Technology gadgets can be literally labelledas the ruling authority across the globe, as the life without technology hasbecome impossible. The modern technologies have factually changed the waythrough which the individuals perceive and approach the world. However many individualsare highly prone to be more dependent to the use of technology gadgets, but thebiggest challenge is the high necessity of the technology gadgets in the dailylife it has become very difficult to distinguish between the normal andaddictive use of technology gadgets and also have the high possibility to havea huge impact on the human abilities that raises the necessities to study aboutthe technology gadgets influence on the cognitive functions. Researches showthat increases use of technology gadgets is associated with the issues ineveryday executive functioning of the individual and decreases the individual’sability to think analytically (Barr et al, 2015). Eventhough the research on the negative impact of technology gadgets on cognitionof the individual is evolving, the results in most of the research tends to beconflicting to each other and unconvincing which at times shows that not allthe technology gadgets and it service have an equal impact on the cognitiveabilities. Irrespective of the results media made an huge effect on the publicperception that all the research finding tends to be very convincing and thetechnology gadgets are having an absolute negative influence on the cognitive functioning of theindividual (Richtel, 2010).
Researches also clearly emphasizes that many of the negative cognitivefunctioning are mostly caused through the indirect effect of the use oftechnology gadgets such as its direct influence on the sleep and mood and thequality of sleep and negative mood has an severe impact on the cognitiveabilities (Lim and Dinges, 2008). Fascinatingly, there is also some researchevidence signifying that individuals vulnerability to cognitive disruption fromtechnology use and the consequential influence on ability to perform cognitivefunctions is more likely to depend on the individuals existing cognitive skillsset, particularly the individual’s ability to exert self regulatory control ontheir behaviour (Tams et al, 2015).One of the veryimportant cognitive ability is the cognitive flexibility. Cognitive flexibilityis defined as the ability to change behaviour such as thoughts or actions inresponse to situational demands (Can˜as, Antoli, Fajardo, & Salmero´n,2005; Lezak, 2004). It includes problem solving, planning, achievement and goaldevelopment which is a element of executive functioning, the group of higherorder cognitive abilities (Anderson, 2002; Burgess & Alderman, 2004;Dubois, Slachevsky, Litvan, & Pillon, 2000; Strauss, Sherman, & Spreen,2006). These abilities are considered to be essential for focused individualbehaviour (Lezak, 2004) and necessitate spontaneous and reactive mechanism(Eslinger & Grattan, 1993).
The processes of foundation to cognitiveflexibility are dynamic, involving cycles of thought generation and suppressionthat appear and dissolve as the individual interacts with varying environmentalfactors like relative cues and task demands (Ionescu, 2012). This modelproposed by Ionescu (2012) has been described as a “unified framework ofcognitive flexibility”, linking a number of cognitive components or mechanismswhich includes various executive functions, attention, perception, goalparameters and monitoring in conjunction with task demands, contextual cues andsensory-motor input. Therefore cognitive flexibility encompasses more thansimple response switching. Research indicates that there are six core processessuch as acceptance, diffusion, self as context, contact with the presentmoment, values and committed action which are involved in achieving cognitiveflexibility. These processes contribute to the development ofcognitive flexibility but the relative contribution of each process and how it differswith each individual is highly vague.
(Hayes, Luoma, Bond, Masuda, & Lillis, 2006) Research also suggests that whenever theseprocesses are not implemented it results in the cognitive inflexibility withthe rise of the experiential avoidance. Experiential avoidance, as opposed toacceptance, occurs when a person actively attempts to change experiences, bothinternal and external, that gives rise to difficult thoughts and emotions (Ruiz, 2010). Recentresearch on technology addiction on adolescents have clearly outlined thatexperiential avoidance explained the outcome of the pattern regarding theaddictive use of technology gadgets and the study concluded that experientialavoidance should be considered while studying the technology dependence (GarciaOliva C, Piqueras JA, 2016). Another study also suggests that experientialavoidance is positively related to the internet addiction (Wei-Po Chou et al,2017)Researches show that individuals who areaddicted to internet tend to have impaired level of cognitive flexibility (DongG, Lin X, Zhou H & Lu Q, 2014). Researchalso suggests that mobile technologies have high possibility to influence anextensive array of cognitive domains, but empirical research on the cognitive impactof mobile technology is narrow.
This is reasonable because technology itself arelatively very young and growing at a tremendous rate however every yearmobile technology is becoming a basic necessity in all the individual lives (eMarketer, 2014). However from the literature it is clearly evident that there is a hugegap on the study of the technology dependence and there also arises a strongneed to study about the relationship between technology dependence, cognitiveflexibility and experiential avoidance. So this study is particularly opted tobridge the gap on the study of the influence technology dependence on thecognitive flexibility and experiential avoidance.MethodologyAim: The present study aimed to assess theinfluence of technology dependence on cognitive flexibility and experientialavoidance among the college students.Objectives:1. Toexamine the technology dependence, cognitive flexibility and experientialavoidance among the college students 2. Toexamine the relationship between the technology dependence, cognitiveflexibility and experiential avoidance among the college students.Hypothesis:1.
Thereexists no significant relationship between technology dependence and cognitiveflexibility and experiential avoidance among the college students.2. There exists no significant relationshipbetween cognitive flexibility and experiential avoidance among the collegestudents.Sample: College students were selectedrandomly from the colleges in and around the Coimbatore city, from these 100 collegestudents were filtered (50 males, 50 females) based on those who have exposureto more than 2 technology gadgets, services and college students within the agegroup of 17 to 22. Data was collected from the college students usingsystematic random sampling method.
Other inclusion and exclusion criteria areas follows.InclusionCriteria:1. Agedfrom 17-22 year’s college students2. Exposedto more than two technology gadgets and it services3. Thosewho are willing to sign the informed consent form and Voluntary ParticipationExclusionCriteria:1. Presenceof any health problems which might interfere in taking the administered tools2. Collegestudents who exceeds or falls below the age category of 17-223. Thosewho are not exposed to more than two technology gadgets and it services Tools:1.
SocioDemographic Details:Thissocial demographic data is intended to gather information regarding the name ofthe participant, age, gender, family type and domicile.2. Gadget’sUse Scale:Thisscale was developed by Munduli in 2014. The Gadget Use Scale consists of thequestions regarding the use of the gadgets in a tabular form. The time spentwith different technology gadgets and its services with the options 1-2 hours,2-4 hours, 4-6 hours and >6 hours. This scale is used to assess the timespent on different technology gadgets and its services of the participants. 3.
TechnologyDependency Questionnaire:Thisquestionnaire was developed by Munduli in 2014. The Technology DependencyQuestionnaire consists of 10 items. Participants have to indicate whether thegiven statements were characteristic or uncharacteristic of them in 5 differentresponses. It assesses the level of dependencyto the technology gadgets of the participants, which shows that higher thelevel of dependency the participant is more vulnerable to addiction oftechnology gadgets.4. Cognitive FlexibilityScaleThe CognitiveFlexibility Scale consisting of 12 items was developed by Martin and Rubin in1995.
Participantshave to indicate whether the given statements were characteristic oruncharacteristic of them in 6 different responses. This scale is used toassess the participant’s level of cognitive flexibility. Thecronbach’s alpha is ?=0.84 which indicates the scale has good internalconsistency reliability.5. MultidimensionalExperiential Avoidance QuestionnaireThe MultidimensionalExperiential Avoidance Questionnaire consisting of 64 items was developed by WakizaGamez in 2011. Participants have to indicate whetherthe given statements were characteristic or uncharacteristic of them in 6different responses. This scale is used to assess the participant’s level of experientialavoidance.
This questionnaire also assess the participants level of totalexperiential avoidance in 6 dimensions behavioural avoidance, distressaversion, procrastination, distraction/suppression, repression/denial and distressendurance. The cronbach’s alpha is ?=0.92 which indicates thescale has good internal consistency reliability.
Procedure: The informedconsent form was given to the participants. The subjects were asked to provideinformation on certain socio demographic details followed by the administrationof the Gadget’s Use scale, Technology Dependency Questionnaire, CognitiveFlexibility Scale and Multidimensional Experiential Avoidance Questionnaire. The instructions abouthow to respond to the tests was explained in detail to the subjects in theirconvenient language (English or in Tamil). The entire administration took up to30 to 45 minutes. RESULTS AND DISCUSSION:Data was coded for IBMSPSS.20 analysis. The frequencies and percentages were calculated for thesocio-demographic variables. In order to test the hypothesis, correlation,t-test was performed to find out the relationship between Technology use anddependence, cognitive flexibility and experiential avoidance.
The results areas follows, Table-1 shows the mean, standarddeviation and t-test of technology dependence, cognitive flexibility andexperiential avoidance among college students Gender N Mean Std. Deviation Std. Error Mean t-test Technology dependence Male 50 35.
42 4.572 .647 -0.500(0.
618) Female 50 35.86 4.223 .597 Cognitive flexibility Male 50 46.
58 6.634 .938 -3.151(0.002) Female 50 51.
02 7.433 1.051 Behavioral Avoidance Male 50 40.56 8.391 1.187 -1.590(0.
115) Female 50 43.02 7.017 .992 Distress Aversion Male 50 46.28 9.
249 1.308 -0.733(.465) Female 50 47.66 9.576 1.
354 Procrastination Male 50 24.42 4.874 .689 -0.785(0.434) Female 50 25.
16 4.546 .643 Distraction/ Suppression Male 50 26.20 5.555 .786 -1.692(.
094) Female 50 28.24 6.470 .915 Repression/ Denial Male 50 45.36 7.899 1.
117 2.449(.016) Female 50 41.
22 8.970 1.269 Distress endurance Male 50 49.96 8.
628 1.220 2.276(.025) Female 50 46.32 7.
308 1.033 Total Experiential Avoidance Male 50 212.64 29.464 4.167 -0.
569(.571) Female 50 215.84 26.700 3.776 Table 1 Show the mean, standard deviation and t-test of technology dependence, cognitive flexibility among college students interms of gender. It shows that there is significantdifference between the cognitive flexibility in terms of gender (t = -3.
151; p= 0.002). However, there exists no significant difference between males andfemales in terms of technology dependence, behavioural avoidance, distressaversion, procrastination, distraction/suppression, repression/ denial,distress endurance and total experiential avoidance.
Surprisingly the research findingsare very much contradicted to the previous research on experiential avoidancesuggested the significant gender difference (Wakiza Gamez, 2011). Table-2 shows relationship between technologydependence, gadgets use, cognitive flexibility and experiential avoidance amongcollege students CF BA DA P D /S R / D D E T E A T D -0.053 0.245 0.344 0.163 0.223 0.
214 0.069 0.340 Mobile -0.008 0.061* 0.119 0.149 0.017* 0.
064 -0.019 0.108* Computer -0.023* -0.008 -0.087 -0.033* -0.044 0.
057 -0.101* -0.027 Tab -0.
001 -0.142* -0.036 0.071 -0.
062* 0.046 -0.144 -0.017* Smart mob 0.
180** 0.075** -0.036 0.126** 0.007** 0.086 0.131** 0.
002** I pad -0.085 -0.046** 0.012 0.
018 -0.027** 0.038 -0.
063 -0.020** Headset -0.014* 0.077* 0.092 0.170* 0.
092* 0.156 0.082* 0.103* Call 0.038* -0.009** 0.
046 0.087* -0.003** 0.062 -0.025* 0.040** Message 0.
028** -0.023** -0.028 0.254** 0.017** -0.
001 -0.072** 0.105** Net 0.012** 0.077** -0.
053 0.204** 0.016** 0.070 0.001** 0.
073** Social net -0.058** 0.056 0.042 0.149** -0.042 0.081 -0.042** 0.
050 Study -0.091 -0.097 -0.076 -0.261 -0.033 -0.063 -0.
026 -0.122 Communication 0.003 0.044 0.056 0.128 -0.018 -0.019 -0.
077 0.039 Entertainment -0.149 0.
214 0.211 0.245 0.114 0.
198 -0.039 0.249 ** Correlationis significant at the 0.01 level * Correlation is significant at the 0.05levelTD = Technology Dependence, CF = CognitiveFlexibility, BA = Behavioural Avoidance, DA = Distress Aversion, D/S = Distraction/Suppression,R/D = Repression/Denial, DE = Distress Endurance & TEA = Total ExperientialAvoidanceTable 2 shows therelationship between the technology gadgets use, cognitive flexibility andexperiential avoidance among college students. It shows that the cognitiveflexibility of the participants is positively correlated with the use of smartmobiles (0.180**), messaging services (0.
028**), internet (0.012**) which issignificant at 0.01 level and call services (0.
038*) which is significant at0.05 level. It also shows that cognitive flexibility is negatively correlatedwith the use of social network (-0.058**) which is significant at 0.01 leveland headset (-0.014*) which is significant at 0.05 level. It also shows that experiential avoidance is positivelycorrelated with the use of smart mobiles (0.
002**), call services (0.040**),message services (0.105**), Internet services (0.
073**) which is significant at0.01 level and mobile (0.108*), headset (0.103*) which is significant at 0.
05 level.It also shows that experiential avoidance is negatively correlated with the useof I-Pad (-0.020*) which is significant at 0.
01 level and tablet (-0.017*)which is significant at 0.05 level. It also shows behavioural avoidance is positivecorrelated with the use of smart mobiles (0.075**), internet services (0.
077**)which is significant at 0.01 level and mobile (0.061*), headset (0.077*) whichis significant at 0.01 level. It also shows that behavioural avoidance isnegatively correlated with the use of I Pad (-0.
046**), Call services(-0.009**), message services (-0.023**) which is significant at 0.
01 level andtablet (-0.142*) which is significant at 0.05 level. It also shows that procrastination is positivelycorrelated with the use of smart mobiles (0.126**), message services (0.
254**),internet services (0.204**), social network (0.149**) which is significant at0.01 level and headset (0.170*), call services (0.
087*) which is significant at0.05 level. It also shows that procrastination is negatively correlated withthe use of personal computers (-0.033*) which is significant at 0.05 level. It also shows that Distraction/Suppression is positivelycorrelated with the use of smart mobiles (0.007**), message services (0.017**),internet services (0.
016**) which is significant at 0.01 level and mobile(0.017*), headset (0.
092*) which is significant at 0.05 level. It also showsthat Distraction/Suppression is negatively correlated with the use of I pad(-0.
027**), call services (-0.003**) which is significant at 0.05 level andtablet (-0.062*) which is significant at 0.
05 level. It also shows that Distress Endurance is positivelycorrelated with the use of smart mobiles (0.131**), internet services (0.001**)which is significant at 0.01 level and headset (0.082*) which is significant at0.
05 level. It also shows that Distress Endurance is negatively correlated withthe use of message services (-0.072**), social network (-0.042**) which issignificant at 0.01 level and personal computer (-0.
101*), call services(-0.072*) which is significant at 0.05 level The table also clearly shows that technology dependencehas no correlation with the cognitive flexibility and experiential avoidanceand it dimensions. However the analyses clearly indicate that the use of thedifferent technology gadgets has a strong relationship with both the cognitiveflexibility and experiential avoidance. Interestingly 2 dimensions (DistressAversion & Repression/Denial) have no relationship with any of thetechnology gadgets. Theliterature also supports the relationship between the experiential avoidance,cognitive flexibility and the technology use (Dong G, Lin X, Zhou H & Lu Q,2014; Garcia Oliva C, Piqueras JA, 2016) Table-3 shows relationship betweencognitive flexibility and experiential avoidance among college students BA DA P D R & D D E T E A CF -0.
024 0.046 0.041 0.
221 -0.344 0.143 -0.101 ** Correlationis significant at the 0.01 level * Correlation is significant at the 0.05levelCF = Cognitive Flexibility, BA = BehaviouralAvoidance, DA = Distress Aversion, D/S = Distraction/Suppression, R/D =Repression/Denial, DE = Distress Endurance & TEA = Total ExperientialAvoidance Table 3 shows the relationship between the cognitiveflexibility and experiential avoidance.
It shows that there is no significantrelationship between the cognitive flexibility and experiential avoidance.However the previous research on the cognitive flexibility and experientialavoidance suggested that lack of experiential avoidance leads to cognitiveinflexibility (Ruiz, 2010). The current research is alsocontradict to the results of the previous study. Conclusion:The present study derived at several conclusions. There existssignificant difference in the cognitive flexibility between males and females.Cognitive flexibility is positively correlated with the use of smart mobiles,calling, messaging, and internet and negatively correlated with the use ofpersonal computer, headset and social network. Experiential avoidance ispositively correlated with the use of personal computer, smart mobiles,headset, calling, messaging and internet and negatively correlated with the useof tablets and I pad/ I phone.
The study also concluded that there exists asignificant relation between the technological gadget use and the cognitiveflexibility and experiential avoidance. So the technology usage is a greatchallenge in recent times.Unfortunatelyin this current era the world is facing a lot of issues on the technologybecause of the inability to categorize the technology use pattern and lack ofthe convincing studies on this area so there is a need to have a deep study. Furtherresearch needs to be done in terms of longitudinal studies, to fully understandthe impact of technology gadgets on college students because they are the more vulnerablegroup to have enormous exposure to technology. Further researches should alsofocus on the intervention to diminish the cognitive function being influencedand disrupted by the technology use and the research should extensively covermuch geographical area and should consider accounting of various othervariables in order to obtain even better understanding about the technologydependence on cognitive flexibility and experiential avoidance.
Thestudy has limitations in such as, the present study was taken within the agegroup of 17 to 22 college students and sample size was small (N=100), also thestudy was conducted only with the students residing in Coimbatore.