Influencing Factors of Employee Readiness to Adopt Advanced Manufacturing Technology (AMT) on Apparel Shop Floor in Sri Lanka

A Compared with other manufacturing industries, apparel manufacturing can be classified as the most labor-intensive industry, which needs a man behind every machine. The use of advanced manufacturing technologies in the apparel shop floor is far behind compared to other industries. The researcher believes that one reason could be the lack of readiness of employees to adopt new technologies. This paper aims to provide empirical evidence on the influencing factors on employee readiness to adopt advanced manufacturing technology on the apparel shop floor, aiming for the Sri Lankan apparel industry. Following a critical review of literature, five factors, perceived usefulness, attitude, perceived ease of use, perceived management support, and techno-optimism, were tested with the data collected through a quantitative survey conducted among 118 employees using a questionnaire. The results revealed that all five factors correlate with employee readiness in various degrees. Findings suggest that apparel organisations may need to improve employee readiness before adopting advanced technologies on the shop floor to bring more success in technology adoption. The literature addressing technology adoption in apparel shop floor is scarce, and this study contributes to that gap. Further research is recommended to contribute to knowledge and find solutions to enhance technological capabilities to bring a competitive advantage to the apparel industry.


INTRODUCTION
Developing countries have gained a significant market share in apparel exports during the past three decades. As per the International Labour Organisation, this amounts to almost three-quarters of the world apparel needs (Anon 2005). As per the Export Development Board of Sri Lanka (EDB 2020) sources, the apparel industry's contribution to the Sri Lankan economy is 40% of the total annual export revenue. Also, it provides a 50% share of the total industrial export revenue, which has been growing over the past three decades. The apparel industry brings foreign exchange and creates 300,000 direct and 600,00 indirect employment opportunities contributing immensely to Sri Lanka's job market (Wijendra 2014;Ranjith & Widner 2011).

Challenges in Brief
After removing the MultiFibre Agreement (MFA) in 2005, the industry became highly competitive and challenging. After the quota removal, countries like China, India, and Bangladesh took advantage of skilled labour, raw material resources, and low-cost production (Haber 2004;Latha et al. 2015). The Sri Lankan apparel industry's biggest challenge is high manufacturing costs compared to the other neighbouring countries. High cost imposes immense pressure on business continuity, and making profits has become highly challenging.
Out of overall manufacturing cost, approximately around 15%-20% incurred from labour (Kelegama & Epaarachchi 2004;Dheerasinghe 2009). The apparel industry is still highly labour intensive and probably the largest single industry with a man behind every machine. Varukolu & Park-Poaps (2009) highlights that the apparel industry has a substantial amount of manual and production-oriented processes. Therefore, maintaining highperformance levels to bring the cost down should be a key strategy and focus on becoming competitive.
There is inevitably an intense pressure to increase productivity, high quality, speed (Ungan 2005;Premkumar et al. 1997), agility, compliance and ethical standards to reduce overall cost and be more competitive and profitable.

Use of Technology for Productivity Increase
Since the end of the 20th century, we witness the explosive growth of digital technologies revolutionising human society and the world. Like other areas, technology impact on the economies is enormous. Snow, Øystein, & Arthur (2017) describes that the global economy is being transformed by digital technology. Also, the CA Magazine explains how the industrial economy is being transformed into a digital economy (Negroponte 1995).

Advanced Manufacturing Technology (AMT)
Due to the advancement of information technology, the AMT domain covers soft and complex technologies and refers fundamentally to the technologies being used in industrial manufacturing (Kumar et al. 2018). AMTs are broken into three main categories by Boyer & Pagell (2000) design, administrative, and manufacturing.
In this dynamic and multi-dimensional competitive environment, AMT enables the strategic capability for the organisation to operate at high levels of performance (Zhang et al. 2006). It can be used as a strategic weapon (Singh et al. 2007) for critical long-term success (Boyle 2006;Welch & Nayak 1992;Hayes et al. 1989). To bring competitiveness, AMT can operate in two ways (Singh et al. 2007). Firstly, by creating more efficient and agile processes which can alter the cost structure. Secondly, it can bring in capabilities to enhance product design, quality, service, and lead time (Vinas et al. 2001).
Ample evidence can be found through many empirical studies to show that AMT has a high capability to provide a vast range of tangible and intangible benefits to an organisation. A few factors include cost, inventory, product quality, and flexibility (Kumar et al. 2018;Singh et al. 2007;Calantone et al. 2012).
With the increased pressure of high labour cost, apparel manufacturers are forced to adopt digital technologies as one of their main strategies to increase productivity while maintaining the quality in high volumes, in a shorter cycle time, and at low cost (Ranjith & Widner 2011;Haber 2004).

Research Problem and Questions
Abeyrathna et al. (2015), in their research, Technology Adaptation of Apparel Industry in Sri Lanka: An Observational Survey, discuss at length the factors and issues of technology adoption by apparel manufactures in Sri Lanka using three significant apparel manufacturing groups. Abeyrathna et al. (2015) explain, though the strategic initiative, Management drive and capital is there, yet the technology adoption rates are meagre. As a practitioner in the field, this researcher also testifies to Abeyrathna et al. (2015) findings. The findings raise a question though the organisations are ready, whether the employees are ready to adopt advanced technologies, and what factors influence their readiness to technology adoption, which is the research problem of this study.
To the best of the researcher's knowledge, no empirical study has been conducted to explore the employee readiness to embrace advanced manufacturing technology on the apparel shop floor. Therefore, the present study identifies factors that influence employee readiness for AMT adoption on the apparel shop floor. Secondly, the study attempts to understand the relationship between identified factors and employee readiness for successful adoption of AMT on the apparel shop floor. Following research, questions are formed.  1993;Miller et al. 1994;Holt 2002;Jones et al. 2005;Riddell & Røisland 2017). According to Rafferty & Simons (2001), in a change initiative of an organisation, readiness is a predecessor to a person's behaviour.
Some other authors defined readiness for change as the degree of preparation (Huy 1999), state of an individual (Madsen et al. 2006), and degree of involvement, motivation and technical capability (Holt & Vardaman 2013) of executing an organisational change. Parasuraman (2000) defines technology readiness as the inclined nature to use and adopt new technology to achieve personal and worklife goals.
As we observe in much of the literature, it is evident that readiness is not the availability of resources but a cognitive concept (Backer 1995), which involves beliefs and perceptions of the proposed change. Therefore, as Riddell & Røisland (2017) describes, readiness consists of several factors, including individual intent, beliefs, attitudes, processes, context, outcomes, and stakeholders of the intended change.
However, individual cognitions are not the only factors for readiness to change (Backer 1995). There are technological, social, and other organisational factors that influence readiness to change (Riddell & Røisland 2017).

Change Management Theoretical Aspect of Readiness for Adoption
AMT involves technology adoption by employees, for which they must go through specific changes in their behaviour. Technology adoption has been widely studied as an essential subcategory of change management. Riddell & Røisland (2017), in their Master's theses, mention that Readiness is novel in the change management domain and literature.
Change awareness within the organisation and 'unfreeze' to make the employees ready for the intended change is often mentioned as change readiness (Riddell & Røisland 2017;Armenakis & Bedeian 1999).
Researchers have developed other models using Lewin's three-step model despite many criticisms, but the core is Lewin's model (Riddell & Røisland 2017;Bakari 2017). The table below compares Schein (2010) and Armenakis & Bedeian (1999) models with Lewin's three-step model.  (Remington 2001). This substantial coalition increases perceived collective efficacy and organisation membership readiness towards the change initiative.
The readiness level to accept the change will increase based on the intensity and clarity of employees' long-term personal benefits due to the successful change and the perceived outcome. Porras & Robertson (1992) explains that perceived outcome has a link with the change vision.
Many researchers highlight the importance of communicating the vision for employee readiness for change (Schweiger & Denisi 1991;Miller et al. 1994;Wanberg & Banas 2000). However, managers do not understand the importance and seriousness of consistent and continuous communication required for a successful change initiative (Pollack & Pollack 2014). It is vital to repeatedly communicate the message in different channels to attract the employees' hearts and minds (Backer, 1995). The effectiveness of this communication results in the employee wanting to change and the desire to make it happen soon (Jung 1966), which enhances the positive attitudes towards the anticipated change (Armenakis et al. 1993;Lines 2016).
Lewin's and Kotter's models mainly focus on the change initiative's process and execution, giving less emphasis on the individual experience (Galli 2018). Hiatt's ADKAR Model (Hiatt 2013) emphasises the employees and how they experience the change but have limitations in the process and execution of significant change initiatives (Galli 2018). ADKAR model discusses five goals. Like Lewin's first phase, 'unfreeze', and Kotter's first four phases of readiness, Awareness and Desire goals in the ADKAR model discuss employee readiness for the desired change.
Awareness is announcing the change initiative to employees and project teams. The challenge is understanding the depth and level of change required (Galli 2018). Galli (2018) explains that the desire is essential for the employees to have positive attitudes and support change actions.

Theories and Models of Technology Adoption and Readiness
Rogers did not precisely examine human choice behaviour when introducing the Innovation Diffusion theory half a century ago (Sun 2016). Rogers (2003) explained, when an individual or group of individuals perceived an idea or practice as new even though it was invented a long time ago, it is an innovation (Dearing 2009). Communicating the innovation between the social group's members through clear channels is called Diffusion (Dearing 2009). Innovation adoption and attributes are critical concepts in this theory that are important for this research study.
There are five stages in the innovationdecision process. The employee needs to increase motivation to reduce the uncertainty of benefits and disadvantages of the desired change (Dearing 2009).
The process starts with the knowledge phase, where employees start to gain an understanding of the innovation, asking questions 'What?', 'How?', and 'Why?'. Rogers (2003) categorised this knowledge into three: awareness, how, and principle knowledge. Understanding the existence of the innovation, which is awareness, will help motivate the employee and gain knowledge on other two aspects, which eventually leads to the adoption of the change (Dearing 2009). 'How' and 'principle' knowledge explains how to use the technology and why the innovation works. These two types of knowledge enhance the employee's readiness to adopt new technology (Spotts 1999), as it increased ease of use, which eventually increased perceived self-efficacy (Bandura 2001). Rogers (2003) says that influencing technology adoption will be more effective if the employee gains more understanding in the knowledge stage. Rogers (2003) explains the innovationdiffusion process as reducing uncertainty and proposing five attributes to support this. He further explains that these attributes are crucial to predict an individual's favourable adoption rate (Dearing 2009). The individual perceived value given to each of these behaviours will decide the readiness of the employee to adopt specific technology (Dearing 2009).
The first attribute, 'relative advantage,' explains the employee's perception that the specific innovation is superior to any other (Rogers 2003), which refers to perceived usefulness. Second attribute compatibility, although conceptually different from the first one, is similar to it. These attributes refer to how much the innovation is perceived as valuable and compatible with the experience, current values, and needs of the innovation adaptors (Dearing 2009). Adaptors needs will negatively result in innovation usage if the innovation is not compatible. Complexity is the third attribute, which refers to the degree of the perceived difficulty of understanding and using the innovation. Complexity can be referred to as user-friendliness or perceived ease of use of the technology. Rogers (2003) says, this can negatively impact the readiness and adoption of innovation. The degree of the experiment can be done if innovation is referred to as 'triability' by Rogers (2003), which is the fourth attribute and has a positive correlation to adoption, where the adoption is faster if the innovation is experienced more times which leads to the self-efficacy of employees (Rogers 2003). The final and the fifth attribute is observability and refers to the extent of the outcome seen by others. Peer observation is a key for the adoption, which positively correlates to the rate of adoption. Successful implementation of technology will continuously motivate others and increase collective efficacy.
Task Technology Fit (TTF) theory is extensively used (Goodhue & Thompson 1995) to measure the performance and impacts of system usage. To increase performance and make a positive impact on a given task, the technology must be fit for the task (Lu 2014). The theory explains a positive correlation between technology utilization and technology fit, influenced by task and technology characteristics (Lu 2014).  Thompson (1995) For this study, the crucial fragments of this model are task and technology characteristics. Task characteristics refer to the degree of technology fit to a specific task attributed to perceived usefulness.
Technology characteristics can be attributed to perceived ease of use, and past research has confirmed the same (Mathieson & Keil 1998).
Venkatesh and Davis, after further research, found that both perceived usefulness and ease of use have a direct influence on the behavioural commitment of the actual use of the new technology (Lai 2017). They came up with their final model of TAM in 1996, eliminating attitude towards use concept (Venkatesh & Davis 1996).  (2017) Perceived usefulness is defined as the belief that the new technology can improve performance, and the belief that using specific technology involves less effort (Lai 2017). Effort expectancy is the degree of convenience to use the system, which means perceived ease of use. It positively correlates with adopting and using new technology (Jamaludin & Mahmud 2011). Social influence influences how others believe that the specific technology is useful (Bozan et al. 2016;Kijsanayotin et al. 2009). An individual perceives the availability of infrastructure and technical support to adopt new technology, defined as facilitating conditions (Yang & Forney 2013).

Behavioural Science Theoretical Aspect for Readiness
The essence of the TRA theory is that an individual's behaviour is determined by behavioural intention. Attitude towards the behaviour (e.g., Smartphone is easy to use) and subjective norm (e.g., Other colleagues are using a smartphone and it is a status) influence an individual's behavioural intention.   (2017) TPB is created by adding perceived behavioural control to TRA, which is defined as the individual perception of limiting their behaviour. Other factors are like TRA first two factors.
The theory of Self-Efficacy is another popular theory to explore. Self-efficacy is defined as the degree of an individual's finding based on his/her belief of his/her ability to reach specific goals in a given situation (Bandura 1982). It can influence the technology usage of a person (Hsia 2014). Affective state, verbal persuasion, are mastery experience, various experiences are the four primary foundations of self-efficacy. The state of an individual's psychology and effective state directly affects self-efficacy. An enjoyable state will have a positive impact, but an unhappy state will affect it negatively.
The core context of Social Cognitive Theory (SCT) explains that learning happens through mutual interaction between individual, environment, and behaviour (Bandura 1986). To understand the technology adoption process, SCT is vital. Two concepts displaying related to production and support to the motivational process is extremely helpful in understanding technology adoption. (Bandura 2001;Straub 2009). Straub (2009) explains that an individual observing others using specific technology successfully will influence himself/herself to successful adoption of it (Straub 2009). The probability of an individual's interest in sustaining an action can be defined as a concept of reinforcement (Straub 2009), and it focuses on the importance of Management and organisational support in technology adoption.

Readiness Empirical Research
In two studies done by Walczuch et al. (2007) and Son & Han (2011), four constructs impacting technology readiness optimism, innovativeness, discomfort, and insecurity were discussed.
Optimism is a belief that specific technology can help to improve the efficiency, control, and flexibility of an individual (Parasuraman 2000). Optimistic individuals tend to see positive aspects and openly deal with new technology (Walczuch et al. 2007).
The individual's tendency to use technology is defined as innovativeness (Parasuraman 2000), and such individuals demonstrate high comfort levels and need little convincing related to the outcome of technology adoption. Prior research has reported a positive correlation of innovativeness towards readiness of technology adoption.
Insecurity is defined as the lack of trust related to privacy and security reasons (Parasuraman 2000). Such individuals tend to have negative feedback on the technology (Son & Han 2011). The general tendency of fear (Mukherjee & Hoyer 2001) and the perception of lack of control (Parasuraman 2000) can bring discomfort in adopting specific technology. Walczuch et al. (2007) concludes that optimism has a solid relationship to perceived usefulness (PU) and perceived ease of use (PEOU) which in turn has an impact on readiness for technology adoption as per TAM. He further explains that innovativeness and discomfort negatively impact PU and PEOU. In addition to PEOU, PU, subjective norms, and perceived behavioural control, Sun et al. (2009) tested the relationship of perceived work compatibility to readiness for technology adoption. Work compatibility is not related to this study as it defines the organisational fit of selected technology but not the personal fit.
The influencing factors in adopting new technology vary as experience (Venkatesh & Davis 2000). In their research on 3D technology readiness, Chatzoglou & Michailidou (2009) highlights some of the key constructs to adopt AMT. They further explained that high experience levels improve the degree of ease of use than lower experience levels. Therefore, it is often assumed that experience level positively affects an individual's readiness for technology use (Thompson et al. 1991).
The job relevance of the adopted technology is essential for an individual's perceived usefulness (Thompson et al. 1991), which has a moderate effect (Chatzoglou & Michailidou 2009). The degree to which an individual perceived that the technology helps him/her to carry out the job well and efficiently is called output quality (Venkatesh & Bala 2008), which has a strong positive relationship to perceived usefulness (Compeau et al. 1999).
In addition to the above, Chatzoglou & Michailidou (2009)  Darbanhosseiniamirkhiz & Wan Ismail (2012) stressed that management support is a significant factor for AMT adoption. Towards the adoption of AMT, management motivation, enthusiasm, and employee encouragement are crucial (Ramdani et al. 2009;Al-Qirim 2007). According to (Jayaraj et al. 2006), one of the best predictors of the success of AMT adoption is management commitment and support.
Al-Ajam & Ali (2015), in their study, focuses on the constructs of the theory of reasoned action and its implications towards adoption of internet banking services in Yeman and validates the influence of attitudes and subjective norms towards technology adoption.

RESEARCH METHODOLOGY
Through literature review mapping, five independent variables were identified. Perceived usefulness (PU), attitude towards technology (ATT), perceived ease of use (PEOU), perceived management support (PMS), and Techno-Optimism (TOP) were derived as independent variables, where advance manufacturing technology readiness has been identified as the dependent variable.

Conceptual Model and Hypothesis Development
Following conceptual model was used to develop hypotheses, to be tested through data collection and statistical analysis.

Operationalisation
Constructs are broken into measurable parts where they could be measured. These specific parts are used to operationalise and test defined hypotheses. The Likert scale is used as the measurement method, as it is well recommended for a respondent to indicate their feelings, opinions, and attitude about a particular problem better (Nemoto & Beglar 2014).

Sampling and Data Collection
This research is adopted the deductive method, and data was collected through a quantitative survey. An apparel company that has implemented advance manufacturing technology was selected for the survey. There were 258 machine operators actively involved in using the technology. Hence the population for this survey is taken as 258, i.e., N = 258.
Maximum likelihood requires a sufficient number of samples, and a smaller number could lead to failure or improper results (Hair, et al. 2010). In line with this condition proposed by Hair, Black, Babin, & Anderson (2010) and by using an online sample calculation tool (System 2012), the sample size was determined as 112, i.e., n = 112 with a 95% confidence level and confidence interval at 7.
Since the population is small and in one geographic location, a simple random sampling method was used to reduce any bias involved. All 258 employees were given a serial number and generated a random number.
A questionnaire was prepared with two sections to collect primary data. The first section was to collect demographic data such as gender, age, education qualifications, and experience. The second section consists of 28 questions to measure independent and dependent variables as highlighted below. The questionnaire was designed to complete within 10 to 15 minutes, consisted of close-ended questions with a Likert scale of 1 -5. As the shopfloor employee's English proficiency was not up to the desired level, the questionnaire was translated into the Sinhala language. Hard copies of the questionnaire were distributed among 125 randomly selected employees and received 118 completed responses.

Data Reliability
A pilot data test was done with 20 samples to check the reliability of the questionnaire, and Cronbach's Alpha is 0.884, which represents 88% reliability. below are the test results of each independent and dependent variable. The alpha value was 0.909 for all 24 questions, which means the data is 90% reliable for further analysis and investigation.

Demographic Profile of the Sample
Analysis shows that 80% of the respondents are female, representing the general understanding that most Sri Lankan apparel shop floor machine operators are female workers.
Only 4% of the employees are over 45+ years of age. One-fourth of the population is in the age group of 36-45 years. The majority of the employees fall into groups 18-25 years (36%) and 26-35 years (35%), which means around 71% of the employees of the population are young.
Almost half, 48% of the respondents, is between 1-5 years of experience, while one fourth (25%) is between 6-10 years, which indicates that 80% of the respondents are well experienced. There are only 20% of respondents below one year.

Correlation and Coefficient Analysis
Following scale from Saunders, Lewis, & Thornhill (2016) is used to find the relationship and strength of the relationship of the independent variable to dependent variable.  The result shows that perceived ease of use has a strong positive relationship and perceived management support has a week positive relationship. All other three factors show a moderate positive relationship.   Thompson 1995;Venkatesh & Davis 1996;Venkatesh, et al. 2003;Sun 2016;Lai 2017). Also, many studies done by Chen, et al. (2009), Chen, et al. (2013, Hsia (2014) This study sanctions the findings of previous research that techno-optimism of the employees has a significant influence on employee readiness. Though it was not discussed and highlighted in theories, much research, Walczuch et al. (2007), Chen et al. (2009), Son & Han (2011), Csuka et al. (2019), and Blut & Wang (2020 have stressed the importance of techno-optimism towards readiness. This study confirms that techno-optimism has a moderate positive relationship and 22% contribution towards employee readiness for AMT adoption.

Multiple Regression Analysis
Like any other research, this study has gaps. The sample selection is from one single company, which limits the generalisability of the findings. A study needs to be taken place to cover an adequate number of companies using AMT. Also, this study focused only on five factors that contribute 46% towards employee readiness for AMT adoption. Future research can investigate other factors that influence employee readiness for AMT adoption on the apparel shop floor. Also, the study could be expanded to test this framework on other industries.
Formulate an employee readiness measuring scale would be useful, and it must be based on commonly accepted criteria. Such a tool would be useful for apparel companies to measure the level of employee readiness before implementing AMT projects.
Further study on smart factories and industry 4.0 on the apparel shop floor is also recommended as the apparel shop floor is very much behind technology compared to other industries. Ajzen, I, Madden, TJ & Ellen, PS 1992, 'A comparison of the theory of planned behavior and the theory of reasoned action', Personality and social psychology Bulletin,vol. 18,no. 1, Al-Ajam & Ali, S 2015, 'Challenges of adoption of internet banking service in Yemen', International Journal of Bank Marketing, vol. 33, no. 2, pp. 178-195. Al-Qirim, N 2007