Innovation Strategies for Textile Companies in Bangladesh: Development Using Quadrant Analysis Based on a Productivity Index ^†

Abstract

Bangladesh currently occupies a vital place in the textile industry, but it is beset with critical problems, such as insufficient infrastructure, low labor efficiency, and the lack of awareness regarding innovation and political volatility among CEOs. To overcome this crisis, an essential task is to measure the performance of individual companies because of the reality that “what you measure is what you get”. In consideration of these issues, this study developed a research framework on the basis of quadrant analysis and the Malmquist productivity index to capture the overall situation of Bangladeshi textile companies and provide useful information. The framework was then used to establish innovation strategies for such companies. The results of case studies indicated that even though improvements to the internal efficiency of these companies are sufficient, most of them need to innovate technology and policy. Applying the research framework to various fields can advance the formulation of optimized strategies tailored to a given company.

1. Introduction

The textile industry plays a critical role in promoting economic growth in a country where economic development is actively underway [1]. The growth of textile companies can be realized through cost reduction achieved via technological innovation. Bangladesh has recently emerged as a major producer of ready-to-wear apparel in the international market. The country, along with other emerging Asian nations, is expanding its exportation initiatives through growth in the textile industry. Its textile industry was founded 30 years ago, but textile production in the country has recently grown so rapidly that it has achieved close to 84% of the nation’s exports, and was made just 2019 [2]. In 2019, Bangladesh became the second largest RMG (ready-made garment) manufacturing and production base after China, but it is expected to become a larger production. Bangladesh has plans to achieve a $50 billion export income by 2030 [2]).

Despite these achievements, however, the growth rate of Bangladesh had declined to 3.5% in 2020 [2]. The production costs incurred by Bangladeshi textile companies continue to increase due to improvements in employment rights, as well as the low productivity of unskilled workers. Such labor productivity can be improved, and labor production costs can be reduced through education support for unskilled workers, but Bangladeshi textile companies remain inactive investors in human resource development and cultivation. Furthermore, many textile companies in Bangladesh have not yet provided new complex offerings such as outerwear, tailored items, and lingerie. Textile companies offering just T-shirts, trousers, and sweaters continue to dominate the country’s exports. Finally, infrastructure is one of the biggest problems of the textile industry in Bangladesh. Transport, energy, and digitization infrastructure are not enough to develop the textile industry [3].

The above-mentioned problems have compelled Bangladeshi textile companies to apply various process technologies and innovations to resolve low productivity. Meanwhile, enterprises around the world strive to improve corporate productivity through innovation, particularly corporate innovation based on performance evaluation, which considerably advances productivity enhancement. The Malmquist productivity index (MPI) is a tool that can also serve to determine the efficiency of internal processes and the level of technological innovation as well as overall productivity in a company. The MPI is decomposed into two components, namely, IR (innovation of technology) and DR (diffusion of technology into a DMU). IR means the shift of the production frontier, whereas the DR means the movement towards the frontier. The adoption of quadrant analysis based on IR and DR in a management decision-making system helps achieve the efficient allocation of company resources, ultimately improving corporate productivity. The method of this study was developed based on the quadrant analysis of IR and DR, and it was expected to develop innovation strategies based on performance evaluation and based on applying presented research method to textile company of Bangladesh.

Previous studies related to the productivity of textile companies focused on calculating the MPI. In the Bangladeshi context, however, a more important approach is for companies to establish innovation strategies through quadrant analysis of IR and DR. It is necessary to develop strategies based on performance evaluation in order to solve the international environmental standards, labor problems, and energy consumption problems faced by Bangladesh textile companies, and to achieve sustainable development. In the current study, therefore, innovation strategies are derived through quadrant analysis based on the quadrant analysis of MPI. As a result of applying the research methodology presented in this study to textile companies in Bangladesh, it was found that only 10% of textile companies had achieved productivity through continuous technological innovation and internal process improvement. Some 90% of textile companies in Bangladesh needed to develop innovation strategies for technological innovation and internal process improvement. The international community’s need for strengthened environmental regulations and reduced energy and water consumption continues to require innovation from textile companies in Bangladesh. For the sustainable growth of a textile company in Bangladesh, it is necessary to evaluate innovation based on the MPI and establish innovation strategies by using continuous evaluation based on MPI.

The rest of the paper is organized as follows. Section 2 presents research related to the productivity of textile companies, and Section 3 discusses the research framework used to establish innovation strategies. Section 4 describes the results of the application of the framework to a sample of textile companies in Bangladesh.

2. Literature Review

2.1. Research on Textile Companies in Bangladesh

Success factors or barriers have been identified by many researchers. Akter et al. (2022) identified barriers and success factors in order to maintain a satisfactory and comfortable environment in the textile industry [4]. Six barriers in the textile industry of Taiwan were revealed by applying an integrated method combining a fuzzy Delphi method, total interpretive structure modeling, and a matrix of cross-impact multiplications applied to a classification (MICMAC) [5]. Based on digital news magazine articles, corporate communications and interviews with product and innovation managers, success factors were investigated in the textile, clothing, and leather industry [6]. Wadho and Chaudhry (2022) used data from a unique innovation survey of Pakistani textile companies to identify the determinants of product, process and organizational innovation and their impact on company labor productivity [7]. Padilha and Gomes (2022) verified, based on SEM, that innovative culture has a greater impact on processes than products in textile companies, and that size does not affect product and process innovation performance [8].

Most studies on textile companies in Bangladesh revolve around the current status and prospects of the textile industry. Rahman and Raju (2022) revealed that the textile-clothing industry of Bangladesh was doing quite well in terms of supply side, but the demand side has to be improved based on method supply chain management [9]. Shahen (2022) explored the nature of textile workers’ life patterns in the context of the COVID-19 pandemic in Bangladesh [10]. Farhana et al. (2022) compared textile industry exports, specific apparel exports, GDP, and market share over the past 30 years by using cross check analysis [11]. Hossain et al. (2022) examined the impact of stakeholder integration and green investments on environmental sustainability practices as well as the moderating role of green technology adoption in the Bangladeshi textile SMEs [12].

Few quantitative studies have been devoted to the formulation of strategies, and qualitative research has focused on identifying limitations and problematic situations in the industry. Supply chain sustainability risks of the textile industry in Bangladesh were identified and quantified based on the fuzzy synthetic evaluation method. Supply chain sustainability risks were extracted according to the probability of occurrence, the degree of impact, the risk importance of each risk factor group, and the total risk [13]. Ahmed et al. (2022) validated self-conducted investigation of the factors that influence employee performance based on factor analysis of employee in textile company of Bangladesh [14].

Five quantitative examinations specific to textile companies in Bangladesh used information derived from the Global Trade Analysis Project based at Purdue University in the US [15]. A study found that working conditions in the textile sector of Bangladesh are frequently characterized by violations of international standards and codes of conduct by foreign buyers [16]. Such environments frequently have barriers with restrained workspaces, inflicting occupational dangers, such as musculoskeletal issues and contagious diseases. Within the textile industry in the Bangladeshi region, commonly occurring incidents also include injuries, fatalities, disablement, and construction collapses [17].

When unskilled workers are unproductive, they increase the cost of production per unit [18]. In Bangladesh, the productivity of workers is low, accounting for only a quarter of that of their Chinese counterparts. Yet, the CEOs of Bangladeshi textile companies are very reluctant to invest in training and development facilities, despite the fact that training leads to increased productivity [19]. Such companies suffer from crises because of the employment of unskilled workers, the lack of awareness among CEOs, and poor infrastructure.

2.2. MPI Research

The MPI is a useful approach to measuring productivity. Specifically, it is used to measure changes in efficiency over time on the basis of a non-parametric approach and determines differences in the productivity of decision-making units (DMUs) between two periods. Regarding the efficiency of technological innovation, researchers have conducted studies at various levels and angles [20]. The MPI method breaks the limitations of using DEA non-parametric or parametric methods for analysis. Furthermore, MPI is able to determine efficiency for various companies using minimal information. MPI can accurately identify important factors that make it easier for decision makers to establish more goal-oriented strategies [20]. Because innovations have the characteristics of multiple inputs and multiple outputs, they are suitable to analysis by MPI. Furthermore, MPI does not rely on the subjective opinions of experts in setting weights between indicators of analysis. Finally, using the results of MPI, it is possible to suggest measures and target values for improving the efficiency of research activities for each DMU. The MPI consists of two components—one measuring efficiency changes and the other measuring technological innovation. Efficiency changes of internal processes and the efficiency of technology innovation are suitable for the analysis of innovation. It can thus be used to evaluate the efficiency to internal processes as well as technological and policy innovations [21]. In this study, efficiency changes were used as DR and technological changes as IR. The change in internal efficiency, which is the result of innovation, is able to be traced through DR, and relative achievement of technological innovation can be grasped through IR.

The MPI has been widely used to analyze the productivity of organizations in various fields, such as administration, manufacturing, and the service industries. For example, Fulginiti and Perrin (1997) used the MPI to determine whether declines in agricultural productivity can be identified in underdeveloped countries [22]. Chen (2003) used the MPI to inquire into changes in productivity calculations in China’s three major industries—chemical, textile, and metallurgy—during a five-year planning period [23]. Liu and Wang (2008) also used it to calculate three MPI components of some semiconductor packaging and testing companies in Taiwan for the period 2000 to 2003 [24]. Mazumdar and Rajeev (2009) applied the MPI to look into changes in the skills gap ratio, technology efficiency, and productivity of pharmaceutical companies in India [25]. Wang et al. (2011) evaluated intellectual capital management in the Taiwanese pharmaceutical industry by comparing the results of the MPI with those derived using gray relational analysis [26]. Chang et al. (2009) used the MPI to examine productivity changes in accounting firms in the United States right before and immediately after the implementation of the Sarbanes–Oxley Act [27].

2.3. Research on Productivity and Efficiency in Textile Companies

Pat research has used the MPI as a means of calculating the productivity of textile companies. Kapelko and Lansink (2015), for instance, analyzed MPI changes in textile industries worldwide and compared productivity by country during the period 1995 to 2004 [28]. Using a bootstrapped Malmquist approach, the authors extracted the indices of technical change, technical efficiency change, and scale efficiency change. Mai et al. (2020) used the MPI to validate that private companies are the most technologically innovative enterprises and ascertain technological and technical gaps in the Vietnamese garment industry [29]. As can be seen, research on the productivity of textile companies suggested the use of the MPI, but these did not conduct MPI analyses to establish innovation strategies for companies.

Various studies have also been directed toward the productivity of textile industries in India and China. Bhandari and Ray (2012) validated the trend of decreasing productivity under the assumption of a fixed input variable [30], while Dhiman and Sharma (2006) investigated productivity performance and the factors influencing the efficiency of the Indian textile industry [31]. For the Chinese context, Zhang and Wang (2010) derived the productivity and profitability of the textile industry [32], while Lin et al. (2011) used a company-level panel dataset to probe industrial agglomeration in the industry and its effects on company-level productivity [33].

Although various studies have been conducted to present the current status and limitations of the Bangladeshi textile industry, little research has been carried out to develop an index for measuring productivity and suggest innovation strategies. A range of other studies have been carried out to ascertain productivity and the causes of productivity changes in textile companies in India and China, but most have focused on simply presenting a productivity index or examining the cause of a change in the index. It is necessary to establish an innovation strategy through quadrant analysis of IR and DR, not just evaluating corporate performance based on MPI. However, most studies that calculated the productivity of textile companies have been focused on simply deriving the MPI. Therefore, in this study, we propose and apply a method that not only calculates the MPI, but also extracts innovation strategies based on performance evaluation through quadrant analysis of IR and DR.

3. Research Framework

The research framework developed in this work involves two phases (Figure 1): A preliminary stage for data collection prior to analysis and an analysis stage for the examination of productivity.

In phase 1, input and output variables for calculating a productivity index are established and used as bases for collecting data. In this study, appropriate input and output variables were selected via a literature review. This stage also entails selecting DMUs to be analyzed. The DMUs in this work were 41 textile limited companies publicly listed on the Bangladeshi stock exchange. There were more than 41 textile industries listed on the Dhaka Stock Exchange for the period 2013–2018, but only these were subjected to the analysis because they provide data through their annual reports. This research used only those companies which provided whole information regarding the variables of this study, and data from these 41 firms are taken from the statement of financial and non-financial sector analysis published in these companies’ annual report. In addition to this research, further information was taken from the official websites of selected companies. Panel data were collected from these textile companies.

In phase 2, the productivity index was calculated using a Malmquist model. A quadrant analysis is performed using the derived IR (innovation of technology) and DR (diffusion of technology into a DMU), after which a textile company’s innovation strategy was determined from the results of the quadrant analysis. IR represents a change in the frontier line through technological and policy innovation, and DR represents a change in the catch-up line through the improvement of efficiency in internal processes.

3.1. Phase 1: Preliminaries

Given that the MPI is based on inputs and outputs, pecuniary indicators are converted into the two aforementioned variables [34]. Generalizing input and output variables were difficult to ascertain because organizations that are subjected to productivity analysis have different characteristics. The derivation of the MPI is a method of estimating productivity growth and change in measurement targets by year through panel data analysis with only quantitative information on input and output variables. For this purpose, we collected annual data on the 41 textile companies from the Dhaka Stock Exchange.

3.2. Phase 2: Malmquist Analysis

If $x^t$ is the input vector of DMU and $y^t$ is the output vector of DMU and (t) means the period, the output set of the DMU can be represented with the following Equation (1):

$$p^t(X^t)=\left\{Y^t:X^{t}produces{Y}^t\right\}$$

(1)

where the $p^t\left(X^t\right)$ output set of the DMU is assumed to be convex, bounded and closed [35]. Therefore, the distance formula for the output set can be expressed as:

$$D^t\left(x^t,y^t\right)=min\left\{\theta :\left(y^t/\theta \right) \in p^t\left(x^t\right)\right\}$$

(2)

where 𝜃 is the radial element to adjust the position of the output vector. The LP model to draw distance is as follows.

$$(\mathrm{D} (x^{t^{\prime }},y^{t^{\prime }}))=max \{ \mathsf{\lambda }:{{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{Z}}^{\mathrm{t}}{\mathrm{x}}^{\mathrm{tn}}\leqq {\mathrm{x}}^{{\mathrm{t}}^{\prime }\mathrm{n}}\},\mathrm{n}=1\cdots \cdots \mathrm{N}$$

$${{\displaystyle \sum }}_{t=1}^T{z}^t{y}^{tm}\geqq {\mathrm{xy}}^{{\mathrm{t}}^{\prime }\mathrm{m}},\mathrm{m}=1\cdots \cdots \mathrm{M}$$

(3)

$$z^t\leqq 0,\mathrm{t}=1\cdots \cdots \mathrm{t}$$

Here, $z^t$ is defined as the intensity variable.

DR relates to changes in the efficiency of a DMUs internal processes, which increase or decrease the efficiency of the DMU. IR reflects changes in border efficiency through innovations between periods 1 and 2. The MPI represents the change in overall efficiency between periods 1 and 2. It can be expressed as follows:

$$\mathrm{MPI} ({ \mathrm{y}}^{\mathrm{t}},{\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{x}}^{\mathrm{t}+1})={\left[\frac{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\times \frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]}^{1/2}$$

(4)

Equation (4) can be transformed into

$$\mathrm{MPI}=\left[\frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]{\left[\frac{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}\times \frac{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]}^{1/2}$$

(5)

where

$$\mathrm{IR}={\left[\frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]}^{1/2}$$

(6)

$$\mathrm{DR}={\left[\frac{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}\times \frac{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]}^{1/2}$$

(7)

$$\mathrm{Thus}, \mathrm{MPI}=\mathrm{IR}\times \mathrm{DR}$$

(8)

Equation (2) shows the output-oriented Malmquist total productivity index, and the MPI can be decomposed as a unit of IR and DR. Here, D is used as a distance feature by taking a DMU located within the evaluated position to the preferred frontier. In Equation (6), IR ($\frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)} }{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}$) defines the efficiency of internal processes, and in Equation (7), DR ($\frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)} }{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}$) expresses the technology and policy innovation of the DMUs from period t to t + 1 [34]. The MPI, IR, and DR reflect that organizational performance levels in periods t and t + 1 are identical. MPI, IR, and DR values greater than 1 indicate improvement, whereas values less than 1 point to the occurrence of inefficiencies.

Furthermore, IR is defined in Equation (9) thus:

$$\mathrm{IR}=\left[\frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right] =\left[\frac{{\mathrm{D}}_{\mathrm{VRS}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}_{\mathrm{VRS}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]\left[\frac{{\mathrm{D}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\times \frac{{\mathrm{D}}_{\mathrm{VRS}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}{{\mathrm{D}}_{\mathrm{VRS}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}\right]$$

(9)

where ${\mathrm{D}}_{\mathrm{VRS}}$ is the output function of variable returns to scale (VRS). DR is defined in Equation (10) in the following manner:

$$\mathrm{DR}=\left[\frac{{\mathrm{D}}_{\mathrm{VRS}}^{\mathrm{t}+1\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}{{\mathrm{D}}_{\mathrm{VRS}}^{\mathrm{t}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right]$$

(10)

3.3. Phase 2: Quadrant Analysis

The X axis is the IR, whereas the Y axis is the DR. This analytical extraction was based on Barros (2005) [36]. The DR values of the textile companies at times t and t + 1 express whether time t + 1 is further away from or closer to the frontier line than time t, and if the DR value is 1 or larger, this means improvement in the efficiency of the internal processes in textile companies [37]. The DR indicates the degree to which a company approaches the most efficient textile company between t and t + 1 given the accumulation of knowledge, the accumulation of technical know-how, and the improvement of internal working processes. If the IR value is greater than 1, the production frontier is generally higher at time t + 1 than at time t. This means that a larger IR can be produced with the same level of input and that textile companies have made technological advances or policy changes [21].

Through quadrant analysis, the CEO of a textile company can devise innovation strategies for increasing productivity. In Figure 2, the first quadrant is where both DR and IR values are greater than 1. In this quadrant, government-funded research institutes achieve relatively high Mamquist productivity indexes through the continuous innovation and improvement of internal work processes. In the second quadrant, the DR value of government-funded research institutes is greater than 1, but their IR value is less than 1. In this quadrant, government-funded research institutes are typified by improved internal work processes but a declining production frontier. In the case of quadrant 3, government-funded research institutes have DR and IR values that are both less than 1. An urgent requirement for these institutes is to innovate in the dimensions of technology, external policy changes, and internal work processes. Finally, quadrant 4 comprises government-funded research institutes with IR values greater than 1 but DR values less than 1. In these entities, the production frontier rises overall through technological innovation and innovation tailored to external policy changes, but changes in internal work efficiency decrease. It is also necessary for them to improve internal work efficiency through the benchmarking of other government-funded research institutes.

4. Results

4.1. Phase 1: Preliminary Process

Input and output variables with high frequency were derived as input and output variables through a review of previous studies that determined the productivity of textile companies. In such scholarship, input variables were mainly the number of employees; however the amount of capital and operating expenses were also used as input variables; and total turnover, net profit, and gross profit were used as output variables. When variables related to profit are used as output variables, they are compared and justified using operating expenses as input variables because such expenditures also contribute to profit. The textile sector is a production domain that generates tangible goods, and output is most typically measured by many researchers through profit. Therefore, total turnover and net profit have been used to identify the MPI. Input and output variables and related literature are shown in the following

Table 1. Input and output variables.

Variables		Related Literature
Input variables	Number of employees	[29,31,32,38,39,40]
	Capital	[29,31,32,39,40]
	Operating expense	[39,40]
Output variables	Total turnover	[28,29,31]
	Net profit	[31,32,40]

[28,29,31,32,38,39,40].

The division of input variables into five categories capital, labor, energy, material inputs, and expenses are jointly referred to in much past research. Another study used a different set of input variables and the choice of input variables relied upon the characteristics of the industry and the convenience of information collection. They used utilized capital, work, and some other contributions as an intermediary for input factors to quantify productivity of the textile company. Some shreds of evidence supported these sets of input variables being employed in different empirical studies. This study used three input variables; capital, number of employees and operational expenses. Capital and number of employees have likewise been utilized as information factors in past research. The textile industry is the most labor-intensive industry. The major portion of operating expense of textile firms includes the material and labor costs. Due to this, operating expense was used as the third input variable. When “sales” is used as an output variable then it is compared and justified with the operating expenses as an input variable because these expenses also contribute to sales.

The textile industry is a production sector that produces tangible goods. The output is most typically measured by many researchers through variables related to sales. Mai et al. (2020) used the average value of a textile company as an output variable [29] and Kapelko and Lansink (2015) used total turnover and net profit as output variables [40]. In order to extract the MPI of the textile company, output related to sales was mainly used. Therefore, this study also used net profit and total turnover related to sales as output variables.

4.2. Phase 2: Malmquist Analysis

MPI, IR, and DR were extracted using DEAP software. As previously stated, DR is a value expressing whether time t + 1 is further away from or closer to the frontier line than time t. A DR greater than 1 means improvement in internal efficiency. IR indicates whether the production frontier changes between t and t + 1. An IR greater than 1 indicates enhancement through technological progress or innovation [21]. With respect to the MPI, a value greater than 1 reflects productivity improvement, whereas a value less than 1 denotes productivity decline. Changes in the MPI, IR, and DR by year are shown in Figure 3. Changes in the MPI, IR, and DR by textile company are presented in Appendix A.

Until 2017, the IR value was greater than 1, but it also manifested a decreasing trend, whereas the MPI and DR continuously increased. These results indicate that the productivity of the textile industry in Bangladesh improved during this period. This trend is reflected in the improvement of internal efficiency. That is, the productivity improvement of the industry was driven by the enhancement of internal efficiency. However, technological innovation or policy innovation in the Bangladeshi textile industry continued to decline.

4.3. Phase 2: Quadrant Analysis of Textile Companies

The textile companies/DMUs were classified via quadrant analysis based on the mean IR and DR of the MPI. Only 10% of the investigated enterprises increased their productivity through technological and policy innovation, as evidenced by only five companies having an IR average greater than 1. Conversely, 69% of the textile companies increased productivity by improving internal processes, with 28 companies showing a DR value greater than 1. To become a leading textile company in rapidly changing international situations and environmental changes, Bangladeshi enterprises should focus on productivity improvement through technological and policy innovation rather than internal process improvement. The results of the quadrant analysis are shown in Figure 4.

Quadrant 1 contains DMUs with improved IR and DR. The first quadrant comprises four textile companies (DMU 7, DMU 19, DMU 31, DMU 34), representing the best-performing enterprises. These companies should continue to invest in and maintain technological and policy innovation to ensure a high IR value. They should also maintain strategies for continuously managing internal efficiency. Quadrant 2 encompasses 24 textile companies where efficiency enhancements took place in parallel with a decline in technological innovation. These companies should strive to improve IR through the introduction of new technologies and policy innovation. Quadrant 3 is composed of 12 textile companies that had low DR and IR. These companies need to modify their innovation strategies by investing in new technology or techniques to reinforce their organizational skills. They must likewise benchmark companies in quadrant 1 to improve internal process efficiency and enhance productivity by benchmarking internal process innovation and technology-based innovation strategies. Finally, in quadrant 4 we find one textile company characterized by a declining IR and improvements in DR. This enterprise should focus on the efficiency improvement of internal processes. To increase efficiency of internal processes, it is necessary to optimize business processes. Strategies for optimizing internal processes include identifying, analyzing, and refining existing processes. Continuous internal process improvement is an important exercise for this enterprise to increase efficiency.

According to the analysis result, the average IR value of the textile companies has been decreasing for 5 years. The decrease of IR value indicates that the frontier line of technological efficiency has been decreasing because of lack of technology and policy innovation. The average value of DR and MPI has been rising. This means that productivity of most textile companies in Bangladesh has been improving through internal efficiency improvement. Therefore, there is a need to implement policy and technological innovation according to changes in the internal business process and the external environment. To increase low IR, it is necessary to develop advanced fiber materials, establish design centers, establish CAD/CAM centers, introduce advanced test equipment, and execute technical advice. In addition, for increasing MPI, the government needs to expand infrastructure related to the textile industry.

Among textile companies, DMU 29 had the highest average value of DR. DMU 29 is a textile company that had continuously introduced innovations in production lines, such as automation based on machines, reduction of manpower through establishment of production facilities, and redesign of production lines. DMU 29 strengthened worker training and established a process that was specialized for a specific garment. Furthermore, DMU 29 established a logistics system and made efforts to build a horizontal organizational culture. DMU 29 made investments in automation, digitization and productivity improvement, and this company followed international best practices.

Other DMUs should benchmark the 29 DMUs production line redesign for improving DR. The textile company with the highest IR was DMU 31. DMU 31 has continuously carried out technological innovation of R and D policies in accordance with changes in the external environment. In particular, DMU 31 has developed textile production process technologies in line with changes in the world environmental regulations and domestic policy.

According to quadrant analysis, 68% of the companies reported an increase in the efficiency of their internal processes, but the frontier line has been decreasing. These companies need to strengthen policies tailored to changes in the external environment based on technology convergence and technology networking activities. Moreover, 29% of textile companies have both DR and IR below 1. These companies need to urgently change their policy related to internal processes and technological innovation.

In the case of DMU 36, only DR was larger than 1 and IR was smaller than 1. In the case of DMU 36, the amount of R and D investment was larger than that of other companies, and DMU 36 invested relatively more in purchasing licenses of textile machinery equipment. As a result, the internal process efficiency was calculated as low, but the DR value was calculated as relatively high.

5. Conclusions

Critical problems, such as insufficient infrastructure, low labor efficiency, and CEOs’ lack of awareness regarding innovation and political volatility, plague the textile industry in Bangladesh [3]. Overcoming this crisis necessitates measuring the performance of individual companies because of the reality that “what you measure is what you get” [41]. However, research in textile companies in Bangladesh has centered on the limitations or current status of the textile industry, and studies on the MPI have focused on determining productivity indices. To address these deficiencies, we developed a holistic and systematic framework for establishing core strategies on the basis of performance evaluation.

We developed the research framework on the basis of the MPI and quadrant analysis. Specifically, strategies for analyzing textile companies in Bangladesh were formulated with reference to the quadrant analysis directed toward IR and DR. The IR and DR were used because the latter provides beneficial information about the efficiency of internal processes and the former offers holistic information on technology and policy innovation. The case studies uncovered that most textile companies in Bangladesh need to innovate technology and policy even as they achieve sufficient improvements in their internal efficiency. A textile company in Bangladesh is expected to increase productivity by allocating resources efficiently through a strategy drawn according to the research framework. Increasing productivity through the efficient use of limited resources is an important means of overcoming the risks of confronting textile companies in the country. Furthermore, applying the research framework to various fields was expected to advance the establishment of optimized strategies tailored to each company’s situation.

The implications of this study are as follows. Based on a study on the productivity of textile companies in Bangladesh, input and output factors for productivity evaluation were selected. Changes in productivity were analyzed using the MPI of textile companies in Bangladesh, and it was revealed that most textile companies currently have DR and IR problems. A textile company to be benchmarked was chosen, and key elements to be learned and benchmarked were extracted. This study provided a useful research framework for analyzing the company productivity based on MPI. Research frameworks can be used in a variety of fields. Quadrant analysis using DR and IR was shown through actual case analysis, and this can also be used in various fields.

The results of the research are limited by the selected input and output variables and the period examined. If more input and output variables could be collected, the value of MPI might increase. The MPI was affected by the period in which the data and the input and output variables were collected. If a longer period of time and more input and output variables were used, more meaningful strategies for textile companies in Bangladesh could be established. Furthermore, because input variables rise with inflation, productivity can fall, so research that considers inflation is necessary. It is also necessary to analyze the efficiency and productivity of the textile industry in Bangladesh through a comparative analysis with foreign textile industries. Moreover, the subjects of the analysis were limited by available data and the subset of companies. The implications of this study can be further strengthened by comparing textile companies in other countries and including non-listed companies in Bangladesh.