smartphone usage & job performance - assistance or ... · smartphone usage intensity has n ot...

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Smartphone Usage & Job Performance - Assistance or

Interference to Work

1Dr.M.KirupaPriyadarshini ,

2Preetha Leena .R ,

3Dr. R. Venkatesan,

1Associate Professor, Department of Management, Kumaraguru College of Technology,

Coimbatore, Tamilnadu, India. 2Research Scholar in Management Studies, Bharathiar University,

Coimbatore, Tamilnadu, India. 3Professor, Department of Mechatronics Engineering, Kumaraguru College of Technology,

Coimbatore, Tamilnadu, India. [email protected], [email protected]

Abstract

Smartphone’s have become increasingly popular for personal and work use.This research

investigates Smartphone usage at workplace, whether it is of assistance or interference to

work and its implications on performance of employees. This survey method consists of a

sample of 576 IT Professionals. It was observed thatknowledge sharing and flexibility has

more influence on the assistance to work variables on the job performance of the

employees. Moreover Non-work purposes and Job Stress has more influence among the

interference to work variables on the job performance of the employees.

1. Introduction

In the country of 1.3 Billion people, mobile phone subscriptions in India have already

reached 1 Billion, according to the latest data by TRAI. The low cost of mobile data and free

accessibility to the internet via Wi-Fi in many areas, is fascinating Smartphone users like

never before. In addition, expansion of 3G and 4G network coverage is further expected to

boost Smartphone sales in the country. According to an analysis, the Smartphone shipment

has grown at a CAGR of around 76% during the period 2010-2015. (ASSOCHAM, 2016)

Hence this study aims to explore Smartphone usage at workplace, whether it is of assistance

or interference to work and its impact on the job performance of IT professionals in Chennai,

International Journal of Pure and Applied MathematicsVolume 119 No. 17 2018, 2651-2663ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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with a view to suggest measures to the Software Organisation for attaining balance in Work

life realms and improved productivity.Studies concerning impact of Smartphone on

Assistance to work and Interference to work have predominantly taken few factors only in

previous studies. Smartphone Usage Intensity has not been studied much in India in IT

Companies. This is the first study of its kind to combine the impact of Assistance to work,

Interference to work, Smartphone Usage Intensity, Job Performance and Productivity.

2. Literature Review

According to the International Telecommunications Union (ITU), a United Nations agency

that conducts statistical data on Information Communications Technology (ICT), mobile

device usage has grown at an amazingly fast rate in the past 10 years. With this has come a

giant leap forward in the proliferation of Smartphone and their use to access the internet. As

current mobile trends develop, it has become apparent that mobile Smartphone cannot be

ignored in marketing research initiatives any longer, Fluid Surveys (2013). Reasons for this

are primarily that it allows for many communication possibilities, all in the same device,

Murad Moqbel, (2012), Jacob Grauers, (2012). Furthermore, the Smartphone usage is

constantly increasing, both seen to numbers of users and data traffic generated (Cisco, 2012).

Assistance to Work

Technology advances made it so that people no longer have to wait to be at a computer, or at

the office, to accomplish their work. This should allow for maximum flexibility for

employees to work wherever is convenient for them, Tricia R. Harris (2014).Chu and Chan

(2009) suggest that in today‘s rapidly changing business environment innovative

organizations could use online communities to identify and capture new ideas for creating

new products and services.

Knowledge sharing is an activity through which knowledge (namely, information,

skills, or expertise) is exchanged among people, friends, families, communities, or

organizations. For IT professionals, whose performance depends a lot on the acquisitionof

technical and organizational knowledge, their social networks are an indispensable informing

channel, Burton, Wu, & Prybutok(2010). A Research on Mobile Learning at Workplace by

(Towards Maturity, 2014) found that those with several years‘ experience in

usingtechnology-enabled learning report higher levels ofmobile usage, but we also see a

International Journal of Pure and Applied Mathematics Special Issue

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spike in usage in thosethat are new to using learning technologies, implyingthat some are

adopting mobile solutions as part of theirfirst steps with learning technologies.

Communication is a process in which participants create and share information with

one another in order to reach a mutual understanding, Carol Xiaojuan Ou Choon Ling Sia

Chun Kit Hui, (2013).The technological advances and increased use of the Internet in recent

years have ledto a communication revolution, (Moqbel, 2012). This communication

revolution, as well asthe more technologically empowered lifestyle of individual users, has

changed the waypeople communicate and connect with each other (Coyle, 2008; O‘Murchu

et al., 2004).

Smartphone enables employees to Organise their work/ manage their work; it gives more

opportunity to be creative and efficient in scheduling of work and meetings. Smartphone also

gives an opportunity to individualise work and provide physical freedom of movement in

work.Smartphone technology also enables users to collaborate digitally with peers, co-

constructing knowledge, Mcgreen & Sanchez, (2005) digitally (via wikis, web forums, blogs

etc) and sharing it (via email, social networking sites like Facebook/LinkedIn and virtual

folders suchas Dropbox), Tonta (2008). Younger employees and those with a millennial

mindset find it hard to draw the line between their personal and professional lives and seek

the flexibility and ease-of-use that their personal devices provide (Cognizant. 2012).

Flexibility in the workplace allows employers and employees to make arrangements about

working conditions that suit them. This helps employees maintain a work/life balance and can

help employers improve the productivity and efficiency of their business. Job flexibility in

this study centres on the location and the timing flexibility of work(ubiquitous

access),Haejung Yun, et al,(2012). These evolving technologies also have an impact on the

way of working. The telecommunication tools made it possible to do the job that was

formerly done in the office, outside the office.

Interference to Work`

Interference to job tasks included drawbacks such as receiving information from Social

Networks which cannot be handle by individuals while at work, receiving information that

are not useful for work and getting distracted from work,Vathsala Wickramasinghe and

M.S.M.Nisaf, (2013). Heavy Smartphone and computer usage is causing stress, insomnia and

depression physical symptoms reported in relation to mobile phone useinclude headaches,


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earache, and warmth sensation but alsoperceived concentration difficulties and

fatigue,Thomée, (2012).

Work overload the situation in which someone has too much work to do. The dual

use (work and nonwork use) of smartphone environments has resulted in a merger of work

and personal life. This can cause a situation in which employees perceive greater work

overload than before using their smartphones for work purposes,Haejung Yun, William J.

Kettinger, and Choong C. Lee,(2012).

The deviant use of Internet technology may distract employees to engage in non work

purposes, from job tasks on hand and may engage in unproductive or unethical activities such

as online shopping, news, music, chatting, auctioning and games while at work,Vathsala

Wickramasinghe and M.S.M.Nisaf, (2013).

According to Thomée (2012), Stress caused by the increased usage of information and

communication technology (in our case, particularly with regards Smartphone) can come in

different forms. The boundlessness that a Smartphone creates means that it is easy to perform

work related tasks in one‘s leisure time and to do leisure time activities at work. Just this

basic effect of Smartphone usage can cause role stress, role conflicts and role overload.

Smartphone usage distracts employees from information at meetings, and also annoys

others (Ebelhar, 2009). Chen et al, (2008) suggest that the deviant use of Internet technology

may distract employees from job tasks on hand and may engage in unproductive or unethical

activities such as online shopping, news, music, chatting, auctioning, and games while at

work.

The term behavioural addiction correctly refers to a compulsion to engage in a natural

reward, which is a behaviour that is inherently rewarding (i.e., desirable or appealing),

despite adverse consequences, Malenka RCet al, (2009), Robison AJ, (2011). Media use has

become so much a part of young adults‘ lives that many do not realize their level of

dependence and/or addiction to their cell phones, James.A.Roberts et al,(2014).In this regard,

Chen et al, (2008) and Stanton, (2002) provide evidence that internet addiction significantly

impacts employees‘ internet abuse at the workplace. For instance, Irfan Ahmed Mohammed

Saleem, Dr. S. Jaisankar (2018), employees‘ Online Social Networking addiction may result

in a decline in employees productivity (Ferreira and du Plessis, 2009; Lichtash, 2004; van

Zyl, 2009)


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3. Research Methodology

The research problem clearly states thatthe aim of the study is to explore the influence of

Smartphone Usage Intensity on Assistance to Work, Interference to Work,Job Performance

and Productivity of IT professionals. This becomes important because the popularity of

Smartphone is relatively new, and employees engaging in Smartphone during office work

hours are growing while scholarly research in this area is presently lacking. Hence this is an

exploratory research. Both Indian and International studies were taken into consideration.

From the previous studies the independent and dependant variables were listed. This survey

method consists of a simple random sample of 576 IT Professionals based in Chennai.

Questionnaires were distributed to 800 employees and 605 were returned out of which 576

completed questionnaires were received. . A questionnaire was adapted from previous studies

suitable for the present study. The questions were referered from previous work of

researchers and were revised for easy understanding of the employees. The questions on

Smartphone usage were rated on a five point Likert scale, having 1 = very low, 2 = low, 3 =

neither high nor low, 4 = high, 5 = very high and the other questions were rated on a five

point Likert scale, having 1=Strongly Disagree, 2= Disagree, 3= neither Agree nor Disagree,

4= Agree, 5 = Strongly Agree.

Research Objectives

To study the influence of Smartphone Usage and Assistance to work on Job Performance.

To study the influence of Smartphone Usage and Interference to work on

JobPerformance.

4. Data Analysis Procedures

Every hypothesis is tested to check for statistical significance. The statistical techniques are

applied using the Statistical Package for Social Sciences (SPSS) computer program for

Windows version 20.0. T-Test, Correlation and SmartPls were used to test the hypothesis and

model of the study.

The construct assistance to work has four variables namely knowledge sharing,

communication, organising work and flexibility. Existence of difference in the perception of


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respondents with respect to the four variables was tested across the High and Low

Smartphone Users. The hypothesis is as below.

Testing of Model Fit using SmartPLS

Partial Least Squares is a family of alternating least squares algorithms, or ‗‗prescriptions,‘‘

which extend principal component and canonical correlation analysis. The method was

designed by Wold (1975, 1982, 1985) for the analysis of high dimensional data in a low-

structure environment and has undergone various extensions and modifications. Partial Least

Squares- Path Modeling is a statistical approach for modelling complex multivariable

relationships among observed and latent variables. Structural Equation Models include a

number of statistical methodologies allowing the estimation of a causal theoretical network of

relationships linking latent complex concepts, each measured by means of a number of

observed indicators, Vincenzo Esposito Vinzi, (2010).

Model 1 – Assistance to Work

The hypothesized framework was constructed with Smartphone Usage Intensity as an

independent construct and Assistance to Work constructs as a mediating construct. Job

Performance and Productivity are the dependent variables. The model was tested for

goodness of fit and the results are as follow.

.

Fig 4.1 Partial Least Squares Model 1 – Assistance to Work


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Table 4.3 Fitness of the Model 1 – Assistance to Work

Path Analysis Mean (STDEV) T Statistics

Communication -> Job Performance and Productivity 0.0576 0.1817 0.3502

Flexibility -> Job Performance and Productivity 0.1865 0.1169 1.6455

Knowledge Sharing -> Job Performance and Productivity 0.7451 0.1546 4.8562

Organising Work -> Job Performance and Productivity 0.0739 0.2358 0.3364

Smartphone Usage Intensity -> Communication 0.7075 0.059 11.9255

Smartphone Usage Intensity -> Flexibility 0.5454 0.0518 10.239

Smartphone Usage Intensity -> Job Performance and Productivity 0.4388 0.0705 5.9807

Smartphone Usage Intensity -> Knowledge Sharing 0.5708 0.0691 8.0202

Smartphone Usage Intensity -> Organising Work 0.6629 0.0643 10.1516

Fitness of the Structural Model

The t statistics in the table above indicate that all structural path coefficients are statistically

significant (>0.05) (Chin,1998, 2010, Henseler et al.,2009, Latan & Ghozali,2013). The

values well abide the prescribed limits thus ensuring that the structural model has a good fit.

From the table it can be inferred that knowledge sharing (4.85) followed by flexibility(1.64)

have a more influence among the assistance to work variables on the job performance and

productivity of the respondent. Moreover Communication and Flexibility increase as

Smartphone usage intensity increases.

Model 2 – Interference to Work

The hypothesized framework was constructed with Smartphone Usage Intensity as an

independent construct and Interference to Work constructs as a mediating construct. Job

Performance and Productivity are the dependent variables. The model was tested for

goodness of fit and the results are as follow.

Table 4.4 Fitness of the Model 2 – Interference to Work

Path Analysis Mean (STDEV) T Statistics

Addiction -> Job Performance and Productivity 0.0237 0.152 0.437

Distraction -> Job Performance and Productivity 0.1249 0.1852 0.9838


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Job Stress -> Job Performance and Productivity 0.2614 0.3429 1.1323

Non Work Purposes -> Job Performance and Productivity -0.3231 0.5089 1.1816

Smartphone Usage Intensity -> Addiction 0.497 0.1184 4.3382

Smartphone Usage Intensity -> Distraction 0.5851 0.1212 4.8773

Smartphone Usage Intensity -> Job Performance and Productivity 0.2595 0.3213 1.1467

Smartphone Usage Intensity -> Job Stress 0.6746 0.1298 5.2666

Smartphone Usage Intensity -> Non Work Purposes -0.0353 0.2103 0.1285

Smartphone Usage Intensity -> Work Overload 0.5436 0.0938 5.8834

Work Overload -> Job Performance and Productivity -0.0507 0.1509 0.6623

Fitness of the Structural Model

The t statistics in the table above indicate that all structural path coefficients are statistically

significant (>0.05) (Chin,1998, 2010, Henseler et al.,2009, Latan & Ghozali,2013). The

values well abide the prescribed limits thus ensuring that the structural model has a good fit.

From the table it can be inferred that Non-work purposes (1.18) followed by Job Stress (1.13)

have more influence among the interference to work variables on the job performance and

productivity of the respondent. Moreover Work Overload (5.88) and Job Stress (5.26)

increase as Smartphone usage intensity increases.

5. Conclusion

Results have confirmed that there exists difference in perception of Communication and

Flexibility by the respondents of who have high and low Smartphone usage. Whereas for the

variables Knowledge Sharing and Organising Work there is a no difference in perception of

Knowledge Sharing and Organising Work by the high and low Smartphone users. Knowledge

sharing and flexibility have more influence among the assistance to work variables on the job

performance and productivity of the respondent. Moreover Communication and Flexibility

increases as Smartphone usage intensity increases

There exists difference in perception of Non work purposes, Job Stress and Addiction by the

respondents who have high and low Smartphone usage. Whereas for the variables Work

Overload and Distraction there is a no difference in perception of Work Overload and

Distraction by the high and low Smartphone users. Non-work purposes and Job Stress have

more influence among the interference to work variables on the job performance and


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productivity of the respondent. Moreover Work Overload and Job Stress increases as

Smartphone usage intensity increases.

Smartphone introduces a multitude of challenges; however, organizations and employees

should treat thisas an opportunity that can yield significant benefits and improved

productivity. The key is to approach this in a holistic fashion to address employee

expectations,while ensuring business requirements are met related to non-work purposes, job

stress, flexibility and knowledge sharing.

References

[1] ASSOCHAM, (2016), ―M-Wallet: Scenario Post Demonetisation‖, Conference on

Indian M-Wallet Market organized by ASSOCHAM.

[2] Brima Aribowo ,(2012), "The Impact of Telework Attitude and Frequency to the

Work/Life Balance, Productivity and Teamwork of Employees Measured by a Daily

Mobile Smartphone Application", Thesis, Rotterdam School of Management Erasmus

University.

[3] Carol Xiaojuan Ou Choon Ling Sia Chun Kit Hui, (2013), "Computer-mediated

communication and socialnetworking tools at work", Information Technology &

People.

[4] Chen, B.X. (Feb 14, 2012), Get ready for 1 billion smartphones by 2016, Forrester

says.

[5] Chen, J.V., Chen, C.C. and Yang, H-H, (2008), ―An empirical evaluation of key

factors contributing to internet abuse in the workplace‖, Industrial Management &

Data Systems, Vol. 108 No. 1, pp. 87-106.

[6] Chin,(1998), ―The Partial Least Squares Approach to Structural Equation Modeling‖,

Modern Methods for Business Research, Ch-10,pp295-336

[7] Chu, K-M. and Chan, H-C. (2009), ―Community based innovation: its antecedents

and its impact on innovation success‖, Internet Research, Vol. 19 No. 5, pp. 496-516.

[8] Cisco, (2013) ,"BYOD Insights 2013: A Cisco Partner Network Study", Cisco

mConcierge.

[9] Coyle, C.L.V.H. (2008), ―Social networking: communication revolution or

evolution?‖, Bell Labs Technical Journal, Vol. 13 No. 2, pp. 13-17.

[10] Ebelhar, J, (Jun 21, 2009), ―Mind your BlackBerry or mind your manners‖.

The New York Times.

[11] Ferreira,T. du Plessis, (2009), ―Effect of online social networking on

employee productivity‖, InterWord Communications.

[12] Forrester, (2012), "The Expanding Role Of Mobility In The Workplace", A

Custom Technology Adoption Profile Commissioned By Cisco Systems.

[13] GoogleMobileAds. (2011) Voice of the smartphone user: South Korea.

www.youtube.com, Retrieved from http://www.youtube.com/watch?v=3j_

2srh7o3M& feature=plcp


2659

10

[14] Haejung Yun, William J. Kettinger, and Choong C. Lee,(2012), "A New Open

Door: The Smartphone‘s Impact on Work-to-Life Conflict, Stress, and Resistance",

International Journal of Electronic Commerce.

[15] Harri Jalonen, (2014), "Social Media And Emotions In Organisational

Knowledge Creation", Proceedings of the 2014 Federated Conference on Computer

Science and Information Systems.

[16] Henseler, Christian M. Ringle and Rudolf R. Sinkovics, (2009), ―The Use Of

Partial Least Squares Path Modeling In International Marketing‖, New Challenges to

International Marketing Advances in International Marketing, Volume 20, 277–319.

[17] Jacob Grauers, Emil Wall, (2012), "Preventing stress through smartphone

usage", Bachelor of Informatics Thesis, University of Gothenburg.

[18] James A. Roberts , Chris Pullig , Chris Manolis,(2015), "I need my

smartphone: A hierarchical model of personality and cell-phone addiction",

Personality and Individual Differences-Science Direct.

[19] King, A. L. S., Valença, A. M., Silva, A. C. O., Baczynski, T., Carvalho, M.

R., & Nardi, A. E. (2013), ―Nomophobia: Dependency on virtual environments or

social phobia?‖, Computers in Human Behavior, 29, 140–144.

[20] Latan.H and Ghozali.I, (2013), ―Partial Least Squares: Concept and

Application Path Modelling using Program XLSTAT-PLS‖, Badan Penerbit

Universitas Diponegoro, Semarang

[21] Lichtash, A.E. (2004), ―Inappropriate use of e-mail and the internet in the

workplace: the arbitration picture‖, Dispute Resolution Journal, February-April, pp.

26-36.

[22] Malenka RC, Nestler EJ, Hyman SE (2009). "Chapter 15: Reinforcement and

Addictive Disorders". In Sydor A, Brown RY. Molecular Neuropharmacology: A

Foundation for Clinical Neuroscience (2nd ed.). New York: McGraw-Hill Medical.

pp. 364–365, 375.

[23] McGreen, N. and Arnedillo Sánchez, I., (2005), "Mapping Challenge: A Case

Study in the Use of Mobile Phones in Collaborative, Contextual Learning", In IADIS

International Conference Mobile Learning. Qawra, Malta: IADIS.

[24] Miller-Merrell, J. (2012). ―The workplace engagement economy where HR,

social, mobile, and tech collide‖, Employment Relations Today. n.d. 1-9.

[25] Moria Levy , (2013),"Stairways to heaven: implementing social media in

organizations", Journal of Knowledge Management, Vol. 17 Iss 5 pp. 741 - 754

[26] Murad Moqbel, (2012), "The Effect Of The Use Of Social Networking Sites In

The Workplace On Job Performance", Thesis-Doctor Of Philosophy In International

Business Administration, Texas A&M International University.

[27] Nicolette Bakhuisen, (2012), "Knowledge Sharing using Social Media in the

Workplace A chance to expand the organizations memory, utilize weak ties, and share

tacit information?", Thesis submitted to VU University Amsterdam, Department of

Communication Science.

[28] O‘Murchu, I., Breslin, J.G. and Decker, S. (2004), Online Social and Business

Networking Communities, Digital Enterprise Research Institute, Galway.


2660

11

[29] Paul Burton,Yu ―Andy‖ Wu and Victor R. Prybutok(2010),"Social Network

Position and Its Relationship to Performance of IT Professionals",the International

Journal of an Emerging Transdiscipline

[30] Rabia Sarwar. Abdul Ghafoor, (2014), "Mobile Phones Usage and Employees‘

Performance: A Perspective from Pakistan", International Journal of Academic

Research in Accounting, Finance and Management Sciences. [31] Robison AJ, Nestler EJ, (2011). "Transcriptional and epigenetic mechanisms of

addiction". Nat. Rev. Neurosci. 12 (11): 623–637.

[32] Stanton, J.M. (2002), ―Company profile of the frequent internet user‖,

Communications of the ACM, Vol. 45 No. 1, pp. 55-59.

[33] The New York Times (Bits).Retrieved

fromhttp://bits.blogs.nytimes.com/2012/02/13/getready-for-1-billion-smartphones-by-

2016-forrester-says/.

[34] Thomée, S., Dellve, L., Härenstam, A., & Hagberg, M. (2010), ―Perceived

connections between information and communication technology use and mental

symptoms among young adults—a qualitative study‖, BMC Public Health, 10, 1–14.

[35] Thomée, S., Härenstam, A., & Hagberg, M. (2011), ―Mobile phone use and

stress, sleep disturbances, and symptoms of depression among young adults—a

prospective cohort study‖, BMC Public Health, 11, 1–11.

[36] Tonta (2008), ―Information services and Web 2.0 new challenges and

opportunities‖, The Electronic Library.25(5), 1-8.

[37] Towards Maturity, (2014), "Mobile learning in the workplace", Infocus report.

[38] Tricia R. Harris,(2014),"The Impact Of Smartphones On Work-Life Balance",

Thesis-Masters of Arts in Industrial and Organizational Psychology, Middle

Tennessee State University.

[39] van Zyl, A.S. (2009), ―The impact of Social Networking 2.0 on

organisations‖, The Electronic Library, Vol. 27 No. 6, pp. 906-918.

[40] Vathsala Wickramasinghe and M.S.M.Nisaf, (2013),"Organizational policy as

a moderator between online social networking and job performance", VINE, The

Journal of Information and Knowledge Management Systems. Vol. 43 Iss 2 pp. 161 –

184.

[41] Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler, Huiwen Wang,

(2010), ―Handbook of Partial Least Squares: Concepts, Methods and Applications‖,

Springer.

[42] Walsh, Shari P. and White, Katherine M. and Young, Ross McD. (2010),

―Needing to connect : The effect of self and others on young people‘s involvement

with their mobile phones‖, Australian Journal of Psychology, 62(4). pp. 194-203.

[43] Wold, H. (1975). PLS path models with latent variables: the nipals approach.

In H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, and V. Cappecchi

(Eds.), Quantitative sociology: international perspectives on mathematical and

statistical modeling. New York: Academic Press.


2661

12

[44] Wold, H. (1982). Soft modeling: the basic design and some extensions. In K.

G. J¨oreskog, and H. Wold, (Eds.), Systems under indirect observation, Part II (pp. 1–

54). Amsterdam: North-Holland.

[45] Uma Maheswari. S And Vasanthanayaki.C. (2017), ―Performance Analysis Of

Compressing Sensing Framework On Interactive Media Content‖, International

Journal Of Pure And Applied Mathematics, Vol. 116, No. 11, Pp. 241-250.

[46] Wold, H. (1985). Partial least squares. In S. Kotz, and N. L. Johnson, (Eds.),

Encyclopedia of Statistical Sciences, Vol. 6 (pp. 581–591). New York: Wiley.

[47] Y. Connie Yuan et al., (2013), "The Use of Different Information and

Communication Technologies to Support Knowledge Sharing in Organizations: From

E-Mail to Micro-Blogging", Journal Of The American Society For Information

Science And Technology.

[48] Irfan Ahmed Mohammed Saleem, Dr. S. Jaisankar (2018), A Study On Kaizen

Based Soft-Computing In Electric Vehicle Manufacturing Processes, International

Journal Of Innovations In ScientificAnd EngineeringResearch, Vol5 , no 5,.Pp 31-39.


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