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Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

The Effect of Perceived Self-Efficacy and Product Comparisons on Outcome Expectancy of Portable Multi-Screens

Shahid Kalim Khan, Li Guoxin, Ehsan Chitsaz School of Management

Harbin Institute of Technology Harbin, China

[email protected]

Naqash Ali, Ume Ammara Department of Management Sciences

CIIT, Pakistan [email protected]

Abstract—In the new era of multi-screens, people no more deal with the single device rather they use various devices in sequence or simultaneously to extend device possibilities within a broader context. Multi-screen trends have given rise to several research opportunities in various academic disciplines. Recent fast adoption of digital devices has resulted in media convergence because consumers use many screens to perform the same task or activity. The main reason for convergence is, coming together of functional benefits of various screens. Functional benefits of a device are perceived as outcome expectancies. However, how users develop this perception of outcome expectancy is relatively less studied in related research. This study investigates some important factors which can influence outcome expectancies of portable multi-screens (i.e. laptop, mobile and tablet/IPAD). Partial Least Squares (PLS) structural equation modeling is implemented to assess the hypotheses through the empirical sample of 287 University students in China. The results indicate that, self-efficacy and product/brand comparisons have significant positive impact on outcome expectancies. Also gender moderates the relationship between self-efficacy and outcome expectancy. In the end, theoretical contributions, practical implications of this research and future directions are discussed.

Keywords—multiscreens; multi-screen; brand preference; outcome expectancy; product comparisons; brand comparisons; frequency of use; use of mobile technology; smart devices; Chinese consumer’s; youth consumer

I. INTRODUCTION Multi-Screening is defined as the use of second screen or more than one screen for related work sequentially or for related and unrelated work simultaneously [1]. Before emergence of the multi-screen trend in recent times, user-device relationship has been understood as how users interact with a single device and valuable insights related to consumer’s; behavior, attitude and motivation, have been provided by the research in this area mainly dealing with single device interaction [2]. These days people no more deal with the single device rather they use various devices in sequence or conjunction to reap maximum benefits out device possibilities within a broader context. Several research opportunities have emerged due to the multi-screen phenomenon in a range of academic disciplines related to user-device relationship.

Media has become diversified with technology innovations over time. Different kinds of devices and content are available to users to satisfy their various needs [2]. These combinations of multiple devices have resulted in smarter and elevated user experience [3]. One of the reasons of the widespread adoption of multi-screens is their portability. Mobile technology has readily ingrained in today’s societies because of its flexibility and anytime/anywhere usage [4]. The portable multi-screen devices have come into sight during the past decade and have emerged as one of the most dominant consumer electronic products [5]. As a result, knowing the factors which can affect the preference or purchase decisions of novel mobile technology and devices have become the fundamental objective for the vendors and distributors of mobile devices.

Multi-screen usage affects consumer purchase path as well. Moreover, the purchase funnel has changed recently due to the emergence of multiple screens. Multi-screen users are different from others as they are mostly, career oriented, well educated, brand loyal and brand advocates [6]. It implies that their brand preferences have an impact on other users as well who are not very regular users of multi-screens. Exploring consumer choice or purchase behavior has been a key interest for

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marketers and researchers over period of time [7-9]. Consumer purchase behavior is a decision process through which consumers can make a choice or preference of specific product and brand [10]. They can also examine their actions and reason for their purchase decision through this decision process. They perform a cost benefit analysis when deciding on a course of action and consider what they would gain or lose from performing the specific behavior [11]. Such evaluation of expected outcomes is the focal point of the expectancy-value theory [12].Expectancy theory mainly explains the consumer mental process regarding making a choice. The decision process that we are talking about is central to this theory. It suggests that people decide to behave or act in a particular way because they hold a motivation behind it. They prefer a specific behavior over others because of some perceived or expected outcomes. Outcome expectancy is representation of an individual's belief that task accomplishment will result in desired outcomes. It is defined as the effect of an act and not the act itself [13]. Outcome expectancy has vital role in consumer preference and choice of a specific brand. However, it has appeared as independent or mediating variables in most of IS and consumer behavior research. Understanding of factors influencing outcome expectancy is not very clear. We realized a serious need to test the outcome expectancy as a dependent variable in order to evaluate the direct impact of other factors on this particular construct.

Device mobility and power of internet have made multi-screen devices strongly integrate with each other and users are no longer restricted to use a single device. They are rather able to choose the right type and appropriate combination of devices that suits their current usage needs [2]. Mobile devices such as laptops, smartphones, tablet computers and PDAs are very much integrated these days along with their various software applications and operating systems [3]. Laptop, mobile and tablet are more common and frequent form of portable screens. Recent fast adoption of digital devices has resulted in convergence because consumers use more than one screen to perform the same task or activity [6]. Various individual activities such as playing games, working on office documents, emailing, watching the news, social networking and many more, are no more dependent on one screen. The main reason for convergence is coming together of functional benefits of various screens [6]. Hence performance or outcome expectancy of multi-screens is a common factor, which is the focus of current study.

II. RESEARCH FRAMEWORK

Figure.2 shows our proposed research model with independent, dependent and moderating variables. An explanation of this framework has been made in this chapter.

A. Self-Efficacy and Outcome ExpectancySelf-Efficacy is very important construct which has been used to elaborate several behavioral aspects in variety of

contexts including consumer’s response to information technologies [14-17]. It can be defined as individual’s belief in their skills and capabilities to perform a specific task [16]. Although, outcome expectancy and self-efficacy affect behavior separately, self-efficacy also directly influences outcome expectancy [18]. In information system research, we can find several studies focusing on the impact of computer self-efficacy and outcome expectancy on different other variables such as performance, use and system success [19-20]. However, the relationship between self-efficacy and outcome expectancy is understudied and unclear.

People rely on their knowledge in order to incorporate predictive factors, construct options, revise and test judgments against the immediate and distal outcomes of their actions. Strong sense of efficacy is needed to remain outcome oriented in face of critical situation demands. Self-efficacy is an imperative motivational variable that have an effect on motivation and outcome expectation. Individuals who are more capable and expert of handling a task or situation will be able to apprehend the situation better and predict the outcomes better than those who are less capable. Expert users will perceive the system to more useful and easier. Consumer’s perceived ability to use a product potentially determines their evaluative and behavioral response to the product [21]. Therefore, self-efficacy is likely to influence attitudes and outcome expectancies and this relationship has been hypothesized as H1.

H1: Self-Efficacy related to the use of portable multi-screens has a positive and significant impact on outcome expectancy of consumer’s preferred brand of portable multi-screens.

B. Product/Brand Comparisons and Outcome ExpectancyPreceding purchase decision, consumers usually look for product information, make comparisons and seek for

recommendation so that a quality decision is made [22]. As per classical economic theory, consumer can evaluate the utilities or outcomes of products or brands based on their characteristics and these evaluations direct the purchase decisions [23]. It is hard for consumers to analyze an option if they consider it in isolation. It is assumed that consumer can asses every option



relative to other available alternatives which they have experienced or explored in the past and also they can evaluate it with respect to any other relevant information that they have acquired previously [23].

Prior to making a purchase decision, consumers usually compare several different brands. They often do comparisons to make preference judgments [24]. Although some preference judgments are developed by comparing the universal evaluations of each alternative, consumer preference decision process mostly involve, first making feature based comparisons among the available options. Evidently, effect of comparisons on preference and choice satisfaction comply with the argument that involving into similarity and dissimilarity comparisons changes the relative weight assigned to common and unique features of the alternatives under evaluation [23]. Comparisons directly affect the preference judgments [25]. Unique features of an alternative which are subject to comparison are more salient and also play a greater role in preference construction [26, 27,28,29]. Concluding from these arguments, it can be assumed that product comparisons influence outcome expectancies. Hence, we can develop a hypothesis as;

H2: Product/Brand comparisons have a positive and significant impact on outcome expectancy of consumer’s preferred brand of portable multi-screens.

C. Moderating Effect

1) GenderIn this study, sex is considered boundary condition that moderates the effect between user’s outcome expectancy

beliefs about multi-screens and their perceived self-efficacy. Gender is a critical personal characteristic and the impact of gender on consumer behavior has been studied before in various researches [30]. Women, more than men, focus on the magnitude of the effort and process to achieve their objectives. Moreover men compared to women, exert more effort to overcome different constraints and they generally have higher self-efficacy than women [31-33].

H3a: Gender will moderate the effect of product/brand comparisons on outcome expectancy of user’s preferred brand of portable multi-screen.

H3b: Gender will moderate the effect of perceived self-efficacy on outcome expectancy of user’s preferred brand of portable multi-screens.

2) Frequency of UseThe frequent and experienced user will see more potential uses of the tool as they become more experienced and

they perceive a tool or device as more useful. Greater experience and frequency of use results in greater familiarity with the technology and better understanding of its use [34].

H4a: Frequency will moderate the effect of product/brand comparisons on outcome expectancy of user’s preferred brand of portable multi-screens.

H4b: Frequency will moderate the effect of perceived self-efficacy on outcome expectancy of user’s preferred brand of portable multi-screens.

Perceived Self-Efficacy

Outcome Expectancy

Product/Brand Comparison

Frequency of use Gender

H1

H2 H3b

H3a

H4b

H4a

Fig.1. Research Model



III. METHODOLOGY

A. Sampling and Data Collection This study is based on empirical data which has been collected through written questionnaire. A survey has been

conducted to examine the hypotheses in this study. Empirical data was mainly collected from University students from various parts of China through offline and online distribution of the questionnaire. Participants are those who have sufficient experience of multi-screen usage especially smartphones, tablet and laptops. We carefully scrutinized the responses for each question. 12 inappropriate responses such as having the same answers to all questions and incomplete responses have been excluded from our sample. In total, 287 valid responses have been analyzed to assess reliability, validity, and hypotheses testing. The choice of a student sample for this study is based on, the extensive usage characteristics of multi-screens among the youth market [35].

B. Measurement Development The survey consisted of several questions designed to measure the constructs in our research model as well as the

classification items such as age group, gender, region, education level, frequency of use and types of specific multi-screen devices they are using. Each construct consisted of multiple scale items that were either adapted from existing related scales or developed for this research where existing scales did not exist.

Most of the items were measured on a five-point Likert scale from ‘strongly disagree’ to ‘strongly agree.’ Backward

translation (with the questionnaire translated from English into Chinese, and back into English) was used to ensure consistency between the Chinese and the original English version of the instrument [36,37]. Face validity of the instrument is established by taking professional advice from a panel of experts and content validity is evaluated by pretesting. Pilot tests have been conducted with a pilot sample of 30 University students. Respondents of the pilot test were requested to provide feedback and suggestions for improvement. Respondents successfully answered all questions with help of the given instructions and they provided some valuable feedback/suggestions. Revised final version of the questionnaire after pilot study was distributed for data collection. All constructs along with their alias used in the analysis model and the respective measurement items are described in Table I below for better understanding of the readers.

TABLE I. DESCRIPTION OF CONSTRUCTS

Constructs Alias in the analysis model No. of measurement items Alias of individual measurement items

Outcome Expectancy OutcomeE 4 OE1,OE2,OE3,OE4

Perceived Self-Efficacy PSEffic 3 SE1,SE2,SE3

Product/Brand Comparison Compare 3 Comp1,Comp2,Comp3

Frequency of Use Frequency 3 FU1,FU2,FU3

Gender Gender 1 Gender

C. Method for analyzing the model Partial Least Squares (PLS) has been chosen for the analysis of the data in this study. Perceived self-efficacy,

product/brand comparisons, outcome expectancy and frequency of use have been considered as latent constructs in our structural model. PLS estimates path models consisting of latent constructs which are indirectly calculated by multiple indicators [38].

D. Respondent’s Profile Table II describes frequencies of our respondents based on gender, age group, education level and frequency of use. Our

sample is good representation of both genders. Most of our respondents are young and falls in the age range of 18 to 28 years. The majority of the respondents are frequent or moderate user of multi-screens which shows that our sample is a good representation of the behavior of interest.

TABLE II. RESPONDENTS’ PROFILE

Measure Item Frequency Percentage %

Gender Male 147 51

Female 138 48



Age Group

Below 18 5 2

18-23 168 59

24-28 98 34

29-33 13 5

34 and Above 4 1

Education Level

Undergraduate 147 51

Graduate/Master Degree 85 30

PhD and Above 54 19

Frequency of use

All the time/Always 5 2

Frequently 115 40

Moderate 135 47

Rarely 25 9

Very Rarely/Never 2 1

IV. RESULTS AND ANALYSIS

A. Measurement Model The measurement model is evaluated by checking four criterions; reliability of every single item, the reliability of every

construct, discriminant validity and convergent validity [39]. We initiated our analysis by checking reliability and validity of first order or lower constructs by performing exploratory factor analysis (EFA), composite reliability (CR) and average variance extracted (AVE) as proposed by various researchers [e.g.,40,41,38]. While composite reliability is tested for reliability of every construct.

The factors loading for the first order items are showing satisfactory item reliability. As presented in Table III, all values are higher than 0.632. While composite reliability of the high order construct is at a satisfactory level also, as all indicators are above the 0.7 threshold (CR = >0.754). Average variance extracted should exceed >0.50 to show good convergent validity [41]. In this case all values are more than 0.50 threshold. Our results showed adequate discriminant validity for our second order latent variables, as all diagonal values are larger than off-diagonal values in the respective rows and columns in Table VI. The values for CR and AVE are shown in Table IV and Table V respectively. As indicated by these results, our entire constructs meet the criterion for reliability, convergent validity and discriminant validity. Overall, our measurement model is satisfactory so we can proceed with our analysis of the structural model.

TABLE III. FACTORS LOADINGS AND CROSS LOADINGS

PSEffic Compare Outcome Frequency

SE1 0.757 -0.055 0.014 -0.124

SE2 0.674 0.012 0.179 -0.08

SE3 0.731 0.047 -0.18 0.202

Comp1 0.099 0.714 0.042 -0.012

Comp2 0.133 0.767 -0.125 0.009

Comp3 -0.266 0.649 0.101 0.003

OE1 -0.199 -0.132 0.708 -0.042

OE2 -0.034 -0.016 0.761 0.067

OE3 0.026 0.033 0.768 0.05

OE4 0.225 0.123 0.649 -0.092

FU1 -0.041 0.132 0.108 0.801



FU2 0.013 0.025 -0.069 0.773

FU3 -0.085 0.047 0.044 0.632

TABLE IV. COMPOSITE RELIABILITY COEFFICIENTS

PSEffic Compare Outcome Gender Frequency

0.765 0.754 0.814 1 0.786

TABLE V. AVERAGE VARIANCES EXTRACTED

PSEffic Compare Outcome Gender Frequency

0.52 0.506 0.523 1 0.533

TABLE VI. THE AVES AND THE CORRELATION BETWEEN THE 2ND ORDER CONSTRUCTS

Construct PSEffic Compare Outcome Gender Frequency

PSEffic 0.721

Compare 0.007 0.711

Outcome 0.317 0.248 0.723

Gender -0.041 -0.053 0.157 1

Frequency 0.089 0.051 0.213 0.087 0.658

B. Structural Model The hypotheses have been assessed by examining the parameters provided by the PLS structural model. Table VII

describes the excellent model fit and goodness indices for our PLS structural model. This implies that, our hypothesized model is meaningful and explaining the behavior of interest. Hence the conclusions drawn from it will be realistic and rational based on practical evidence.

TABLE VII. MODEL FIT AND GOODNESS INDICES

Measure Obtained Value Acceptable Range

Average path coefficient (APC) 0.137, P=0.005 should be significant

Average R-squared (ARS) 0.205, P<0.001 should be significant

Average block VIF (AVIF) 1.148 acceptable if <= 5, ideally <= 3.3

Average full collinearity VIF (AFVIF) 1.148 acceptable if <= 5, ideally <= 3.3

Tenenhaus GoF (GoF) 0.318 small >= 0.1, medium >= 0.25, large >=0.36

R-squared contribution ratio (RSCR) 1.000 acceptable if >= 0.9, ideally = 1

Sympson's paradox ratio (SPR) 1.000 acceptable if >= 0.7, ideally = 1

Nonlinear bivariate causality direction ratio 0.917 acceptable if >= 0.7

Statistical suppression ratio (SSR) 1.000 acceptable if >= 0.7

The results are shown in Figure 2 and Table III. The R2 value of 0.2 indicates that the theoretical model explained a

substantial amount of variance in performance. Also, 20 percent of the variance in outcome expectancies accounted for by the model. The theoretical model exhibited substantive explanatory power given the minimum 10 percent criteria which suggests that R2 of a dependent variable should be minimum percent to make any meaningful impression. Furthermore, such value of R2 is not an unusual phenomenon in social sciences research. There are several studies with low R2 values for example; the



well cited study of Cooper and Kleinschmidt [42] consisted of adjusted R2 scores of 0.27 and 0.21. This modest value of R2 indicates that there are other important factors beyond self-efficacy and product/brand comparisons which can affect outcome expectancy of a product or brand.

Figure 2 presents final structural model in which path coefficients (shown it Table IX) of the suggested model can be

taken as standardized beta weights, each estimated by controlling all other paths effects. To check if the paths are significant, bootstrapping resampling has been conducted. In bootstrapping resampling, the PLS parameters of a series of random sub samples of the total sample are frequently tested, until significance can be estimated based on their convergent findings [43].

TABLE VIII. THE SUM OF DIRECT AND INDIRECT EFFECT OF LV ON PERFORMANCE

PSEffic Compare Gender*Compare Gender*PSEffica Frequency*Compare Frequency*PSEffica

Outcome 0.285 0.215 -0.057 -0.135 -0.066 -0.064

As presented by Table VIII, Our structural model can be made an abstract by noting the following significant direct

effects of LVs: PSEffica predicted OutcomeE (β = 0.29, p < 0.01) and Compare predicted OutcomeE (β = 0.21, p < 0.01). Frequency cannot moderate both relationships of compare to OutcomeE (β = -0.069, p =0.13) and PSEficca to Outcome and predicted RE(β = -0.066, p =0.14). At the same time Gender is moderating well the PSEffica and OutcomeE (β = 0.14, p < 0.01) but it doesn’t have any significant impact on relationship between Compare and OutcomeE (β = -0.07, p =0.13).The descriptive analysis explains that the PSEffica is 30% more effective in predicting OutcomeE within female population.

Change from direct to overall effects (i.e., direct + indirect effects) can suggest important indirect effects which means;

contribution of a variable through its contribution to other variables. Some important variables’ contributions were demonstrated in full effects results. For every such result, we estimated indirect effects through the methods specified by N. Kock and Gaskins (2014) [44].

C. Hypotheses Results As shown in Figure 2 and Table IX, the results suggest that PSEffic is the most important variable in performance

prediction regarding status or quality of Outcome(28.5%). Our findings show that PSEffic is positively related to Outcome (p< 0.01), thereby supporting H1. We also find support for H2 (p< 0.01), which predict that Compare is positively related to Outcome. H3b is also supported (p< 0.01) as our results demonstrate that Gender is moderating the prediction of Outcome by

Compare (R)3i

Frequency (F)5i

PSEffic (R)3i

Gender (F)5i

OutcomeE (R)4i

β=0.21 (P<.01)

β=0.29 (P<.01)

β=0.06 (P=0.14)

β=0.14 (P<.01)

β=0.07 (P=0.13) β=0.06

(P=0.17)

R2 .

Fig.2.The structural model results.



PSEffic. The results reject our supposition regarding H3a, H4a and H4b showing that the Gender does not affect Compare-Outcome and Frequency does not have any moderating effect on the independent – dependent relationships of this model’s variables. Table VIII shows total sum of negative and positive impact of Latent construct on performance. Negative total effect of Gender on PSEffic (-0.135) implies that this effect is more significant for the lower value of Gender (female=s2).

TABLE IX. ΒETA COEFFICIENTS AND HYPOTHESES TESTING

H1 H1 H3a H3b H4a H4a

β Coefficient 0.29 0.21 0.06 0.14 0.07 0.06

Hypothesis Supported Supported Rejected Moderately Supported Rejected Rejected

V. DISCUSSION

Before proceeding to analysis of structural model, measurement model was tested which showed good reliability and validity by fulfilling various criteria like, factors loadings, composite reliability (CR) and discrminant validity. These results proved that the studied model is an appropriate measure and further analysis can be performed. Then structural model also showed excellent model fitness and met all set statistical criterion. Hence our hypothetical research model is good enough and rational which means, further conclusions drawn from this analysis will be logical and practically proved.

Four key findings have appeared from this study. First, the results highlight the significance of self-efficacy in preference construction transmitted through outcome expectancy which is in line with the previous findings (i.e., [18], [21]). Second, our results also confirm the notion of the significance of product/brand comparison in determining outcome expectancy in context of consumer brand preferences. Third, it points out the important phenomenon of gender influencing relationship between self-efficacy and outcome expectancy by showing that female with higher self-efficacy will be able to see more benefits of a product/brand than male. Fourth, it proves that consumers’ frequency of use does not change the way they perceive a brand or model of portable multi-screens in term of its usefulness. The frequency of use has no significant impact on relationship between independent and dependent variables.

VI. IMPLICATIONS AND FUTURE RESEARCH DIRECTION

The present study contributes to the literature in different ways. Firstly, to our knowledge, this is one of the initial studies related to multi-screen user behavior. The prior research in this area has tested many variables to predict brand preferences of digital devices. This paper has focused on important preference-forming construct of outcome expectancy. Particularly, this study shows how users develop their perception of outcome expectancies or usefulness about their preferred brand of portable multi-screen devices. Secondly, the simple but parsimonious research model is an vital contribution to the current and emerging literature on consumer behavior, by choosing the imperative variables and applying them to a new context of multi-screens based on the prevailing concepts of self-efficacy, outcome expectancies and relatively less studied construct of brand/product comparison in a new perspectives; how it can influence outcome expectancy. The model provides interesting insights by using Gender and Frequency of use as moderating factors. This work also adds to the existing knowledge concerning the antecedents of outcome expectancy.

These results have insightful practical implications in the related areas especially in marketing, sales and software application development. As the study suggests people with higher capability of device usage can better understand or predict outcome expectancy of specific brand/product, marketers may introduce some introductory training or detailed product evaluations sessions for target customers. Also, customers should be facilitated in terms of brand/product comparisons at point of sales based either online or offline so they can grasp the usefulness of devices more easily. This paper elicits few vital insights for professional in design and product development. Product designers and developers should incorporate more easy to use features in these devices so that majority of consumers can perceive them as useful for them. A very novel and technically landmarked key feature of a product may not contribute towards profitability of a firm if it is not useful for its end users or they lack the capability to use it.

There are ample of research opportunities within field of multi-screens. For example future researcher may extend the knowledge in this area by investigating same factors in other age-segments and population or they might consider other important factors constituting outcome expectancy of multi-screen devices. Multi-screen is newly developed field of consumer behavior which is open to variety of explorations. Another important aspect related to this is consumer’s everyday



multitasking with multi-screen devices because most of user’s screen time is spent in multitasking with these devices. There are some researches related device multitasking but media context but overall understanding of why people combine multiple devices to fulfill their daily needs is lacking. Research concerning with motivations behind different multi-screen use pattern is seriously lacking and future researchers should emphasize on this.

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BIOGRAPHY Shahid Kalim Khan is Ph.D. candidate at School of Management and Economy at Harbin Institute of Technology, Harbin, China. He earned his Master Degree in Project Management from Royal Institute of Technology (KTH), Sweden, and Master in Business Administration with focus on International Marketing from Mälardalens University Sweden. While he has completed his Bachelors Honors degree from Government College University Lahore, Pakistan. He has worked as Lecturer at



Department of Management Sciences, COMSATS Institute of Information Technology Pakistan for several years. Mr. Khan has plenty of industrial and corporate-sector work experience in multinational firms around the world. Multi-screen user behavior is his top research interest along with, social media, co-creation and dual process models of consumer behavior. . Dr. Li Guoxin is a Professor of Marketing and Management at Department of Management and Economy, Harbin Institute of Technology, China. She is also working as visiting scholar in USA and Canada from past few years. She has published in various prestigious Journals including ‘Journal of Business Research’ and ‘Journal of Product Innovation Management’ among others. Dr. Guoxin is Editor of Journal of Global Association of Marketing Science and Reviewer of Cyber-Psychology, Behavior, and Social Networking (SSCI). Apart from Journal publications, she has also presented papers and chaired in many conferences globally. Professor Li Guoxin has done several research projects in collaboration with public and private sector. Her key research areas are; social capital and social network, E-commerce, consumer behavior, co-creation and innovation management.

Ehsan Chitsaz is a Ph.D. student majored in business administration, Harbin Institute of Technology. He was a manager in Isfahan science and technology town and research on internal factors affecting new firms performance. His recent interest is the psychology of competition. His recent articles published in Technological Forecasting and Social Change and Technovation.

Naqash Ali is a graduate student at COMSATS institute of information technology Pakistan. He has completed his MBA degree from the same institution. His recent research interest includes, multi-screen behavior, lagging team members’ performance management, brand preference and real estate development in Pakistan.

Ume Ammara is a graduate student at COMSATS institute of information technology Pakistan. She earned her MBA education from the same institution. Her recent research are; multi-screen use behavior, project management, quality management and textile sector.


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