Perception of Labor Flexibility in an Organization in Central Mexico Download PDF

Journal Name : SunText Review of Neuroscience & Psychology

DOI : 10.51737/2766-4503.2020.018

Article Type : Research Article

Authors : Cruz García Lirios

Keywords : Flexibility; Climate; Leadership; Turnover; Salary

Abstract

The objective of this work has been to explore the dimensions of labor flexibility to establish the dependency relationships between its indicators. An exploratory, cross-sectional and correlational study was carried out with a sample selection of 100 employees from an organization in central Mexico. The flexibility is indicated by the turnover, the wage casual and lack of benefits. Lines of research on the incidence of leadership styles on the variables in question are recommended.


Introduction

As of this writing, the SARS CoV-2 pandemic and the Covid-19 disease have killed three million and infected about 20 million. In Mexico, about 120 thousand have lost their lives and more than 2 million have been infected (WHO, 2020) [1]. In this panorama of health and economic crisis, the policies of containment and mitigation of the pandemic have been implemented as strategies of confinement and social distancing, causing a labor crisis which is reflected in labor flexibility such as remote work (PAHO 2020) [2].

Labor flexibility is indicated by 1) legislation adjusted to unemployment , subsidies and vacancies informal ; 2) precarious conditions in the matter of selection, training and education reflected in the reduction of wages; 3) unfair competition and monopoly led to an increase in demands and a decrease in the quality of processes and products; 4) export strategies sponsored by the state and specialized labor willing to work long hours with low income; 5) energy crisis and its effect on the maquiladora industry, mainly the automotive industry, which encouraged mass production without quality control; 6) the contraction of public investment and massive unemployment that meant the impoverishment of jobs; 7) the proliferation of power groups as a barrier to entrepreneurship and innovation that promoted mass production without quality; 8) State intervention that aggravated compensation for illnesses and accidents, as well as unemployment insurance that caused the entry of unskilled labor; 9) public policies that generated poverty, pollution and agglomerations of workers in a single industrial sector; 10) negotiation, pacts and corporate, union and state agreements associated with corruption, inequality, sabotage, strikes, boycotts, confrontations [3].

Therefore, the objective of this work is to explore the structure of labor flexibility from the perception of employees who kept their jobs even when they have been confined and distanced to carry out their work. Are there significant differences between the theoretical dimensions of labor flexibility reported in the literature with respect to the observations in the present study?

The premise that answers the question lies in assuming that significant differences prevail between the structure of labor flexibility reported in the literature with respect to the structure of perceived flexibility in the surveyed sample [4]. This is so because the state of the question has been oriented towards the description and explanation of dimensions related to the work environment without considering the perception of workers regarding the same work environment, demand and resources that the organization where they work for Carreon [5].


Theory of Labor Flexibility

Labor flexibility reveals a change from state management to a personalized selection of labor, the replacement of machinery operated by groups and collectives with automated technologies that not only allowed to extend the working day, but also transformed it into continuous and permanent, opening the possibility of employment of unskilled and poorly paid personnel, which is why it is subcontracted for a short period [6].    

In this way, the competition between the organizations that replaced the parastatals and the emergence of companies in areas of specialization and innovation after the technological revolution, mainly informational, has generated a labor demand for jobs that is estimated at 40% not existed for five years [7].

If we consider that the occupational changes due n to different dimensions, then it will be possible to see a future scenario in which occupations are presented in accordance with economic, social, labor, educational, scientific and technological structures [8].

The prospect of labor flexibility, understood as a probable scenario to produce knowledge in accordance with technological development purposes applied to the quality of processes and products, carries some expected consequences for the 2020-2060 period [9]. It is a scenario in which migration would reach a limit shared with the level of productivity that began to be observed since 2015, but whose antecedents go back to 1950 when the Welfare State was consolidated and the business crisis, innovation, productivity and competitiveness [10].

Regarding the educational consequences, mainly in terms of occupational specialization, the trend observed in 2010 not only prevails for the economically active population, but also worsens in 2030 when estimating the asymmetries between employment difficulties [11]. In other words, an increase in occupational skills corresponds to a reduction in work primary. In this sense, those who do not have a high level of specialization and updating of knowledge are close to unemployment, although people with postgraduate degrees do not guarantee formal employment.        

Opportunities and capacities, from the logic of occupational flexibility, are factors of gender equity. As of the year 2030, not only is an occupational parity between men and women expected, but it is also assumed that the degree of education-training will allow an equitable distribution of leadership to be observed [10]. The indicators related to the level of migratory selectivity, education and training will see equity scenarios in the period from 2020 to 2040, but asymmetric after this period given the level of competitiveness and technological dependence of the organizations [12].

Modeling of the Variables of Labor Flexibility

The specification of a dependency relationship model consists of the design of the incidence trajectories between the variables related to labor flexibility on performance [13]. In this sense, the literature warns that the reduction of flexibility in the field of occupational health involves the incidence of variables such as quality of life, subjective well-being, work culture and organizational climate - empathy, trust, entrepreneurship, innovation, productivity and competitiveness.

However, the literature also notes the influence of stress -depersonalization, exhaustion, frustration- on well-being and the cultural-organizational climate. Thus, the resilience emerges as a personal, group and organizational response to the threats and risks posed by the implementation of the flexibility work in organizations and institutions [14].

In the case of educational and health institutions, the stress associated with resilience generates absorption, dedication and dynamization [15]. These are three factors that distinguish individuals, groups and organizations that not only develop resilience, but also generate opportunities and skills linked to occupational satisfaction [16].


Method

Design: A cross-sectional, exploratory and correlational study was carried out.

Sample: A non-random selection of 100 managers of micro, small and medium enterprises was carried out in central Mexico. 67% are women and the remaining 33% are men. 32% completed their baccalaureate studies, 41% completed their bachelor's degree and the remaining 27% had postgraduate studies. 45% declared that they had income of less than 3,500 0 pesos per month (M = 3412 0 SD = 23 8 .14), 41% mentioned that their income ranged between 3,500 0 and 7,000 0 pesos per month (M = 5813 7 SD = 113 0 .24) and the remaining 14% acknowledged that their income exceeded 7 0 000 pesos per month (M = 8124 0 SD = 2348.56). 42% are married, 24% are single and the remaining 34% are in common law.

Instrument: The Labor Flexibility Scale was used, assuming that the items in the literature could be adjusted to the study context. Provided that they have been tested in samples such as those in the study, as well as the inclusion of response options that imply intervals of significance in the responses of each item.

In the case of job flexibility, respondents' intentions were weighted against informality and staff turnover. This is the case of the reactive "If there were unemployment, they would take turns to have a job opportunity." Each item corresponds to one of the five response options: 0 = not likely, 1 = very unlikely, 2 = unlikely, 3 = somewhat likely, and 4 = very likely.

Procedure: The surveys were conducted online: www.atn.es.tl prior information that the results of the study would not negatively or positively affect their employment situation. In addition, the anonymity and confidentiality of the data was guaranteed in writing.  

Analysis: The information was processed in the Statistical Package for Social Science. Is the alpha parameter estimated Cronbach for interpreting the consistency internal of the instrument, the statistical adaptation and sphericity Bartlett and Kaiser Meyer Olkin. To establish the factorial solution, as well as the factorial weights and the percentage of variance explained in an exploratory factor analysis of the main axes with promax rotation [17]. The validity of the instrument, which supposes a construct that emerges in different contexts and samples [18]. Finally, the correlation parameter was calculated to establish the probable trajectories s relationships put forward factors [19].


Results

The overall internal consistency of the instrument (alpha of 0.889) exceeds the minimum required (alpha of 0.80). This means that the Labor Flexibility Scale can be applied in different contexts and samples, yielding results like those of the present study (see Table 1).

Table 1: Instrument descriptions. 

R

M

SD

A

F1

F2

F3

F4

F5

F6

r1

4.14

1,469

0.885

 

 

 

 

 

0.601

r2

4.53

1,429

0.884

 

 

 

 

 

0.675

r3

4.65

1,639

0.882

 

 

 

 

 

0.683

r4

4.23

1,178

0.881

 

 

 

 

0.549

 

r5

4.48

1,303

0.882

 

 

 

 

0.673

 

r6

4.85

1,108

0.881

 

 

 

 

0.693

 

r7

4.40

1,483

0.881

 

 

 

0.671

 

 

r8

4.85

1,993

0.888

 

 

 

0.543

 

 

r9

4.57

1,141

0.881

 

 

 

0.581

 

 

r10

4.11

1,204

0.882

 

 

0.612

 

 

 

r11

4.57

1,289

0.889

 

 

0.567

 

 

 

r12

4.94

1,055

0.888

 

 

0.673

 

 

 

r13

4.57

1,910

0.885

 

0.541

 

 

 

 

r14

4.74

1,100

0.888

 

0.543

 

 

 

 

r15

4.52

1,798

0.892

 

0.654

 

 

 

 

r16

4.47

1,472

0.883

0.623

 

 

 

 

 

r17

4.40

1,599

0.884

0.635

 

 

 

 

 

r18

4.32

1,126

0.886

0.625

 

 

 

 

 

Source: Prepared with the study data; M = Mean, SD = Standard Deviation, A = Alpha removed the value of the item. Extraction method: main axes, Rotation: Promax. Suitability and sphericity ?X = 1864.322 (300gl) p = 0.000; KMO = 0,857? F1 = Leadership (0878 and 29878 alpha% of the total variance explained) , F2 = C OMPENSATION (0.870 and 7.973 alpha% of the total variance explained), F3 = Structuration (0.894 and 7.471 alpha% the total variance explained, F4 = Conditions (alpha 0.892 and 5.84% of the total variance explained) , F5 = Contingency (0.782 and 4.996 alpha d% of the total variance explained) , F6 = Risk (alpha of 0.746 and 4.559% of the total variance explained.) All items include five response options: 0 = not likely, 1 = very unlikely, 2 = unlikely, 3 = somewhat likely, and 4 = very likely.


In fact, if a minimum requirement of 0.70 and a maximum of 0.90 is assumed as the exclusion criterion, then none of the items would be exclude. The prerequisite for estimating the validity of the instrument is the adequacy and sphericity of the scale, understood as tests that establish the volume of partial correlations and the absence or presence of a factor identity. Low correlations circumscribed to one entity suggest that analyzes are not recommended to establish dimensions or factors. Thus, the adequacy and sphericity ??2 = 1864.322 (300gl) p = 0.000; KMO = 0.857? suggests estimating the factors recommended by the theory.          

In the case of validity, understood as the efficiency with which an instrument or scale measures what it intends to measure, from a confirmatory factor analysis of principal components with promax rotation it was possible to observe six factors configured by the five theoretical dimensions, a despite the fact that: The first factor predominantly included the theoretical dimension of the relationship with the boss (explaining 29.878% of the total variance). The second factor included the theoretical dimension of compensation (which explains 7.973% of the total variance). The third factor included the organizational structure (explaining 7.471% of the total variance). The fourth factor on working conditions (which explains 5,584% of the total variance). The fifth factor included s contingencies the organizational environment (0.782 and 4.996% alpha of the total variance explained). The sixth factor refers to the risks of the organization's environment (alpha of 0.772 and 4.559% of the total explained varies).                  

Based on the reliability and validity analyzes, it is recommended to adjust the observed factors to the theoretical dimensions, suppressing those items that are dispersed or reconceptualising the dimensions. This would allow the contrast of model reflection of the organizational climate, taking into account the theoretical dimensions and the empirical factors             

Furthermore, the correlation matrix shows that there are positive and significant relationships between the five factors, evidencing the possibility of a reflective structure of the organizational climate as a second-order factor (see Table 2). 

Table 2: Correlations and covariations between the factors. 

 

M

S

N

F1

F2

F3

F4

F5

F6

F1

F2

F3

F4

F5

F6

F1

29.7756

5,05445

205

1,000

 

 

 

 

 

1,896

 

 

 

 

 

F2

22,4078

4.25856

206

0.799 **

1,000

 

 

 

 

0.564

1,897

 

 

 

 

F3

17.8349

2,83280

212

0.832 **

0.744 **

1,000

 

 

 

0.671

0.672

1,804

 

 

 

F4

20.3173

3,29475

208

0.690 **

0.837 **

0.657 **

1,000

 

 

0.694

0.604

0.547

1,875

 

 

F5

17.4340

2.76237

212

0.688 **

0.602 **

0.614 **

0.638

1,000

 

0.546

0.674

0.673

0.604

1,673

 

F6

16.28 21  

2.1923 1  

219

0.561 **  

0.506 **  

0.423 **  

0.332

0.4035

1.00

0.587

0.654

0.593

0.651

0.579

1,782

Source: Prepared with the study data; M = Mean each factor, standard = Deviation of each factor, N = N umber of observations in each factor, F1 = Leadership , F2 = Compensation, F3 = Structuration, F4 = Conditions , F5 = Contingency , F6 = Risk. * p <0.01; ** p <0.001; *** p <0.0001            

Table 3: Dependency relationships between the factors and labor flexibility.

Model

Hypothesis

Trajectory

?

P

R

2  

adjusted    

1 A

Labor flexibility è Leadership  

0.693

0.009 

0.693

0.409

0.303

 

1 B

Labor flexibility è Compensation  

0.644

0.000

0.634

0.488

0.384

 

1 C

Labor flexibility è Structuring  

0.642

0.000

0.603

0.453

0.334

 

1D

Labor flexibility è Conditions  

0.570

0.260

0.550

0.323

0.207

 

1E

Labor flexibility è Contingencies  

0.542

0.000

0.542

0.395

0.291

 

1F

Labor flexibility è Risks  

0.592

0.000

0.592

0.342

0.238

II 

2A

Leadership è Compensation

0.470

0.000

0.491

0.241

0.121

 

2B

Leadership è Structuring

0.452

0.320

0.419

0.270

0.150

 

2 C

Leadership è Conditions

0.412

0.202

0.401

0.240

0.115

 

2D

Leadership è Contingencies

0.331

0.002

0.383

0.143

0.084

 

2E

Leadership è Risks

0.332

0.067

0.357

0.184

0.072

III 

3A

Compensation è Structuring

0.321

0.002

0.356

0.194

0.084

 

3B

Compensation è Conditions

0.204

0.000

0.232

0.003

0.005

 

3C

Compensation è Contingencies

0.201

0.000

0.212

0.001

0.001

 

3D

Compensation è Risks

0.234

0.000

0.234

0.002

0.009

IV

4A

Structuring è Conditions

0.134

0.127

0.134

0.023

0.020

 

4B

Structuring è Contingencies

0.189

0.135

0.165

0.024

0.021

 

4C

Structuring è Risks

0.135

0.189

0.118

0.029

0.022

V

5A

Conditions è Contingencies

0.089

0.093

0.083

0.006

0.005

 

5B

Conditions è Risks

0.073

0.032

0.094

0.008

0.006

SAW

6A

Contingencies è Risks

0.061

0.541

0.025

0.004

0.003

Source: Prepared with the data of the study; ? = Parameter of the dependency relationship between a dependent variable and an independent one, both in relation to other determining variables. Significance = Degree error allocation dependency ratio, R = Statistic or the dependency relationship, R = Statistic of the dependence to the square, R adjusted = Statistic of the dependence to the squared and adjusted, which reflects the total variance explained for each model.                   

 

The adequacy and sphericity ?X2 = 789.577 (10gl) p = 0.000; KMO = 0.833? suggests performing second order factor analysis. The second-order factor or labor flexibility included each of the six factors, explaining 76.690% of the total variance, which suggests the contrast of the model based on five reflective factors in which the relationship with the boss would be the predominant factor.

It is possible to observe that the organizational climate, as a second order factor formed by the relationship with the boss, compensation, structure, compensation and motivation are determinants of labor flexibility as a second order factor indicated by isolation, overload, complicity and consultations (? = .634, p = .000; R = 0.634, R2 = 0.402, R2 jd = 0.399).              

Regarding the rest of dependency relationships, low values tend to be spurious and not significant relationships. Once the six first-order factors and their linear relationships had been established, their structure was observed to establish the reflective trajectories of labor flexibility. 

Finally, the adjustment parameters ??2 = 5.552 (2gl) p = 0.062; GFI = 0.974; NFI = 0.964; IFI = 0.977; CFI = 0.972; RMSEA = 0.229 ? shows the fit of the theoretical structure with respect to the weighted observations.           


Discussion

Regarding the theoretical, conceptual and empirical frameworks, where leadership highlighted as a factor of labor flexibility, establishing two - way communication and intrinsic motivation as indicative levels of external demands and internal resources, the weighting of balances the time to establish relationships, tasks, supports and innovations in terms of conditions, rotations, salaries, rewards and benefits in situations that are increasingly subject to the market. The present work, rather, proposes that leadership is an intangible capital in terms of skills, knowledge and experiences, which will also motivate staff to the point that a climate of relationships will coexist with a rotation of functions and decrease of wages in unemployment situations.                 

However, the non-experimental and exploratory type of study, as well as the non-probabilistic and rather intentional type of sample selection, limit the results of the study to the surveyed sample. It is recommended to carry out an experimental study with probabilistic selection to be able to contrast the hypotheses in a context and sample different from the present work.         

Regarding the studies that warn of market contingencies as indirect determinants of the work environment and the performance of organizations. In other words, to the extent that business development and microfinance policies encourage productivity, leaders are committed to carrying out strategies that, due to their degree of improvisation, imply unidirectional communication and motivation in remuneration that allows them to be at living up to the demands. From the market. In such a scenario, labor flexibility is a mediating factor of economic, productive and employment policies, but leadership prevails, with flexibility being a distinctive feature of the environment rather than of the organization or groups working within them.         

However, the influence of leadership in involves training a working environment that can be oriented labor flexibility with other types of work, goals, innovations and supports, which are a traditional leadership or guidance of employees or subordinates, while motivating the talent and intellectual capital. If the work environment is the result of local and simultaneously determines policies a type of casual performance and rotation, then organizations in establishing a scenario of trust and expectation could n influence the formation professional.

It is necessary to carry out the contrast of the labor flexibility model in groups of mycro, small and medium-sized companies in order to establish the organizational determinants and their influence on the similarities and differences between MSMEs when weighing their performance, commitment and satisfaction. This design could also be extended to gender, age, income and marital status groups to elucidate the profiles that would fit the informal and austere working conditions.    


Conclusion

The contribution of this work to the state of the art lies in the establishment of the reliability and validity of an instrument that measures work climate and flexibility, but the type of design and selection of the sample imitate the findings in the study sample. The statistical properties of the instrument indicate that flexibility is multidimensional since, it seems to be a mediator of the policies of local impulse on the opportunities and informal labor capacities. This reflects a validity of context that the instrument in question could develop more in samples and scenarios different from that of the study. Furthermore, in relation to other variables such as leadership, the instrument can be extended to incorporate leadership as a determinant of labor flexibility.    


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