Journal of Management Information and Decision Sciences (Print ISSN: 1524-7252; Online ISSN: 1532-5806)

Research Article: 2023 Vol: 26 Issue: 3S

Influencing Factors of Workers′ Migration from loom Industries to other Professions: Evidence from Sirajganj district of Bangladesh

Md. Monowar Uddin Talukdar, University of Brahmanbaria

Md. Motahar Hossain, University School of Business

Aditya Barman Uzzal, Jatya Kabi Kazi Nazrul Islam University

Md. Sabuj Sarker, Khwaja Yunus Ali University Enayetpur

Most. Arifa Khatun, Khwaja Yunus Ali University Enayetpur

Md. Al amin, Khwaja Yunus Ali University Enayetpur

Md. Shawrat Hayat Sajib, Khwaja Yunus Ali University Enayetpur

Md. Mostafizur Rahman, Khwaja Yunus Ali University Enayetpur

Citation Information: Uddin Talukdar, Md. M., Hossain, Md. M., Barman Uzzal, A., Sarker, Arifa Khatun, M., Al amin, Md., Hayat Sajib, S., & Rahman, M. (2023). Influencing factors of workers’ migration from loom industries to other professions: evidence from sirajganj district of bangladesh. Journal of Management Information and Decision Sciences, 26 (S3), 1-13.


Handloom census 2018 reported 8.42 percent of handloom units established at Sirajganj district of Bangladesh. The study aimed at detecting factors affecting handloom workers’ relocation to other professions. Stratified sampling method has been used to collect data and descriptive statistics have been used to present demographic information. The analysis of the research revealed that coefficient of determination R square for the dependent variable, i.e., the handloom workers migration is 0.512 that suggests the four independent variables named Psychological, Health Hazard, Financial and Non-Financial factors which explain 51.2% of the variation to support the factors affecting the handloom worker’ migration. Additionally the study observed that psychological factors and financial factors were not influencing significantly on the workers’ switching from handloom industry to other profession in the study area. On the other hand, factors concerning health hazard and non-financial aspects were impacting directly towards the handloom workers’ migration in this region.


Handloom Industries, Workers, Migration, Sirajganj, Census.


The handloom industry has been occupied the leading commercial activity in Bangladesh, and it has become country's largest rural industry (Islam et al., 2013).There are more than 0.183 million handloom units, 0.505 million handlooms, and around 1 million handloomweavers, with about half of them being women in this country (Banarjee et al., 2014). In Bangladesh, tiny and cottage enterprises play an important role in reducing poverty by raisingfamily income and generating employment opportunities (Islam et al., 2013). The handloom industry accounts for 48.04 percent of total cottage industry service and 49.46 percent of total cottage industry production in the country (Islam et al., 2013). Textile is one of the most fundamental requirements for human beings. Mills, handlooms, and power looms are the three autonomous sectors of the textile industry, which compete with each other to a substantial extent and jointly supply the country's clothing needs, generate excess for export, and employ a huge number of people. The competence of Bangladeshi weavers, according to an international specialist, is second to none in the world (Rahman, 2013). This industry has a brilliant past, an insecure present, and an uncertain future due to a number of internal as well as external variables operating behind the scene in Bangladesh. The current state of handloom weaving is debatable, and the number of weavers in the business is rapidly dwindling as a result of a variety of inside and outside issues that have a straight impact on the sector (Sharmin & Hossain, 2020). The Bangladesh Bureau of Statistics (BBS) reported the 3rd Handloom Census 2018 from 10 to 14 May 2018 to examine the current position of handloom employees and the overall state of the industry, and the results showed an upsetting picture (Abid Aziz et al., 2013). In the present circumstances, the handloom sector has lost its capability for competition when compared to other industries, particularly considering market share, and in most countries, it has become almost fictitious (C-169, HAL Colony, HAL Post, Balanagar, Hyderabad, India. 2017). Due to scarcity of working capital, about 0.2 million looms have been stopped up. The fixed funds and demand for loan per loom, according to the handloom census of 1990, are Tk.10,008 and Tk.8,904, respectively (Raihan, 2010). The electric power handloom is an alternative to the handloom. In the same way, the weaving business is being updated in stages. Weavers are switching careers due to a lack of training in how to operate modern machinery (Roy, 2017). Between 1990 and 2003, the rate of loss in businesses and employment was predicted to be 3.8 percent and 1.31 percent, respectively. For the years 2003–2013, the comparable rates are expected to be 5.0 percent and 6.80 percent, respectively. Using the abovementioned drop rates, the number of surviving handloom establishments in 2017 would be around 45 thousand, down from 100 thousand in 2003 and 165 thousand in 1990. The handloom sector, on the other hand, has pursued its own path toward power looms. Nonetheless, this industry is projected to remain to the extent that it is required for the production of specialist textiles for which demand will prevail and weavers will be compensated in a manner that is compatible with power looms.

Literature Review

The revisions of some literature concerning four factors impacting workers’ migration from handloom industries to other professions are as under:

Psychological Factors

Workers in non-industrial nations should habitually change from country life, with its tranquil and cozy connections, to the plant climate; from customary reliance on regular cycles in farming and actual work to normalized creation, exact timing, quick result, and energy reliance; and from a setting of recognize activity with the land and harvests to the generic climate of the machine (Kalimo, 1987). Psychological elements are those that affect an individual's psychology and motivate them to lead a good life. Work satisfaction increases both member of staff devotion and the amount of discretionary effort team members are happy to devote in. While persons are unsatisfied with their work, their performance at work, as well as their overall quality of life, decreases. Work development of handloom laborers is energetically influenced by different psychosocial cycles like partner collaboration, accomplishing similarly redundant work, having no chance to get out partaking in a break all through the shift, and twisting around being a basic work.

Health Hazard (Repetitive Stress Injury)

Intensifying, loudness, radiation, and pressure are all physical hazards that can injure an employee without requiring them to contact them. Working people's opinions of stress are associated with a multitude of conditions in the profession and in respective lifestyles (Kalimo, 1987). Comparable to public, political, innovative, and monetary changes, the extent of wellbeing and security at work has bit by bit and consistently expanded. Globalization of the world's economies and its connotations have been viewed as the most momentous variable for change in the work space, and in this manner in the degree of word related security and prosperity, in both good and bad viewpoints (Alli, 2008).

Financial Factor

When employees are in financial trouble, their productivity may suffer as well. To attract and retain capable workers, handloom owners must pay higher wages to workers in areas with a high cost of living. However, under the current scenario, the owner provides minimal salaries to the handloom workers in order to cover their living expenses. In this concern there are some financial factors are identified which are advance payment facilities, limited income opportunity, limited wages, responsibility of accidental faulty production, no way to other financial opportunity (Anumala & Samal, 2017).

Nonfinancial Factor

Non-financial aspects including superiority of service, a company's elasticity, resource operation, and market track were exposed to be important predictors of a service company's productivity. Non- financial factors have long been recognized as impacting important indicators of willingness to achieve targets. For the handloom workers movement, some nonfinancial factors are identified such as: uncertainty of getting work, underestimating of the work, work demands depends upon the willingness of proprietor of handloom, paucity of the availability of work (Liton et al., 2016).

Handloom Worker Migration

Handloom productions are converted to the power loom industry, excess of worker, demand of other work opportunities, availability of other work against handloom occupation, humiliation of the handloom work, hardworking in nature are the main dependent variables of Hand loom workers’ relocation (Parvin et al., 2020).

Objective of the Research

The prime goal of the study is to detect the influential factors of handloom worker migration to the other profession.

Research Methodology

By using structured and semi structured questionnaire, the study acquired information from 107 handloom workers from Sirajganj district of Bangladesh. Out of 107 samples 105 were used to analyze data for unengaged response. Information was gathered through a standardized 26-question survey. Four independent variables are identified to support the dependent variable. Snowball sampling technique has been applied for sampling techniques. The factors determining the worker migration of handloom worker of Bangladesh, Sirajganj district has been selected as the sampling area. The 5-point likert- scale has been used indicating 1 as strong disagreement and representing 5 as strong agreement. Descriptive statistics have been used to demonstrate the demographic profile of the respondent and inferential statistics have been used to establish the factor analysis. For demographic analysis SPSS V16 has been used and Smart PLS V 3.3.3 has been used for determining the influential factors of the conceptual model.

Conceptual Framework and Model Development

Psychological factors, health hazard factors, financial factors and nonfinancial factors are tentatively identified for the reasons of the handloom worker migration to the different jobs for living. Figure 1 shows the conceptual model for justifying the study objectives.

Figure 1 Model of Factors Affecting Workers’ Migration of Handloom Industry

Hypotheses Development

H1: Psychological factors influencing directly on handloom workers’ migration.

H2: Health Hazard has significant impact on handloom workers’ migration.

H3: Financial factors have direct influence on handloom workers’ migration. H4: Nonfinancial factors have positive impact on handloom workers’ migration.

Data Analysis and Interpretation

Demographic Profile of the Respondent

Demographic summary of the respondents is shown in the table 1 below. Demographic profile of the respondent represented based on the collected data from 107 respondent of the hand loom work migrant.

Table 1 Demographic Information of the Migrated Hand-Loom Workers
Demographic Information      
Description Items Number Percentage
  Illiterates 41 43.93%
Education Below PSC 20 18.69%
  JSC 20 18.69%
  SSC 20 18.69%
  HSC 4 4%
  Degree /Honours 2 2%
  21-30 35 32.71%
  31-40 22 20.56%
Age 41-50 18 16.82%
  51-60 15 14.02%
  61-70 7 6.54%
  71-80 3 2.80%
  Below 10 Years 61 57.00%
Experiences Nov-20 28 26.17%
(as a handloom worker ) Above 30 Years 20 18.69%
  Below 10 Years 99 91%
Experiences Nov-20 8 7%
(as other profession ) Above 30 Years 2 2%
  Small Business 39 36.45%
  Van Driver 15 14.03%
  Power loom technician 12 11.21%
Profession (After the migration ofhandloom profession) Student 9 8.41%
  Job 9 8.41%
  Unemployed 7 6.54%
  Agriculture 6 5.60%
  Day Labor 4 3.74%
  Other Professions 6 5.60%

This table above is commonly used to show the demographic profile of the respondent. It can be seen that the majority of handloom employees are illiterates about 43.93%. The age clusterof the respondent between 21 to 30 contains 32.71% from where 57.00% handloom laborers are capable under 10 years the people who are relocated to the handloom occupation and then again, in the wake of moving the handloom work 91% representatives are capable by doing other profession. After the leaving the place of employment, larger part of the relocated hand loom laborers are locked in with the private company around 36.45%.

Discriminant Validity

Fornell Larcker's standard of contrasting the Average Variance Extracted (AVE) value with identical connection values with different factors to assess discriminant validity was utilized in this research. The square base of AVE had a more prominent worth than the comparable relationship with different variables. Table 2 shows the discriminant validity of the parts.

Table 2 Model Validity Measures
Financial Factor 1 1        
Handloom Worker Migration 0.553 0.234 0.743      
Health Hazard Factor 0.796 0.094 0.6 0.892    
Non-Financial Factor 0.535 0.22 0.614 0.494 0.73  
Psychological Factor 1 -0.023 0.231 0.262 0.13 1

The Measurement Model

Construct reliability (CR) and Average Variance Extracted (AVE) have been utilized to depict the markers are sufficiently portrayed. The typical change separated esteem is assessed as a supplemental proportion of develop steadfastness to guarantee that the high worth addresses the expressed markers that are really demonstrative of the build. Financial Factor (CR=1.00, AVE=1.000, Alpha=1.00), Health Hazard Factor (CR=0.881, AVE= 0.553, Alpha=0.838), Handloom worker migration (CR=0.886, AVE=0.796, Alpha=0.744), Nonfinancial Factor (CR=0.775, AVE= 0.535, Alpha=0.572), Psychological Factor (CR=1.000, AVE=1.000, Alpha=1.00). All builds have a Cronbach's Alpha worth moreprominent than 0.50. All variables meet the fundamental develop dependability and normal difference separated degrees of 0.70 and 0.50, individually. The pointer dependability of eachconcentrate thing ought to be somewhere around 0.40. As of now, the concentrate's all's marker dependability things are more than 0.40, demonstrating that our model's mean pointer is precisely addressed Table 3.

Table 3 The Construct Reliability (CR), Cronbach’s Alpha, Multicollinearity, Averagevariance Extracted (AVE) Value
Variables (O) (M) (STDEV) (|O/STDEV|) (IR) (CR) Alpha (AVE)
FF3 <- Financial Factor 1 1 0   1 1 1 1
HHF1 <- Health Hazard Factor 0.906 0.905 0.028 32.671 0.821 00.881 0.838 0.553
HHF3 <- Health Hazard Factor 0.878 0.876 0.033 26.367 0.771      
HLWM2 <- Handloom Worker Migration 00.797 00.798 0.043 18.617 00.635      
HLWM3 <- Handloom Worker Migration 00.727 00.727 0.053 13.711 00.529      
HLWM4 <- Handloom Worker Migration 00.71 00.708 0.068 10.454 0.504 0.886 0.744 0.796
HLWM5 <- Handloom Worker Migration 00.732 00.726 0.08 9.125 00.536      
HLWM6 <- Handloom Worker Migration 00 .726 00.725 0.059 12.245 00.527      
HLWM7 <- Handloom Worker Migration 0.765 0.762 0.053 14.307 0.585      
NFF1 <- Non Financial Factor 0.767 0.749 0.102 7.527 0.588 0.775    
NFF2 <- Non Financial Factor 0.711 0.704 0.081 8.808 0.506
NFF5 <- Non Financial Factor 0.714 0.717 0.079 9.063 0.000
PSYF3 <- Psychological Factor 1.000 1.000 0.000   1.00      

Exploratory Factor Analysis

Exploratory Factor Analysis (EFA) is generally used to analyze the factors of an event of social science and business perception. 105 valid responses are allowed to do the exploratory factor analysis. For examining the Handloom worker migration some independent constructs are identified which are psychological factor, health hazard factor, financial factor and nonfinancial factor. Above factors are used to identify the handloom worker migration where factor analysis technique is used (Soundarapandian, 2002).

Factor-1 (Psychological Factor)

This factor includes four variables such as feelings of work with coworkers, choice of repetitive work, no options for taking rest, repetitive work. To justify the reasons for work migration of handloom worker all of psychological matters are included to this factor.

Factor-2 (Health Hazard Factor)

Basically health hazard factor includes the items which are appeared for the repetitive stress injury. Specifically, breathing problems, hearing problems, bone decay, visionary problems and other feelings of weakness are treated as the health hazard factors. These items represent the health hazard factor.

Factor-3 (Financial factor)

For handloom worker migration some financial factors are identified which are facilities of advanced money, limited earnings, wages measurements, erroneous compensation, and extra gain form work as compensation.

Factor-4 (Nonfinancial Factor)

Different non-financial factors are important to identify the reasons for the work migration of handloom workers. From where we get some items which are work uncertainty, negligence of the society as a handloom worker profession, sustainability of work depends on other factors, work stability and work availability of work.

Factor-5 (Hand-Loom Workers Migration)

There are four independent variables that can be used to examine the hand-loom workers migration which has been affirmed above factors and other items of this factor which have been demand for power loom, existing workers, demandand accessibility of other jobs, inferior complexity during weaving work, unequal payment and reverence.

Common Method Bias Test (HTMT analysis)

There is no normal technique predisposition among factors if the relationship between one element and another is more modest than 0.85. Because of the examination introduced underneath, obviously the worth of some other component's relationship to it is under 0.85. Subsequently, we might presume that there is no normal strategy partiality in this study, as determined by correlation metrics Table 4.

Table 4 HTMT Analysis
Financial Factor          
Handloom Worker Migration 0.255        
Health Hazard Factor 0.108 0.754      
Non-Financial Factor 0.302 0.854 0.743    
Psychological Factor 0.023 0.252 0.305 0.169  

Structural Model Assessment

The underlying model is explored by including way coefficient assessment and change made sense of R2 values. The investigation explicitly estimated the hypothesized model's all's connections by depicting connections freely. Bootstrapping by 5,000 re-sample delivered coefficient and t-statistics (Parvin &Haque, 2017). The route coefficients between dependent and independent constructs are represented by the structural model. Because these four factors regression coefficient are shown in the regression model table table 5, and from the proposed modelit can be identified that two factors which are Health Hazard and Non-Financial Factors havea significant and positive effect on hand-loom workers' migration to different profession at Sirajganj district in Bangladesh at a 5% level of significance (Phan et al., 2021).

Table 5 Regression Weight
Hypothesis Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Comment
H1 Psychological Factor -> Handloom Worker Migration 0.084 0.084 0.080 1.060 0.289 Not Supported
H2 Health HazardFactor -> Handlom Worker Migration 0.373 0.368 0.088 4.257 0.000 Supported
H3 Financial Factor -> Handloom Worker Migration 0.114 0.111 0.069 1.646 0.100 Not Supported
H4 Non-Financial Factor -> Handloom Worker Migration 0.394 0.411 0.086 4.589 0.000 Supported
R- Square Value 0.512
Model Fit SRMR 0.099

That actually intends that assuming proprietor of handloom industry diminished the wellbeing danger factor for the tedious work injury, the movement of the specialists of hand- loom industry limited by 0.373 units and nonfinancial elements are worked on by 0.394 units. The outcomes and speculation testing are introduced in Table 5. Since the t-value is more prominent than 1.96 at the 5% degree of significance, the result show that the hypotheses H2 and H4 are accepted. H1 and H3 were not accepted, on the grounds that the t-value was less than 1.96 at the 5% degree of significance. Health hazard and Nonfinancial variables are fundamentally affected the work movement of the handloom laborers in Sirajganj region (Khan & Momin, 2013).

The coefficient of assurance R square for the dependent variable, i.e., the handloom laborers movement of Shirajganj area in Bangladesh, is 0.512, as displayed in Table 5. This recommends that the four autonomous factors of Psychological, Health Hazard, Financial and Non-Financial factors can clear up 51.2% of the variety for help the elements influencing the purposes behind handloom specialist relocation of Sirajganj area of Bangladesh. The Standardized Root Mean Square Residual (SRMR) is 0.09, which is not exactly the proposed acceptable fit to the information Table 5 (Hu and Bentler, 1998). The fit files demonstratedthat the model was very much fit to the information. Whenever PLS-SEM is important, it precisely predicts marker significant pieces of information. The four free developments of Psychological, Health Hazard, Financial and Non- Financial Factor were undeniably connected to the dependent construct, as displayed in table 5 (hand-loom laborer migration).

Conclusion and Recommendations

The large-scale movement of weavers, the introduction of shed weaving, and high indebtedness among weavers have all contributed to the professional weaver's in this area, according to the case study of Enayetpur Niranjana and Vinayan, n.d. The study's goal is to describe the current state of Bangladesh's handloom weaving industry. The research looked into the various facets and difficulties surrounding the handloom industry to find the reasons for migrating the profession. The industry is facing a lot of problems that have been highlighted through our discussion. This industry faces a number of difficulties such as some of these issues are linked to market failure, while others include inability to satisfy capital cost of production, product diversification, and so on. Our discussion offers some suggestions for getting the handloom sector to the next level of development (Rahman, 2013), by restraining the worker migration as a traditional profession. Creating a central network to transmit critical information to weavers, enhancing weavers' understanding of the financing and sales processes, and establishing handloom institutes in every handloom are just a few of the efforts that will help revitalize the industry and also the government and non-governmental organizations should provide financial, technical, and policy support for the growth of Bangladesh's handloom sector. For this result the sector will save a significant amount of foreign cash that is currently spent on the import of foreign cloth (Raihan, 2010) and these traditional industrial sectors will revive for its own production.


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Received: 17-Feb-2023, Manuscript No. JMIDS-23-13235; Editor assigned: 18-Feb-2023, Pre QC No. JMIDS-23-13235(PQ); Reviewed: 03-Jan-2023, QC No. JMIDS-23- 13235; Revised: 25-Feb-2023, Manuscript No. JMIDS-23-13235(R); Published: 28-Feb-2023

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