environmental performance [23]–[25].
In green training literature, it is highlighted that green
training has a positive impact on greening the organization
because GT enables all the employees to think and go green,
which will lead to improving the green practices in the
organization at all levels. Furthermore, green training also
develops green teams in the organizations to achieve an envi-
ronmentally friendly environment in the organizations [26].
In Spain, for example, green training motivated organizations
to adopt advanced green practices, protect the environment,
and contribute to environmental performance [25], [27].
D. EMOTIONAL INTELLIGENCE
Emotional intelligence is not a very old concept, having
emerged for the first time in 1920’s. Emotional intelligence
was divided into three concepts of abstract, mechanical and
social intelligence. These three concepts were studied for
the first time in emotional intelligence. In 1980’s, the con-
cept of emotional intelligence was first conceptualized by
Steiner [28]. Emotional intelligence was studied in dif-
ferent disciplines such as human resource, organizational
behaviors, and other disciplines [29]. Furthermore, emotional
intelligence has been categorized as inter-emotional and
intra-emotional intelligence in previous literature [30], [31].
Emotional intelligence is the part of social intelligence.
Emotional intelligence is the ability of individuals to under-
stand one’s emotions and feelings [29]. Emotional intelli-
gence enables individuals to differentiate and manage their
actions and reasoning in all fields of endeavor. In addition,
emotional intelligence is the individual’s capability to iden-
tify, access and produce emotions which will facilitate the
individual to make judgements, make decisions for different
actions [32]. So, the emotional intelligence plays the most
important role in understanding and regulating the individ-
ual’s feelings and emotions [30].
Emotional intelligence is the complete mental process
which starts from articulating and appraising of self-
emotions. In the next phase, it will enable individuals to
understand the verbal and nonverbal emotions and words of
others. The next step is to adopt the self and others’ emotions.
Individuals can then use the emotions of self and others for
specific actions and reasons. So, individuals can regulate the
emotions of others. This complete process will become the
overall process of emotional intelligence [30].
E. SUPPLY CHAIN COLLABORATION
Supply chain collaboration is the process of planning and
execution of activities which will enable supply chain part-
ners to respond according to dynamic changes in the mar-
ket [33], [34]. Supply chain collaboration is the complete
mechanism to integrate organizational internal and external
resources, activities, and team members to achieve specific
organizational goals which were not possible to achieve
alone [35], [36]. Supply chain collaboration is very benefi-
cial for organizations to develop the relationship with other
supply chain members [37], [38].
The primary purpose of supply chain collaboration is to
satisfy customer needs through the integration of operations
and team members with each other [39]. In addition, supply
chain collaboration develops close and strong relationship
between supply chain partners, enabling them to share infor-
mation with each other [40]. Therefore, supply chain collab-
oration is not an option for the organization, rather it is the
basic necessity to achieve organizational goals [41].
Supply chain collaboration is the important factor to
develop and sustain competitive advantage. This is because
supply chain collaboration will enhance the communication
among team members, which will reduce the errors in the
supply chain operations. So, the supply chain collaboration
will enhance productivity and increase the trust level between
supply chain partners to achieve competitive advantage and
organizational goals [36], [42]–[44].
In this study, the relationship between attitude, sub-
jective norms, perceived behavior, continuance intention
to use smartwatch (as theory of planned behavior) has
been considered to have a positive effect on supply chain
collaboration. In addition, green training and emotional intel-
ligence have been hypothesized to have mediating effects
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FIGURE 1. Conceptual framework.
FIGURE 2. Research design.
between the continuance intention to use smartwatch and
collaborative behavior. The overall conceptual model has
been drawn graphically in Figure 1 as schematic diagram.
III. METHODOLOGY
A. RESEARCH DESIGN
Research design is the pictorial representation of stages
through which a study is conducted. Figure 2 demonstrate
the design of this research. Stage 1, stage 2, and stage 3 in
the research design have been discussed in Section I and
Section II. In Section III, stage 4 is being elaborated in detail.
In Section IV, stage 5 has been elaborated.
B. SAMPLE AND DATA COLLECTION PROCEDURE
Pharmaceutical industry in Pakistan is the targeted population
of this study. The sample has been derived from population
through simple random sampling technique. The sampling
size is 150 employees from pharmaceutical companies taken
from Lahore (the capital of Punjab province) and Islamabad
(the capital of Pakistan), because more competitive environ-
ment has been observed in these big cities. Organizations in
these cities face greater competition to gain market share, thus
they invest in the training and development of their employees
TABLE 1. Profile of respondents (N=150).
and in the advancement of information communication tech-
nologies. Furthermore, the employees are more competent
and innovative. In these big cities, people are more aware and
use of wearable technologies are higher than other cities in
Pakistan. In addition, the success of pharmaceutical indus-
try is based on smooth flow of information and medicines
throughout the market. This is only possible if pharmaceutical
companies have strong supply chain collaborative system.
In previous literature of wearable technologies, health care
industry has been focused [45]. Thus, support for the phar-
maceutical industry as population of this study is consistent
with previous research [45].
In this study, survey method has been used to collect the
data from respondents. Both offline and online questionnaires
have been designed to conduct the survey. Questionnaire has
been divided into two parts; first part is related to demo-
graphic questions related to information about respondents
and the second part of questionnaire has inquired about
research phenomena. These research questions have been
adapted and measured on five-point Likert scale. Online
survey method is a more convenient and rapid data collec-
tion method. It will collect and store the survey responses
automatically. On the other hand, in offline questionnaire,
physical survey has been conducted during the period of
September to October 2018 from Lahore and Islamabad. The
results of demographics of respondents have been mentioned
in Table 1, which provide the information about respondents.
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C. WHY USE PLS-SEM
The advancement in information communication technol-
ogy has facilitated the researchers to conduct their studies
such that these technologies facilitate research process and
enhance research quality. In this study, the online question-
naire has been developed through the use of Lime survey,
which is a web-based application. The use of this application
will enable researchers to develop online survey. It will create
the hyperlink of survey to send through email or through
any other social media. Respondents can open the survey,
complete the questionnaire and submit it on line. Lime sur-
vey will collect and store the responses automatically. These
responses can be downloaded in comma separated values
(CSV) format. This format can be imported directly into
Smart PLS for further data analysis [46].
Partial least square structure equation modeling
(PLS – SEM) is the advanced statistical methodology to
analyze the data [46]. There are two main reasons to use
PLS – SEM. In this study, analysis has been done on
5000 bootstrap subsamples method to avoid sample size
issue [47]–[49]. The first reason is that it can work more
effectively on small sample data size. This is because this
method did not consider the normality assumptions for
analysis, which was considered in traditional software like
AMOS [50]. It gives more accurate results in small sample
data size [51]. Second, this method is more accurate in the
exploratory stage of any phenomena because PLS – SEM
can elaborate the phenomena more precisely at exploratory
stage [52]–[54]. In this study, the relationship between inten-
tion to use smartwatch, emotional intelligence, green training
and supply chain collaboration is at exploratory stage which
was not developed in previous literature. So, PLS – SEM will
statistically test this conceptually developed relationship at
exploratory stage.
The research report has been written and formatted through
the use of Microsoft Word 2016, and Microsoft Excel 2016
has facilitated to present the analysis tables in order to
reduce the unnecessary information from output tables.
Endnote X9 has been used for citation and references to
avoid any human error from citation and references portion.
This is because it is very difficult to mention all citations
and references without the use of citation software. After
the completion of paper, English grammar has been checked
through the use of Grammarly premium account to remove
major mistakes from the report. Furthermore, English will be
double checked by human if necessary.
D. RESEARCH INSTRUMENT
In this study, all the questions have been adapted and mea-
sured on five-point Likert scale. The validity of items was
measured through construct validity, content validity, and
convergent validity which were tested through factor loading.
Items having factor loading less than 0.6 have been deleted
and not considered for further analysis.
Attitude (AT): Attitude was measured through six ques-
tions and adapted from literature [55], [56].
Subjective Norms (SN): Subjective Norms was measured
through four questions and adapted from literature [55], [56].
Perceived Behavior (PB): Perceived behavior was
measured through three questions and adapted from
literature [55], [56].
Continuance Intention to use smartwatch (INT): Intention
to use smartwatch was measured through four questions and
adapted from literature [57].
Emotional Intelligence (EI): Emotional intelligence was
measured through sixteen questions and adapted from liter-
ature [58].
Green Training (GT): Green training was measured
through ten questions and adapted from literature [22], [26].
Supply Chain Collaboration (SC): Supply chain collabora-
tion was measured through four questions and adapted from
literature [36].
IV. FINDINGS
A. COMMON METHOD VARIANCE
Common method variance is also called common method
bias. The common method bias is different from instrument
variance method. This is because it is related to methodology
instead of instrument and refers to the chance that this issue
will arise when data of independent and dependent variables
has been collected at the same time from same respondents
through the use of single questionnaire [59]. The chances
of common method bias can be reduced if the questionnaire
has been divided into different pages, which will separate the
independent variable from dependent variable [59]. In this
study, cross sectional data collection method has been used.
So, the data regarding independent and dependent variables
has been collected from the same respondents at one time,
which is why the chances of common method variance are
high in this study.
Common method variance can be analyzed statistically.
There are three common techniques to analyze common
method variance, which are Harman Method, Lindell & Whit-
ney Method and Bagozzi approach. In this study, Bagozzi
approach has been used to analyze the common method bias
because it is a very common and easy method to use. Bagozzi
method suggests that if the correlation among variables is less
than 0.9, there is no common method bias issue [60]. So, data
can be used for further analysis. In this study, researcher has
used Bagozzi method to measure the common method bias.
The correlation between variables can be checked through
correlational matrix, which was mentioned in Table 2. The
correlation value of each variable is less than 0.9, which
showed there is no common method bias issue in the data.
B. MEASUREMENT MODEL
Structural equation modeling method consists of two models.
The first is measurement model, which measures the relia-
bility and validity of data [61]. Reliability is also measured
through Cronbach’s Alpha and composite reliability. Validity
can be measured through content, convergent and discrimi-
nant validity [52], [62].
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TABLE 2. Fornell-Larcker Criterion Method for Discriminant Validity.
FIGURE 3. Cronbach’s Alpha values.
1) CRONBACH’S ALPHA
Reliability is the internal consistency of items. In this study,
reliability was measured through Cronbach’s Alpha test.
The minimum acceptable value for Cronbach’s Alpha value
is 0.6 [52], [63], [64]. In this study, the Cronbach’s Alpha
values of reliability of attitude, subjective norms, perceived
behavior, intention to use smartwatch, green training, emo-
tional intelligence, and supply chain collaboration is more
than 0.6, which is greater than minimum acceptable values.
So, the data is reliable for further analysis. Reliability val-
ues have been mentioned in Table 3, while Figure 3 shows
the reliability of each variable, which shows the minimum
acceptable value and the graphical Cronbach’s Alpha values
of each item.
2) COMPOSITE RELIABILITY
Composite reliability (CR) is the overall reliability of all
items of specific variable [54]. Composite reliability can be
measured through PLS – SEM. In PLS – SEM, composite
reliability was measured when the items are reflective. On the
other hand, if items are measured through formative items,
the variance inflation factor (VIF) test was used [65], [66].
In this study, attitude, subjective norms, perceived behavior,
intention to use smartwatch, green training, emotional intel-
ligence, and supply chain collaboration have been measured
through reflective items. So, composite reliability test has
been used. The minimum acceptable value for composite
reliability is 0.6 [51], [66]. In this study, composite reliability
of all variables is shown in Table 3, and graphical illustration
shown in Figure 4, which show the minimum acceptable
TABLE 3. Factor Loadings, t – statistics, Reliability, and Average Variance
Extract.
value of composite reliability and individual composite reli-
ability of each variable.
3) CONTENT VALIDITY
Content validity is related to the construction of items. The
content validity shows the logical flow and grammatical cor-
rectness of each item. If all items have logical flow and they
are grammatically correct, the reader and respondents can
understand the items completely. Content validity enables
respondents to respond to the items well. Content validity
can be measured through factor loading of each item. The
minimum acceptable item loading is 0.6 of all items to have
content validity [51], [52], [66]. In this study, factor loading
was measured through smart PLS and the results are shown
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M. N. Shafique et al.: Role of Wearable Technologies in Supply Chain Collaboration
FIGURE 4. Composite reliability values.
in Table 3. The results showed that all items have factor
loadings greater than 0.6 which showed all items have content
validity.
4) CONVERGENT VALIDITY
Convergent validity shows the theoretical relationship
between variables. The convergent validity showed how
much variables are interlinked with each other, how much
they are correlated with each other. Because if variables have
no correlation with each other then they cannot integrate in to
single framework of the study. In this study, attitude, subjec-
tive norms, perceived behavior, intention to use smartwatch,
green training, emotional intelligence, and supply chain col-
laboration has considered and their convergent validity has
tested through factor loading. The minimum acceptable item
loading is 0.6 of all items to have convergent validity [51],
[52], [66]. In this study, factor loading has measured through
smart PLS and the results have mentioned in Table 3. The
results showed that all items have factor loadings more
than 0.6 which showed all items have convergent validity.
5) DISCRIMINANT VALIDITY
Discriminant validity is the opposite of convergent validity.
It shows the degree to which variables are different from each
other. If the variables are the same as other variables, there is
no need to consider them as a separate variable. So, discrim-
inant validity showed whether the variables are completely
the same or if they have some difference from each other.
Discriminant validity can be measured through two ways.
First, it can be measured through the square root of average
variance extracted (AVE) and this value is then compared
with correlation among the variables [47], [51], [62]. If the
values are higher than the correlation, there is no discriminant
validity issue. Moreover, PLS – SEM can test the discrim-
inant validity through Fornell-Larker criterion method. The
Fornell-Larker criterion method also measures the correlation
and square root of AVE values [51]. In this study, the discrim-
inant validity was measured through Fornell-Larker criterion
method as shown in Table 2. In addition, if the items have
AVE values greater than 0.5, they are discriminant valid.
Second, factor loading can also be used to measure the dis-
criminant validity. If the items have factor loadings greater
than 0.6, they have discriminant validity [67].
The overall results of factor loadings, t-statistics, Cron-
bach’s Alpha, composite reliability and average variance
TABLE 4. Collinearity statistics.
extracted values can be used for the validation of measure-
ment model in PLS – SEM. In this study, the results of all
values are in favor to accept the measurement model as shown
in Table 3.
6) COLLINEARITY
Collinearity issue occurs when two constructs of formative
nature are correlated with each other. However, the issue
becomes more critical if more than two formative indica-
tors are correlating with each other named as multicollinear-
ity [52], [62]. It is indicated as Variance Inflation Factor (VIF)
in SmartPLS 3.0 software. The maximum threshold value
of VIF is 5. Therefore, VIF value of each indicator should
be below 5. Since VIF value of each indicator is below 5,
there is no collinearity issue found. Although collinearity
statistics need not to be evaluated in reflective measurement
models [52], [62], yet VIF values of all indicators for assur-
ance of results are shown in Table 4.
C. STRUCTURAL MODEL
Structural model is the second model in structural equation
modeling technique. In structural model, explanatory factors
(R2) and significant values (p values) are the most crucial val-
ues to assess the structural model [47], [48], [62]. In advanced
PLS – SEM software, the values of all possible direct and
indirect paths of the conceptual model can be statistically
analyzed, which was not possible in traditional techniques.
In smart PLS, small sample size data can be assessed. This is
because this software does not require the normality assump-
tions [50]. Instead, it works on bootstrapping subsample tech-
nique which is more advanced and accurate. The minimum
recommended bootstrapping subsample size is 500 [49]. But
in this study, bootstrapping subsample size is taken as 5000 to
get more accurate results. All the possible direct and indirect
paths of conceptual model have been statistically tested and
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TABLE 5. Path Coefficients, t – statistics, and significant values.
the results of their explanatory factors (R2), t – statistics and
their significant values are shown in Table 5.
There is no hard and fast rule for explanatory fac-
tors (R2) values which show the minimum acceptable
value [72], [68]. However, in previous literature, the val-
ues of R20.02, 0.13 and 0.26 are categorized as low,
medium and high explanatory variance [69]. The objective of
structural model is only to explain the variance between
independent and dependent variables. The path coefficient
values of attitude to continuance intention to use smartwatch,
continuance intention to use smartwatch to emotional intel-
ligence, continuance intention to use smartwatch to green
training, continuance intention to use smartwatch to sup-
ply chain collaboration, emotional intelligence to supply
chain collaboration, green training to supply chain collab-
oration, perceived behavior to continuance intention to use
VOLUME 7, 2019 49021
M. N. Shafique et al.: Role of Wearable Technologies in Supply Chain Collaboration
FIGURE 5. Path coefficients.
FIGURE 6. Path coefficients for supply chain collaboration.
TABLE 6. Model fit indexes.
smartwatch, and subjective norms to continuance intention
to use smartwatch are shown in graphical form in Figure 5.
These are all possible direct paths and show the path coef-
ficients (R2) values of independent to dependent variables.
All values are positive which showed that if the values of
independent variable were to increase or decrease, the value
of dependent variable will also increase or decrease in the
same manner. However, the magnitude of change in value will
be different in each relationship.
The overall structural model of PLS – SEM model has
been imported from software and shown in Figure 6. The
outer model is consisting of factor loadings of each item
and the inner model shows the value of R2as mentioned
on paths and inside the circles of each dependent variable.
As shown, the statistical values have empirically supported
the conceptual model.
The overall model fitness can be analyzed through model
fit index in smart PLS. The overall model fit index shows
the model fitness of whole model. In PLS – SEM, the over-
all model fitness can be analyzed through two values.
First, Standardized Root Mean Square Residual (SRMR),
in which the minimum recommended acceptable value is less
than 0.08 [70]–[72]. The second model fit index is Normed
Fit Index (NFI). The NFI is related to chi square index,
where higher values are more preferred. The minimum
recommended value for NFI is 0.9 [73], [74]. In this study,
the values of overall model fit have been analyzed through
saturated and estimated models. The values of both saturated
and estimated model values are closer to the recommended
values. So, both SRMR and NFI values are in favor of statisti-
cally tested model, which is why the whole conceptual model
has been accepted through empirical results. The values of
both saturated and estimated models are shown in Table 6.
V. CONCLUSION AND DISCUSSION
The usage of smartwatches is becoming very common. This
usage is opening new opportunities for IT professionals,
consumers, and organizations. Smartwatches are handheld
wearable technology. Smartwatches are different from mobile
and tablet devices because they have scanning and sensory
features that differ from other devices [10]. Furthermore,
smartwatches can be connected to mobile phones to attend
calls and to use different applications on them. These wear-
able devices can also be used to count steps, track sleep, make
enjoyment, provide ease in communication and many other
features that encourage consumers to continue using them.
In this study, theory of planned behavior has been used to
understand the continuance intention to use smartwatch. This
study has developed the relationship between intention, green
training and emotional intelligence to achieve supply chain
collaboration. This relationship has been empirically tested
through advanced statistical technique PLS – SEM. The
path coefficient results are significant, which are in favor
to accept the conceptual framework. So, this study showed
that the continuance intention to use smartwatch, green train-
ing and emotional intelligence will enhance supply chain
collaboration.
Theory of planned behavior has been used to measure
the continuance intention to use smartwatch through three
constructs; attitude, subjective norms, and perceived behav-
ior. These three constructs have positive effect on contin-
uance intention to use smartwatch. Results of this study
are consistent with previous studies on theory of planned
behavior [15], [18]. The results have proven that the atti-
tude of consumer, subjective norms of society and perceived
behavior of consumer will encourage the consumers to use
wearable technology and motivate them to use these wearable
technologies continuously.
In this study, emotional intelligence and green training has
mediating the relationship between continuance intention to
use smartwatch and supply chain collaboration. This medi-
ating effect has measured through PLS – SEM using mea-
surement and structural models. The results showed that high
intention to use smartwatch will increase the supply chain
collaboration. Furthermore, the indirect mediating paths of
intention to use smartwatch through emotional intelligence
and green training has the significant effect on supply chain
collaboration. The results showed the partial mediation of
both mediators of emotional intelligence and green training
between continuance intention to use smartwatch and supply
chain collaboration.
49022 VOLUME 7, 2019
M. N. Shafique et al.: Role of Wearable Technologies in Supply Chain Collaboration
In this study, a multidisciplinary framework has been intro-
duced and empirically tested which was not found in liter-
ature. The wearable technologies have focused in computer
science and information communication technologies studies
and many consumer behavior related studies [4], [45]. How-
ever, the organizational perspective and utilization of wear-
able technology, especially smartwatch, has been ignored in
supply chain studies. In this study, the relationship between
wearable technology (IT perspective), green training (human
resource management), emotional intelligence (psychology)
and supply chain collaboration (supply chain management)
has been developed and empirically tested, which give a big
picture of the utilization of wearable technologies in supply
chain. So, this study will open new horizons in literature and
open new practical implications of wearable technologies in
organizations.
The advancement in wearable technologies, especially
smartwatch, has introduced new features to attract consumer
attention. So, consumers get emotionally involved in it, which
is why organizations must understand this reality and move
towards the wearable technologies to communicate with their
team members. In addition, organizations must hold training
sessions about the use of new wearable technologies espe-
cially smartwatches. Organizations must integrate their com-
munication activities through smartwatches, which is more
convenient and can track and record the health and usage
information of their team members.
In organizations, people are moving rapidly towards the
use of smartwatches. In addition, supply chain team members
are very engaged, and they are reducing the usage of their
mobile phones and increasing the usage of wearable tech-
nologies. The frequency of using smartwatch is beginning to
increase. Users are also recommending that others should use
smartwatches. Furthermore, the employees would be tracked
in the advancement of technology, and when the new series
or model of smartwatch are introduced, they will prefer to
buy them. This is because they think new version has more
advanced features which will make it easier for them to use.
In summary, this study has established a multidisciplinary
relationship to use of wearable technologies and their practi-
cal implication for organizational benefits. Theory of planned
behavior has helped to establish this relationship. The
PLS – SEM results showed that continuance intention to use
smartwatch has positive effect on supply chain collabora-
tion. Furthermore, green training and emotional intelligence
played the mediating effect between continuance intention to
use smartwatch and supply chain collaboration. The overall
model fit index showed that the conceptual model is fit.
In addition, the advanced PLS – SEM technique based on
both measurement model and structural model empirically
supported the conceptual model.
VI. LIMITATIONS AND FUTURE DIRECTIONS
In this study only, smartwatch has been considered as wear-
able technology, while the other wearable technologies like
Bluetooth hand free, smart eye wear, wearable camera,
wearable wrist bands and many other wearable technologies
has been ignored. In future studies, other wearable technolo-
gies can be considered. In this study, the survey method
has been used to collect the data. It is recommended for
future studies to consider interview method for data collection
because people have innovative ideas to improve wearable
technologies. Their feedback is very important and can be
recommended to organization which will facilitate to design
and develop customized features for the new models in wear-
able technologies. This study has been limited to intention
to use smartwatch, emotional intelligence, green training,
and supply chain collaboration while many other factors
like regulations, peer pressure, market demands, and many
other important factors have been ignored in this study. So,
it is highly recommended to consider these factors in future
studies. This study has focused on Pakistani population but
in future other developing counties like India, Sri Lanka,
Bangladesh and Nepal can be focused. The implication of this
study in other countries will generalize this study.
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MUHAMMAD NOMAN SHAFIQUE received
the M.Phil. degree in business administration from
Pakistan. He is currently pursuing the Ph.D. degree
in business administration with the Dongbei Uni-
versity of Finance and Economics, China. He is
learning new research techniques and implement-
ing his studies. He has completed many research
certifications and has published few research
papers in different journals.
MUHAMMAD MAHBOOB KHURSHID rec-
eived the M.Sc. degree in project management
from the COMSATS Institute of Information Tech-
nology, Islamabad. He is currently pursuing the
Ph.D. degree in information systems with the Uni-
versiti Teknologi Malaysia. He is currently serv-
ing as an Assistant Controller of Examinations
with the Department of Examinations, Virtual Uni-
versity of Pakistan (VUP). He has ten years of
professional experience carrying out operations
regarding examinations in VUP.
HAJI RAHMAN a man of ambitions to take the
challenges in his field. He remained a hardwork-
ing human with an excellent academic career. He
received the M.S. degree in engineering manage-
ment from UET, Taxila, Pakistan, and the Ph.D.
degree in management sciences (HRM) from Pre-
ston University, Islamabad, on HEC scholarships.
He was a faculty member for more than nine years
at the Ghulam Ishaq Khan Institute with best result
producing teacher awards. He has remained as an
Assistant Professor with Preston University, Islamabad campus for two
years. He is also a Visiting Faculty Member at the IIUI, Islamabad, and
Federal Urdu University of Arts Sciences and Technology, Islamabad. He
also delivered training modules at CDA Training Academy and Pakistan
Manpower Institute, Islamabad. He is currently an Assistant Professor with
the Department of Management Science, University of Buner. He also holds
the additional charge as a Director Quality Enhancement Cell (QEC) at
University of Buner. He is the author of a number of international and
national research papers. He has also presented his research articles in a
number of international research conferences. He received the Punctuality
and Efficiency Awards for Fall Semester 2015, Spring Semester 2016, Fall
2016, and Spring 2017 from Preston University.
ASHISH KHANNA is a highly qualified
individual with around 15 years of rich exper-
tise in Teaching, Entrepreneurship, and Research
& Development with specialization in Computer
Science Engineering Subjects. He received the
B.Tech. and M.Tech. degrees from GGSIP Uni-
versity, Delhi, in 2004 and 2009, respectively, and
the Ph.D. degree from the National Institute of
Technology, Kurukshetra. He has been a part of
various seminars, paper presentations, research
paper reviews, and conferences, as a convener and a session chair, and a guest
editor in journals, and has coauthored several books in publication house and
papers in national journals, international journals, and conferences. He has
published many research papers in reputed journals and conferences. He
also has papers in SCI-indexed and Springer journals. He has coauthored
10 Text books and edited books i.e. Distributed Systems,Java Programming
and Website Development,Java Programming,Computer Graphics,Com-
puter Graphics and Multimedia,Computer Networks,Computer Networks
and Data Communication Networks,Success Mantra for IT interviews,
and Fundamental of Computing. He has also an edited book in Lambert
publication. He recently successfully managed Smart India Hackathon
in 2017 at MAIT, GGSIP University with teams under him winning prices at
their distributed systems, cloud computing, vehicular ad hoc networks, and
opportunistic networks. He displayed vast success in continuously acquiring
new knowledge and applying innovative pedagogies and has always aimed
to be an effective educator and have a global outlook which is the need of
today. He is currently associated with some Springer and IEEE conferences
and managing special sessions for them and looking forward for some more
challenging tasks. He was a reviewer in some SCI indexed Journals, like
Cluster Computing (Springer) and IEEE conferences. He is also a Reviewer
and a Session Chair of IEEE international conference ICCCA 2016 and 2017.
He has designed the syllabus for cloud computing, Java Programming, and
distributed systems for GGSIP University. He was a Guest Editor in IEEE
conference-IC3TSN-2017 and managing a special session on Parallel and
Distributed Network-based Computing Systems. He was a Guest Editor in
Springer Conference at ICDMAI-2018 and managing a special session on
Computational Intelligence for Data Science.
DEEPAK GUPTA received the M.E. degree from
the Delhi College of Engineering, in 2010and the
Ph.D. degree from Dr APJ Abdul Kalam Technical
University (AKTU), in 2017. He is currently an
Assistant Professor with the Department of Com-
puter Science and Engineering, Maharaja Agrasen
Institute of Technology, GGSIP University, Delhi,
India. He has published many scientific papers
in SCI journals, like JoCS, IAJIT, and FGCS.
In addition, he has authored/edited over 25 books
(Elsevier, Springer, IOS Press, and Katson) and six journal special issues.
His research area includes software usability, human-computer interaction,
nature-inspired computing, machine learning, and soft computing. He is a
Convener and an Organizer of ICICC-2018 springer conference. He has also
started a research unit under the banner of Universal Innovator. He is also
associated with various professional bodies, like ISTE, IAENG, IACSIT,
SCIEI, ICSES, UACEE, the Internet Society, SMEI, IAOP, and IAOIP.
VOLUME 7, 2019 49025
M. N. Shafique et al.: Role of Wearable Technologies in Supply Chain Collaboration
JOEL J. P. C. RODRIGUES (S’01–M’06–SM’06)
received the five-year B.Sc. degree (licentiate)
in informatics engineering from the University
of Coimbra, Portugal, the M.Sc. degree and the
Ph.D. degree in informatics engineering from UBI,
the Habilitation degree in computer science and
engineering from the University of Haute Alsace,
France, and the Academic Title of Aggregated Pro-
fessor in informatics engineering from UBI. He is
currently a Professor with the National Institute of
Telecommunications (Inatel), Brazil, a Senior Researcher with the Instituto
de Telecomunicações, Portugal, and a Visiting Professor with the federal
University of Piauí, Brazil. He is also the Leader of the Internet of Things
Research Group (CNPq), the Director for Conference Development-IEEE
ComSoc Board of Governors, the IEEE Distinguished Lecturer, a Technical
Activities Committee Chair of the IEEE ComSoc Latin America Region
Board, and the President of the scientific council at ParkUrbis-Covilhã Sci-
ence and Technology Park. He has authored or coauthored over 700 papers
in refereed international journals and conferences and three books. He holds
two patents. He is a member of many international TPCs and participated
in several international conferences organization. He was a recipient of
several Outstanding Leadership and Outstanding Service Awards from the
IEEE Communications Society and several best papers awards. He was
the Chair of the IEEE ComSoc Technical Committee on eHealth and the
IEEE ComSoc Technical Committee on Communications Software, a Steer-
ing Committee Member of the IEEE Life Sciences Technical Community
and a Publications Co-Chair, and a Member Representative of the IEEE
Communications Society on the IEEE Biometrics Council. He is the Editor-
In-Chief of the International Journal on E-Health and Medical Communi-
cations and an Editorial Board Member of several high-reputed journals.
He has been a General Chair and a TPC Chair of many international
conferences, including IEEE ICC, IEEE GLOBECOM, IEEE HEALTH-
COM, and IEEE LatinCom. He is a licensed Professional Engineer (as a
Senior Member), a member of the Internet Society, and a Senior Member
of ACM.
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