Research Article: 2024 Vol: 28 Issue: 2
Role of Self-Help Groups in Knowledge Economy Diffusion for Better Social and Economic Life
Francis Sebastian, Rajagiri Business School
Citation Information: Sebastian., F. (2024). Role of self-help groups in knowledge economy diffusion for better social and economic life. Academy of Marketing Studies Journal, 28(2), 1-13.
Abstract
Self-help groups (SHGs) have an impact on the spread of the knowledge economy and its effects on people's lives and livelihoods. Knowledge creation, diffusion, and application have all become major forces in today's knowledge-based economy. SHGs have garnered fame as a means of developing economic independence and empowering individuals, especially women. This study delves into how SHGs spread the knowledge economy at the neighborhood level and the ensuing alterations to the local economy and society. This study examines the diffusion of knowledge-based economies among active members of SHGs and non-members of SHGs in rural villages in the Alappuzha and Ernakulum districts of Kerala. The results of this research will be beneficial for both researchers and practitioners working in the fields of small and medium-sized businesses (SHGs), the knowledge economy, and international economic growth..
Keywords
Diffusion of innovation, knowledge economy, Education, ICT, Self Help Groups, Quality of Life.
Introduction
Self-help groups are small, volunteer organisations that help each other attain a goal. They are frequently made up of peers who have banded together to help one another satisfy a need, overcome a challenge, or make a desired social or personal change (Katz & Bender, 1976). SHGs bring impoverished and marginalised individuals together to solve an issue. Governments, NGOs, and others use the SHG concept globally. In 1976, Bangladeshi professor Mohammed Yunus began experimenting with microcredit and women's SHGs, which popularised self-help groups. Empowering poor women altered Bangladesh's poverty-eradication efforts (CIRDAP Digest, 2000). By the 1990s, state governments and NGOs saw SHGs as more than financial vehicles. They saw them as a community of like-minded people tackling various concerns. SHGs addressed political and social issues. India's ninth five-year plan recognised the self-help group method's value in grassroots development. Self-help groups give disadvantaged people credit for escaping poverty. The Indian government created the innovative "SELP HELP GROUP" anti-poverty programme. NABARD initiated the Indian SHG movement in 1986–1987. In the mid-1990s, Kerala organised urban poor women into local groups (NHGs). NABARD considers these NHGs SHGs for SHG-bank linkage and loan facilities (George, 2004). Rural women's self-help groups empower women to escape predatory moneylenders, lowering rural poverty. These groups facilitate community development engagement.
Self-help groups are small, volunteer organisations that help each other attain a goal. They are frequently made up of peers who have banded together to help one another satisfy a need, overcome a challenge, or make a desired social or personal change (Katz & Bender, 1976). SHGs bring impoverished and marginalised individuals together to solve an issue. Governments, NGOs, and others use the SHG concept globally. In 1976, Bangladeshi professor Mohammed Yunus began experimenting with microcredit and women's SHGs, which popularised self-help groups. Empowering poor women altered Bangladesh's poverty-eradication efforts (CIRDAP Digest, 2000). By the 1990s, state governments and NGOs saw SHGs as more than financial vehicles. They saw them as a community of like-minded people tackling various concerns. SHGs addressed political and social issues. India's ninth five-year plan recognised the self-help group method's value in grassroots development. Self-help groups give disadvantaged people credit for escaping poverty. The Indian government created the innovative "SELP HELP GROUP" anti-poverty programme. NABARD initiated the Indian SHG movement in 1986–1987. In the mid-1990s, Kerala organised urban poor women into local groups (NHGs). NABARD considers these NHGs SHGs for SHG-bank linkage and loan facilities (George, 2004). Rural women's self-help groups empower women to escape predatory moneylenders, lowering rural poverty. These groups facilitate community development engagement. (NRLM) of the Indian federal and state governments is expected to reach over sixty million village-poor families (NRLM, 2020). Indian NGOs are running large-scale SHG programmes on women's empowerment, gender, sociocultural inequities, good health, and accessibility and accountability of government welfare programmes. SHGs also use organisation (group) loans, thrift, and savings, developing skills, and collective or individual exploration of local resources to satisfy their livelihood needs (Ghosh & Bose, 2017; Sharma et al., 2012). Several of these approaches are aimed at making the economic system more accessible to ordinary people (Chatterjee, et al. 2018). SHGs are the best at women's empowerment, rural development, and income sharing. SHGs are important for local economic development, especially for women's groups (Swain & Wallentin, 2012; Khatibi & Indira, 2011). Rathod and Devi (2018) say SHGs are changing the economy. SHGs strengthen local group development initiatives and raise standards across geographic and social strata within the global development agenda, particularly the SDGs (Anand et al., 2020). SHGs are vital to small rural development, even though they need government and non-governmental aid (Kaushik & Singh, 2010). SHG intervention is more popular in developing countries like India since it empowers individuals politically and economically and improves their social lives (Geethanjali & Prabhakar, 2013; Brody et al., 2017). Involvement also boosts psychological strength, economic judgement, networking, and social regard (Brody et al., 2017).
Digitization is a global buzzword. Knowledge-based economies are transforming the world. "Knowledge" remains vague despite many studies and conversations. Smith, K.H. (2002) states that knowledge is undefined. In 1962, economist Fritz Machlup coined the term "knowledge economy." Knowledge is learned, developed, transmitted, and efficiently employed to advance the knowledge economy. Knowledge, together with information and communication technology, generates innovation, creativity, and new ideas, which create income, growth, employment, and human capital (Cavusoglua, 2016). Knowledge may be crucial to a nation's competitiveness.
Drucker (2012) described a knowledge society as the creation, sharing, and use of information to improve society. Production relies on knowledge, along with land, labour, and capital. Powell and Snellman (2004) define the knowledge economy as high-intensity activities that accelerate technological and scientific advancement. Techniques, inventive thinking, scientific understanding, and advanced technological skills help knowledge-based economies create, acquire, and share knowledge (Sum & Jessop 2012). In the 21st century, a knowledge-based economy is inevitable. The world's largest economies are driven by technology and growth. Knowledge investment is supposed to accelerate socio-economic progress by maximising knowledge creation and utilisation. Building and managing knowledge infrastructure and increasing knowledge workers' productivity through the development, expansion, and use of new information are crucial to this knowledge society's success. Mutula (2004). Economic incentives and an institutional regime, a skilled and educated workforce, a successful innovation system, and adequate information infrastructure are the four pillars of the knowledge economy, according to Chen and Dahlman (2005).
Theoretical Background and Hypothesis Development
The research focused on education, the knowledge economy, and information technology to empower women. In her book Women's Empowerment in South Asia, Sudha defined female empowerment as the ability of women to convert socio-economic development by empowering them to effectively engage in matters affecting them through training courses, tutoring, consultancy services, and the provision of facilitating instruments for women to lead in their communities, areas, and countries. Insufficient knowledge and work skills, related to social inhibition of women in societies dominated by men that establish unbreakable barriers to gender equality, suffocating women's power to enhance, lead to poverty within women, compelling them to accept low pay despite limited possibilities for employment (Adeosum and Owolabi, 2021; Gallo-Cruz, 2021). As in many countries, poverty disproportionately affects women in India. 2003 (Besley and Burgess). Players who perceive women's major responsibility as housework restrict women's efforts to create a small enterprise (Madichie and Nkamnehe, 2010; Shoma, 2019). In addition, women's involvement in SHGs helps all members directly, improves their personal economic well-being, and creates jobs for other women (Srinivas, 2015, as quoted by Khan & MBhat, 2022). Thus, women's lives have improved (Mathur and Agarwal, 2017). SHG membership enhances women's living standards, economic position, and decision-making autonomy, according to Kumar et al. (2021). According to Tripathi (2006), society should help mobilise grassroots people to participate in the transformation to a knowledge-based society by analysing the impacts of ICT on community cultures, social values, and economic potential. Information systems and national development were examined by Mangla (2003). Pick & Azari (2008) helped identify barriers to implementing initiatives that could accelerate a country's growth. While developing and upgrading information systems, he stressed the need to use current channels and consider local demands. He convincingly explains why many rising countries now see "informatization" as the way to prosperity. Webster (2014)'s description of the information society moved beyond technological and economic aspects to describe it as a society. Martin (2017) studied the major patterns and interconnections between information, information, and communication technologies and the global society and economy, as well as the trajectory of information-based transformation. Zelazny (2015) examined how the information society and the knowledge economy relate.
Internet and telecommunications technologies can improve communication, involvement, and information dissemination in rural areas (Narula and Arora, 2010). Technology allows women to launch businesses, according to Sassen (2002). Adopting ICT can help create a welcoming environment for women. Society has changed significantly, according to Carrasco and Vanderkast (1998). Information science, computers, and ICT are change agents. Globalisation, the information society, its elements, and many definitions, as well as the information professional's role, are thoroughly discussed.
Nurunnabi (2017) lists six features of the knowledge economy: human resources, innovative thinking, ICT, the economic system, schooling, and work chances. The author stressed the significance of diversifying the economy through information-based economies and building a knowledge-economy framework for long-term growth and development. V.S. Tchamyou's (2017) African study indicated that knowledge-based economies reduce unemployment and boost global competitiveness. The knowledge-based economy improves business globally. Policies Dubina, et al. (2012), In the information economy, innovation and virtualization are important, but there are other differences. One of these traits is seeing information as a production ingredient. A successful knowledge-economy transformation requires ICT infrastructure modernization, creative capability development, and education investment. Metcalfe (2009) suggested that ICT impacts are described in terms of information and knowledge, talents and skills, and human-built structures to illustrate the critical role of technical processes in stimulating economic growth.
After World War II, the phrase "quality of life" (QOL) was first used in the US to describe how material riches affected people's lives, but it has since been expanded to include schooling, health and welfare, industrial and economic growth, and the defence of the "free world." The socio-medical literature uses concepts like satisfaction with life, self-esteem, well-being, pleasure, the meaning and purpose of life, functional capacity, and adjustment to describe the quality of life (A.J. Carr et al., 1996). Borthwick-Duffy (1992) defined QOL as (a) the calibre of one's living standards, (b) their satisfaction with them, and (c) a combination of living standards and happiness. Realising one's values, aspirations, and desires through one's abilities or lifestyle is another definition of QOL (Emerson, 1985); (Ventegodt et al., 2003) define QOL as a lovely life and feel it is the same as a high-quality life. The QOL idea states that living a great life is based on a few realistic norms of conduct relevant to our culture. Quality of life measurement, which often includes information about living conditions, dwelling characteristics, and availability of basic needs, gives in-depth awareness and insight into poverty's dynamic aspects (Mamun, et al. 2012).
Knowledge is Enhanced through ICT
The acronym ICT stands for information, communication, and technology. Knowledge is defined as information, and technology is defined as the application of computer and communication technology. ICT is the technology that is used for information management and helps with communication (Elston, 2007). The electronic dissemination of information is what information technology (ICT) is defined as. Industry and business change quickly as a result of ICT adoption (Giotopoulos et al., 2017). Communication networks, the Internet, and intranets are examples of how people use ICT to communicate and integrate knowledge. In collaborative contexts, ICT supports the combination of explicit knowledge most effectively (Nonaka & Konno, 1998). According to previous research, information technology (IT) is a critical component of knowledge generation (Davenport & Prusak, 1998). It enables the previously impossible rapid collection, storage, and exchange of knowledge, aiding the knowledge creation process (Roberts, 2000).
H1: ICT has a positive impact on knowledge.
Knowledge is Enhanced by the Skill
An agent's skilled action demonstrates knowledge that demonstrates the agent's mastery of the relevant activity. Guidance is the relationship between the knowledge that manifests a skill and the action that is taken as a result of that knowledge. The term "guidance" refers to a set of instructions. Any skilled action is guided by knowledge that demonstrates mastery of the activity. (Stanley & Williamson, 2016) Skilled action is guided by propositional information, particularly propositional knowledge that reveals an agent's expertise (Ryle, 2009). Knowledge is defined in terms of skill in reductive virtue epistemology (Turri, 2015; Kelp, 2013; Pritchard, 2012; Kelp, 2011; Greco, 2010; Battaly, 2008; Zagzebski, 1996; Sosa, 2007; as cited in Pavese, 2016). Sosa, for example, regards knowledge as apt performance, which is identified as one’s skill-based success (Sosa, 2015; Sosa, 2010; Sosa, 2007; Sosa, 1995). In many cases, it appears that a skillful action necessitates knowledge, much like a skilled nurse necessitates a wealth of medical knowledge (Evans 1994; Glenn W. Donnelly, 2006). Based on these examples, it's tempting to conclude that competent action is knowledge-directed activity, placing knowledge above skills in the explanatory hierarchy (Stanley, 2011).
H2: Skilling has a positive impact on knowledge
Knowledge is Improved via Education and Training
Education provides a powerful lens through which to analyse knowledge formation and the inclusion or exclusion of experiences. It contributes to women's empowerment by helping them develop a good self-image, self-confidence, and critical thinking skills (Lakshmi Narayana and Rajesh 2002, cited by Das, Ashim, et al. (2020)); Wills (1994) defines training as the transmission of measurable and defined knowledge or skills. Training is defined as "any formal or informal activity or course that helps acquire the knowledge and skills necessary to complete the task" (McLeod and King, 1996). It is recommended that training be reinforced by actual, hands-on experience. It is also reported that the majority of workers have trouble following spoken instructions (Hughey & Mussnug, 1997). They need to take a more proactive role in their education. Education, on the other hand, is often seen as a more general, less specialised, or practical method of knowledge expansion (Jennifer, 2011). It has also been observed that education aids an individual's ability to think critically and make rational decisions (Chinnadurai 2005, as cited in Das, K. 2017). It improves a person's communication skills, which are useful for communicating with the outside world. As a result, in a nutshell, it improves an individual's quality. Swain and Varghese (2009) found that longer self-hhelp group membership and non-governmental organisation training were both linked to the creation of new assets. People's attitudes change as a result of education, but training is an important procedure for acquiring information, changing attitudes, improving skills, and fostering participant confidence. It boosts their self-esteem and abilities, as well as their ability to communicate the needed knowledge to peers and clients (Meena M. and Singh K.M., 2013).
H3: Education and Training have a positive impact on knowledge
Knowledge Improves the Quality of Life (QOL)
Knowledge helps identify and recognise those traits, advise or instruct others on how to develop and integrate them, and explain how and how much they benefit. The three basic factors of quality of life that were examined are health, education, and the environment (Evans, 1994). Several other researchers, on the other hand, preferred to compile a list of all variables connected to QOL, including social, economic, ecological, environmental, national, health, educational, technical, institutional, and security-related factors (Liu, 1980; Day, 1987, as cited in Amato & Amato, 2002).
H4: Knowledge is positively related to Quality of Life (QOL).
Knowledge is Positively Correlated with Employment
In Drucker’s (2012) view knowledge has become the primary means of production , surpassing money, raw materials, and labor.This shift towards the information age is currently underway. As the economy becomes more knowledge- based , the value of highly talented individual continues to rise (Michaels et al., 2001). By raising educational standards to international levels of excellence, nations can contribute a significant share of the global supply of well-paying jobs that require high skills (Brown & Lauder, 1996, 2001).
In 1996, Brown and Lauder conducted a study on the relationship between economic growth and education in the modern world. They emphasized the crucial point that the government's current responsibility lies in investing in education and training to ensure that workers possess the necessary employable skills, rather than guaranteeing full employment. According to theories on human capital and status attainment, employers seek individuals who possess the ability to recite, compose, perform basic arithmetic, communicate effectively, negotiate, resolve conflicts, conduct research, organize information, and generate innovative ideas (Becker, 1964; Blau and Duncan, 1967; Hyman, Treiman, and Terrell, 1975; Wright and Reed, 1975; Spaeth, 1976; Sewell and Hauser, 1975). These talents, knowledge, and abilities are acquired through schooling Figure 1.
Figure 1 Conceptual Model
Source: Literature Review.
*H1, H2, H3, H4, H5 are hypotheses.
H5: Knowledge is positively related to employment.
Research Methodology
The research endeavor was to comprehend the dissemination of a knowledge-based economy within the realm of self-help groups (SHGs), encompassing both their members and nonmembers. Our study focused on exploring the intricate interplay between information and communication technology (ICT), skill development, education, and training and their impact on knowledge acquisition. Simultaneously, we delved into the reciprocal relationship between knowledge and its effects on the overall quality of life and employment opportunities. To ensure the credibility and validity of our survey instrument, we embarked on a preliminary pilot research phase involving the meticulous design of a comprehensive questionnaire. It is worth noting that, in the realm of quantitative research, pilot studies generally deem samples ranging from 10 to 30 statistically significant (Johanson & Brooks, 2010). Consequently, we sought the valuable expertise of two distinguished professors and one assistant professor to validate the questionnaire's design. Subsequently, we distributed the revised copy of the questionnaire to 15 members of SHGs, meticulously addressing any concerns related to readability and linguistic nuances. This rigorous process was thoughtfully devised to ascertain the high content validity of our questionnaires (Rubio et al., 2003). Upon completion of these vital steps, we proceeded to disseminate the finalized questionnaires to our intended audience, seeking their valuable insights and participation in our research endeavor.
Data Gathering, Participants and Criteria
A convenience survey was conducted, and 203 participants participated in it. The research was conducted between December 2018 and December 2019. The participants in the study are all women microentrepreneurs from rural Kerala, both members and non-members of self-help groups. The sample respondents were chosen using a multi-stage, simple random process. The study's population consisted of all female micro-entrepreneurs from rural Kerala, separated into members and non-members of SHGs and NHGs. Two districts (Alappuzha and Ernakulum) were chosen from the entire state of Kerala. In total, 15 SHGs were chosen from each district, with parameters such as the year of incorporation, the group size, the amount of savings and loans, the number of income-generating activities (IGAs) completed, and the amount of thrift per member taken into account. A total of 150 women from SHGs were picked. It was hoped to get responses from every member of the 30 SHGs that were picked. One or two respondents were eliminated in extremely rare cases where members could not be reached despite multiple attempts. As a result, there were a total of 200 respondents, including 101 from Alappuzha and 102 from Ernakulum. Three replies were omitted from the study because they were incomplete. As a result, the sample size was 200, with 50 respondents from each of the two districts. To compare the impact of a knowledge-based economy on the active members of SHG and nonmembers of SHG, data were also collected from 100 non-members (friends and neighbors of SHG members) from the two districts.
Statistical Analysis
A quantitative research approach using an organized questionnaire to obtain information and attitudes is used to follow a descriptive and explanatory research design. According to Hair et al. (2010), a value of 150 is adequate for measuring less than seven constructs and small commonalities when using structural equation modeling (SEM). The questionnaire set was examined using SE modeling (SEM). SE modeling is the study's second generation of multivariate analysis (Awang P., 2015). This strategy is widely used by academics and researchers to analyze data collected through questionnaires. The use of AMOS2 programs to customize this SEM method is ideal since the analysis performed will yield more precise results (Barbara, 2013).
Results
Internal Consistency and Validity
We created Cronbach's values for each construct to assess the internal consistency of our measurement items. Table 1 shows the standard loading estimate or factor loading values. All other things were deemed to fulfill the criteria, except for one item of information and communication technology and one item of education and training. It can be seen from Table 1 that the loading of these items was> 0.500. To assess the association of the scale elements within the construct items, the composite reliability (CR) and average variance extracted (AVE) were also calculated.
| Table 1 Result of Factor Loading, Cronbach`S Alpha | ||
| Constructs | Factor Loading | Cronbach`s alpha |
| Information Communication Technology | ||
| ICT2 | 0.496 | |
| ICT4 | 0.996 | |
| Skilling | ||
| SK1 | 0.811 | |
| Sk2 | 0.692 | |
| Sk3 | 0.601 | |
| Education and Training | ||
| ET1 | 0.873 | |
| ET2 | 0.617 | |
| ET3 | 0.766 | |
| ET4 | 0.493 | |
| Knowledge | ||
| KG1 | 0.576 | |
| KG2 | 0.861 | |
| KG3 | 0.854 | |
| Quality of Life | ||
| QOL2 | 0.638 | |
| QOL4 | 0.893 | |
| Employment | ||
| Emp1 | 0.849 | |
| Emp2 | 0.742 | |
| Emp3 | 0.689 | |
The squaring of the AVE for each component is larger than the correlations between them. (Hair et al., 2010) This proves the existence of discriminant validity. All items had loadings of factors, average variance extracted, and composite reliability scores above 0.50 and 0.70, respectively. However, due to low factor loading, one ‘motivation to learn' component and two 'training efficacy' elements were removed. These results corroborate the validity of the conceptions and the trustworthiness of each and every item (Netemeyer, Bearden, & Sharma, 2003). The evidence is shown below Table 2.
| Table 2 Values for Composite Reliability & Average Variance Extracted | |||||||||
| SL | CR | AVE | 1 | 2 | 3 | 4 | 5 | 6 | |
| 1 | Quality of Life |
0.992 | 0.992 | 0.776 | |||||
| 2 | ICT | 0.747 | 0.502 | 0.499 | 0.996 | ||||
| 3 | Skilling | 0.788 | 0.501 | 0.579 | 0.538 | 0.76 | |||
| 4 | Education and Training | 0.814 | 0.601 | 0.576 | 0.583 | 0.635 | 0.759 | ||
| 5 | Knowledge | 0.747 | 0.602 | 0.529 | 0.581 | 0.519 | 0.017 | 0.727 | |
| 6 | Employment | 0.806 | 0.582 | 0.6 | 0.603 | 0.676 | 0.021 | 0.646 | 0.763 |
Source: Author’s computation.
Diagonal Values
Square root of the average variance extracted is represented by bold values on the diagonal (AVE).
Confirmatory Factor Analysis
The primary objective of performing a confirmatory factor analysis (CFA) is to assess the extent to which the proposed models align with the observed data. This evaluation involves comparing various indices generated by the analysis with predetermined threshold values. threshold values. The measurement model employed in this study adheres to a standardized framework and exhibits a relative chi-square (CMIN/DF) value of 1.599, which falls within an acceptable range. The relative chi-square, also known as the CMIN/DF ratio, provides a normalized measure of the chi-square fit index divided by the degrees of freedom. Additionally, we consider the Comparative Fit Index (CFI), which ranges from 0 to 1. In our case, the CFI score of 0.972 indicates a strong fit, as it is close to 1 and signifies a robust model fit. Moreover, we evaluate the incremental fit index (IFI), which should ideally be equal to or greater than 0.90 to establish model acceptance. In this study, the IFI score is 0.972, surpassing the threshold and confirming the suitability of the model. Another crucial index is the Tucker-Lewis Index (TLI), also referred to as the non-normal fit index. TLI takes into account the complexity of the model. With a TLI score of 0.957, close to 1, we observe a favorable match between the model and the data. Lastly, we assess the Root Mean Square Error of Approximation (RMSEA), a measure that quantifies the extent of discrepancy between the model and the observed data. According to Hu and Bentler (1999), an RMSEA value of 0.06 or lower is indicative of a good model fit. In our study, the RMSEA value of 0.05 surpasses this criterion, suggesting an even better fit Table 3.
| Table 3 Values of Goodness of Fit | |||||||||
| Model | X2 | df | X2/df | GFI | RFI | RMSEA | CFI | IFI | NFI |
| Range | - | - | <3.00 | >0.8 | >0.80 | <0.08 | >0.9 | >0.9 | >0.9 |
| Base | 316.60 | 198 | 1.599 | 0.925 | 0.929 | 0.972 | 0.957 | 0.972 | 0.055 |
SE Modelling
SE modeling analysis was carried out by utilizing Amos 21 to test the study's hypotheses. The model's fit to the data was evaluated using goodness-of-fit measures. The chi-square minimum to degree-of-freedom (CMlN/DF) ratio as per computation is 3.160, which is significantly lower than the cut-off level of 5.00 (Hair and Hampson, 2006), indicating that the proposed model and the data are well-matched. Furthermore, values greater than 0.9 for the goodness-of-fit index (GFI = 0.977), incremental fit index (IFI = 0.989), comparative fit index (CFI = 0.989), and normed fit index (NFI = 0.985) indicate a satisfactory fit. The root mean square error of approximation (RMSEA), which is less than 0.1, was estimated to be 0.073 (Hair and Hampson, 2006).
Hypothesis Testing and its Effects
The sample size of the SHG member user group was 100. The standardized coefficients of ICT and education and training for SHG members are.034 and -0.180, respectively, while P > 0.050; hence, it was found to be insignificant. It implies that Hypothesis H1 for SHG members was found to be insignificant. The impact of skilling (=.980, p = 0.00) on knowledge is significant and positive. The effect of knowledge on quality of life and employment ( = 0.512, p = 0.00) and ( = 1.188, p = 0.00) is significant and positive. It means that skilling positively impacts knowledge, and knowledge impacts the quality of life and employment of the self-help group members. Therefore, hypotheses H2, H4, and H5 for SHG members were found significant (Table 4).
| Table 4 Values for Model Goodness of Fit | |||||||||
| Model | X2 | df | X2/ df | GFI | RFI | RMSEA | CFI | IFI | NFI |
| Range | - | - | <3.00 | >0.8 | >0.80 | <0.08 | >0.9 | >0.9 | >0.9 |
| Base | 625.68 | 198 | 3.16 | 0.977 | 0.923 | 0.073 | 0.989 | 0.989 | 0.985 |
The group of non-self-help group members' user sample is 100. The standardized beta coefficient of ICT is 0.361 and p = 0.00; hence, it’s found to be significant (H1 for non-self-group members is accepted). The impact of skilling (=0.271, p=0.00) and education and training (=0.256, p= 0.00) on knowledge is significant and positive. The impact of knowledge on quality of life (=0.329, p=0.007) and employment (= 1.188, p = 0.00) was discovered to be significant and positive. Therefore, hypotheses H1, H2, H3, H4, and H5 for non-self-help group members were found to be significant (See Table 5). It means that ICT, skilling, education, and training positively impact knowledge, and in return, knowledge improves one’s quality of life and employment.
| Table 5 Summary of Hypothesis Testing | ||||
| Hypothesis | Hypothesis Path | Path coefficient | p-Value | Final Remarks |
| H1 | KW←ICT | 0.212 | 0.000 | Accepted |
| H2 | KW←SKILL | 0.481 | 0.000 | Accepted |
| H3 | KW←E&T | 0.259 | 0.000 | Accepted |
| H4 | QOL←KW | 0.579 | 0.000 | Accepted |
| H5 | EMP←KW | 0.977 | 0.000 | Accepted |
Discussion
The model is accepted since all indices show good scores. All hypotheses were proven to be accepted for general fit. The group-wise analysis of hypotheses shows unlatching results. The hypotheses testing for the SHG member group revealed that Hypothesis 1 was not accepted, which means there is no positive association between ICT and knowledge. A possible explanation points to a digital divide. This could also be associated with Vygotsky’s (2012) theory of the zone of proximal development (ZPD), which says that a teacher has to find out the level of the student’s knowledge and understanding in a certain area. The majority of the SHG members were women, and they might have to depend on their community and circle of sources for more knowledge acquisition than ICT. Another explanation could be gender differences in language when using ICT; the obstacles to the usage of ICT are generally structural, i.e., time, location, and literacy, and not personal (Best & Maier, 2007). Another finding is that the hypothesis that education and training positively impact knowledge has been rejected. A possible reason could be a lack of seriousness towards training among the members due to the overtraining syndrome (OTS) (Kreher & Schwartz (2012). The other reason may be the influence of trainee attitude (Noe, & Schmit, 1986).
The suggestions that are incorporated in the study area are that, primarily to accelerate the rate of empowerment, future SHG strategies must place a higher emphasis on member training and capacity building, as well as proper connection assistance; secondly, training women in management and marketing skills is one of the great challenges; thirdly, to achieve cohesiveness and seamless operation of activities such as saving, lending, and gatherings, SHGs require good leadership and regular visits by promoters; and finally, the SHG members may not all be of the same caliber of expertise. NGOs can recognize underperforming members of a group and provide them with appropriate training to help them become competent. Short-term training sessions at the panchayat level can be organized for this purpose. They must be educated to excel in language proficiency. Corporates can provide skill-based activities to SHGs as part of their CSR (corporate social responsibility) activities. Through the ED cells and incubator cells, educational institutions must provide training programs and appropriate services to SHG enterprises. R&D functions will be assisted by technical institutions. B-schools must make available their faculty resources for training and practical assistance in all areas of business management. Students in technical and business management should be encouraged to focus their research projects on the requirements and difficulties of SHGs, as this will provide them with hands-on experience that will prepare them to start their businesses in the future.
A sample survey was used to conduct the study. The sample analysis observations are applied to the entire population. As a result, the study has all of the limitations of a sample survey versus a census survey. Even though the study was conducted at the state level, data was only collected from two districts in Kerala. However, every precaution was taken to verify the sample, data, and analysis. Therefore, the results of the study can be confidently extrapolated to the general populace. Women in Kerala who run small businesses were polled for this study; they were classified as either SHG members or non-members. To further this investigation, we can expand our focus to all 14 of Kerala's taluks.
Conclusion
The study's objective was to investigate the effects of the diffusion of a knowledge-based economy on both SHG members and non-members. The study concludes that self-help and non-self-help groups have a positive influence on the diffusion of a knowledge-based economy. Thus, the products are marketed more efficiently in a variety of ways. The implications of this study are expected to contribute to the existing literature on SHGs, knowledge-based economies, and development. By uncovering the mechanisms through which SHGs diffuse knowledge and transform lives, this research will provide insights for policymakers, practitioners, and development organizations to effectively harness the potential of SHGs as vehicles for knowledge-driven development. Ultimately, the study aims to shed light on the transformative power of SHGs in creating more inclusive, equitable, and prosperous societies through the diffusion of the knowledge economy.
Funding
There were no funds available to support the research.
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Received: 19-Sep-2023, Manuscript No. AMSJ-23-14023; Editor assigned: 20-Sep-2023, PreQC No. AMSJ-23-14023(PQ); Reviewed: 30-Oct-2023, QC No. AMSJ-23-14023; Revised: 29-Dec-2023, Manuscript No. AMSJ-23-14023(R); Published: 19-Jan-2024