*Corresponding author Email: [email protected] P-ISSN : 2252-8997 ARTICLE
Asia-Pacific Management and Business Application 11 (3) 263-282
©UB 2023 University of Brawijaya Malang, Indonesia http://apmba.ub.ac.id
The Importance of New Ways of Working to Influence Workforce Agility in The Manufacturing Sector for Managing Destructive Situation
Fransiska Cicilia Pembayun Noviansista Cornelisa*
Hary Febriansyahb
a, b School of Business and Management, Institut Teknologi Bandung, Bandung, Indonesia
Abstract
After the COVID-19 pandemic, the manufacturing industry could quickly regain its performance. The question remains whether new ways of working (NWW) contribute to this adaptable behaviour of manufacturing employees. Implementing NWW techniques in manufacturing varies since the production process includes primary and secondary activities.
Moreover, Psychological Empowerment plays a role in Workforce Agility in terms of employee sustainability as a human who requires intrinsic motivation to cope with any environment. This study surveyed 233 manufacturing employees in Indonesia and used SEM-PLS to determine the importance of applying NWW for manufacturing to attain its agility, particularly in the human factor. The results indicate that NWW has a positive effect on Workforce Agility. The authors conclude with discussions and the implications of implementing NWW in the industrial sector based on the supported proposed model.
Keywords
New Ways of Working; Workforce Agility; Manufacturing
Received: 5 January 2023; Accepted: 3 February 2023; Published Online: 30 April 2023
DOI: 10.21776/ub.apmba.2023.011.03.1
Introduction
Efficiency in the manufacturing sector, which produces products in large quantities, helps people fulfilling their needs in no time. Therefore, if this sector cannot operate for any reason, a large number of populations will struggle to attain human needs, either primary, secondary, or tertiary needs. For example, if we could go back to the beginning of COVID-19, there were times when face masks were getting scarce because the demand was excessively rising.
Still, even with the social distancing
regulation when people could not meet others due to reducing the contagious effect, the manufacturing sectors could regain their performance and incrementally fulfil human needs, especially during the pandemic of COVID-19 (UNIDO, 2021).
The pandemic of COVID-19 situation should be explorer in any business sector because the pandemic has caused a business disturbance on an extraordinary scope (Ozdemir et al., 2022).
In the manufacturing sector, the growth in the first-second quarter of 2020 dropped off than in 2018 and 2019 (UNIDO, 2021).
Nevertheless, manufacturing sectors recovered their condition since the third quarter of 2020 and performed even higher than in 2018 and 2019 (UNIDO, 2021). In a disruptive situation, manufacturing sectors require resilience to produce human supply needs. For managing the resilience factor in manufacturing sectors, agility can be a crucial element for the organization.
Moreover, agility is not only for coping with and adapting to the dynamicity situation but also for keeping up with the rapid development of technology and dynamic organizational (Lee et al., 2022;
Ozdemir et al., 2022).
Many researchers studied agility as a critical element in attaining efficiency in the manufacturing sectors. The theory of agility in the manufacturing sector has been popular since the early 1990s to answer how to cope with the dynamic situation, increasing change, uncertainty, and competitive environments (Devadasan et al., 2005; Sherehiy et al., 2007). Recent findings found that the workforce is the most important element of agility in the manufacturing sector compared to advanced technology (Menon & Suresh, 2020) because the optimum use of technology could work effectively when the workforce achieves agility. Workforce Agility is critical as it can deal with change more effectively and positively (Leask &
Ruggunan, 2021), which is important in response to the rapid development of technology and the dynamicity situation, including the pandemic of COVID-19 (Ivaldi et al., 2022). Hence, many papers have researched Workforce Agility in many sectors during the pandemic of COVID-19 (Brack et al., 2021; Leask & Ruggunan, 2021; Menon & Suresh, 2020; Tamtam &
Tourabi, 2021; Thayyib & Khan, 2021).
However, studies about Workforce Agility in the manufacturing sector during the
pandemic of COVID-19, which is an important sector in fulfilling many human needs in any situation, still need to be examined.
The manufacturing sector should acknowledge how to manage the workforce to achieve the best performance on agility, even in a disruptive situation like the pandemic of COVID-19. In order to reduce the devastation caused by pandemics, the organization must invent some innovations (Ozdemir et al., 2022). Laubengaier et al.
(2022) proposed that technology and administrative process innovations are necessary for manufacturing that aims to transform traditional manufacturing into smart manufacturing. Smart manufacturing is described as an integrated and flexible manufacturing system adapting to changing conditions (Laubengaier et al., 2022). In addition, an innovative manufacturing company actively develops new products, processes, and New Ways of Working (NWW) (Laforet & Tann, 2006).
Concerning Workforce Agility as the most important factor in agile manufacturing, organizations have considered implementing NWW in adjusting themselves to dynamic situations (Aroles et al., 2021). Moreover, the dynamic situation was getting real during the pandemic of COVID-19 to survive in business continuity, prevent employee demotivation, maintain the best performance even when doing distance work, or manage unemployment through precaution regulations (Aroles et al., 2021; Azizi et al., 2021). According to research by J. H. Coun et al. (2021), supportive culture can increase intrinsic motivation, while Psychological Empowerment effectively mediates NWW to increase workplace initiative in the financial sector. However, the company requires Workforce Agility to deal with the COVID-19 pandemic and other destructive situations. So, it still needs to clarify the relevance between NWW and Workforce Agility when the manufacturing
The Importance of New Ways of Working to Influence Workforce Agility... 265 sector could operate in dynamic
environments.
Consequently, this study focuses on examining the effect of NWW implementation in the manufacturing sector on Workforce Agility. In addition, this study looked over Psychological Empowerment as the mediating effect between NWW and Workforce Agility.
Literature Review
Human Resource Management (HRM) practices collaborated with New Ways of Working (NWW) to respond to the Information, Communication, and Technology (ICT) rapid development (Gerards et al., 2018). Notably, it has become more crucial since COVID-19 policies in many countries increased the attention of many organizations to implement NWW to keep business continuity (Aroles et al., 2021; Gerards et al., 2020). The concept of NWW is not only a broader trend of workspace differentiation and flexibilization (Mitev et al., 2021) but also becomes an enabler for organizations to respond more flexible environment: new market requirements, improve service quality, and enhance operating efficiency (Peters et al., 2014).
Bondarouk & de Leede (2016) posit that NWW will eventually become the normal way of working, but the development of new technology and the needs of the labour market demand new forms of work.
Therefore, in a complex and highly unpredictable environment, NWW is an amazingly flexible form of HRM practice to improve organizational agility. The focus of NWW is on “How,” “When,” and
“Where” employees can continue working under disadvantageous conditions.
New Ways of Working in The Manufacturing Sector
The issue of how manufacturing could incorporate NWW practices while some activities are taking place in the workplace
must be addressed. Different work flexibility trends and the rise of innovative workspaces, such as collaborative spaces, or Fab Labs, are examples of how NWW is manifested in the real world (Mitev et al., 2021). Additionally, factors for integrating NWW practices include the fragmentation of work, the proliferation and ramification of work practices, the new cooperation model, and the rise in production consumption (Mitev et al., 2021). So, given that it has a variety of activities, NWW might have a wide range of practices in manufacturing sectors (Porter, 1985). A manufacturing system is any process that transforms raw materials into a finished good with increased or added value (Parnaby, 1979). The actions involved in producing the product are divided into primary and support activities (Porter, 1985). The primary activity, physically creating the product, is one of the main activities. In contrast, support activities provide inputs that have been purchased, technology, human resources, and various firm-wide operations to assist the primary Porter's (1985) description of primary activities, the concept of flexibility in NWW to manufacturing may be constrained because primary activities should be linked to the firm's processes.
However, Lee et al. (2022) asserted that industrial artificial intelligence (the intelligent system, the digital system, the automation system) might connect people to machine networks and enable one person to handle a sizable fleet of machines because of the rapid development of ICT.
To increase manufacturing resilience, it could also convert conventional physical production systems into digital ones (Lee et al., 2022). Therefore, with the rapid development of ICT, employees who are engaged in primary tasks may become more flexible in the future.
ICT has been crucial in terms of workplace dynamics (Aroles et al., 2021;
Gunasekaran, 1999; Mitev et al., 2021).
ICT has developed into an issue that has to
be handled in the information-intensive production environment compared to conventional manufacturing systems (Gunasekaran, 1999). ICT gives workers a variety of alternatives for communication with co-workers, supervisors, and clients when working together over the NWW (Demerouti et al., 2014). ICT in workplace communication allows employees to share their expertise (J. H. Coun et al., 2021).
Sherehiy & Karwowski (2014) proposed that collaborative usage of ICT applications promotes employee adaptability regarding access to knowledge via ICT as part of NWW. Letmathe and Rößler (2022) further emphasized the significance of knowledge transmission via digital work instructions or any portal in industrial operations.
According to an experiment, digital work instructions for manufacturing produced products faster and with fewer faults than paper-based instructions (Letmathe &
Rößler, 2022). The outcome shows how ICT may be used to implement NWW to create flexible industrial settings. Industrial artificial intelligence is a further potential of ICT. Because industrial artificial intelligence enables self-adjustment, self- optimization, and self-configuration, replacing human functions, "smart"
factories might make quick judgments with minimum human input (Lee et al., 2020).
The phrase this advanced technology will produce a future of employment in cyber manufacturing, from people operating machines to robots acting as humans up close to the merging of humans and machines or whole processes with intelligence systems (Lee et al., 2020, 2022;
North & Kumta, 2018). However, creative attempts continue to be limited to humans (North & Kumta, 2018).
Work is no longer confined to a specific location or time, and further usage of ICT might disassemble work design to enable more complicated tasks (Aroles et al., 2021;
Mitev et al., 2021; Obermayer et al., 2022).
By completing work outside of the organization's physical surroundings and at
different times, the location and timing of work become autonomous (Duque et al., 2020). Worktime flexibility allows employees more freedom in determining when they work (Demerouti et al., 2014).
Flexibility in working hours shifts from the conventional control of set working hours with obvious distinctions between work and leisure to the more internal and personal management of these distinctions (Mellner et al., 2016). As they are not involved with the physical environment, employees in support manufacturing operations might utilize flexible scheduling. It is difficult for primary activities to determine whether they operate autonomously. Since manufacturing's primary operations have input-output performance characteristics at a given time and process, people must adapt their work time to the established process (Parnaby, 1979). Moreover, workplace flexibility provides workers with several possibilities for their work location (Demerouti et al., 2014). NWW workplace flexibility methods include nomadic work, hot- desking, co-working space, virtual work, and mobile work (Aroles et al., 2021).
Palvalin et al. (2015) propose physical and virtual environments for the WWW. The virtual environment incorporates nomadic working, co-working space, and virtual mobile working. However, hot desking affects the physical surroundings. With place or time flexibility, employees can be contacted easily and quickly, interact with co-workers around the world, have real- time information readily available, make decision-making hastened, and have flexible work schedules (Demerouti et al., 2014). However, the production system is not entirely cyber-enabled. In that case, the notion of a flexible workplace cannot be applied to some operations in the central portion since workers must have physical interaction while producing goods (Porter, 1985). In conventional manufacturing, numerous personnel carry out the process and interact with the physical environment as part of the significant operations
The Importance of New Ways of Working to Influence Workforce Agility... 267 (Parnaby, 1979). Despite this, NWW
flexibility might nevertheless mediate the setting of the physical environment and increase employee engagement at work (Duque et al., 2020). In this dynamic era, the workplace and all its spaces should be helpful to both self-concentration and cooperation (Palvalin et al., 2015), such as by including hot-desking or communal areas.
Greater autonomy and self-management are required for workplace flexibility in terms of time and location practices (Demerouti et al., 2014; Gerdenitsch, 2017; Mitev et al., 2021; Peters et al., 2014). Regarding the NWW environment, Palvalin et al. (2015) define social work as one in which workers have autonomy and may use NWW concerning organizational habits.
Therefore, autonomy enables employees to adopt NWW (Palvalin et al., 2015; Peters et al., 2014). J. H. Coun et al. (2021) connected workplace flexibility with professional autonomy, which enables individuals to choose "how" to execute and complete their tasks. Moreover, in these changing circumstances, professional autonomy requires self-management as an essential competency (North & Kumta, 2018). Self-management entails arranging work, setting or redefining work goals, selecting appropriate tools and procedures, and managing competence growth and work-life balance (North & Kumta, 2018).
Bal & Izak (2021) elaborated on work-life balance as an advantage of autonomy over flexibility in which individuals may control their work-life balance. Professional autonomy may improve work-life balance for support tasks in manufacturing, but the situation will be different if autonomy is extended to main operations. Introducing self-management for workers in primary tasks creates solutions for improvement in the physical environment and the physical operational process (North & Kumta, 2018). Using the notion of NWW, physical workplace workers may have space to share their expertise, express their thoughts,
exchange problem-solving strategies, focus on themselves, and cooperate with others (Duque et al., 2020; North & Kumta, 2018;
Palvalin et al., 2015). Because encouraging workers by allowing them to share their ideas will foster a creative environment at work (Palvalin et al., 2015).
NWW and Workforce Agility
NWW methods help increase manufacturing resilience in disruptive environments (Cooke et al., 2019). J. H.
Coun et al. (2021) discovered that firms that have embraced empowering HRM strategies, such as workplace flexibility, professional autonomy, and access to information through ICT, encourage workplace proactivity via Psychological Empowerment. Moreover, proactivity is a component of Workforce Agility, resilience, and flexibility (Sherehiy et al., 2007). According to Sumukadas and Sawhney (2004), power-sharing as a set of higher-order employee engagement is necessary for workforce adaptability.
Power-sharing is described as the employee recommending an improvement, providing and receiving feedback, and exercising self- management (Sumukadas & Sawhney, 2004). Professional autonomy under NWW is analogous to power-sharing as it gives employees flexibility. Furthermore, employee participation or power-sharing strategies substantially and significantly affected worker agility (Sherehiy &
Karwowski, 2014). In addition, collaboration and work teams need new work structures for employee advancement and growth in agile firms (Sherehiy &
Karwowski, 2014). According to recent research (Gerdenitsch, 2017), flexible workplaces and work hours assisted people in becoming more adaptable.
Workforce Agility is not considered a personality, attitude, or feature but rather an observable performance or behaviour (Sherehiy & Karwowski, 2014). Instead of technology, agile manufacturing is the primary contributor to Workforce Agility
(Alavi et al., 2014; Menon & Suresh, 2020;
Sherehiy et al., 2007). Agile manufacturing is the ability to survive and thrive in a hostile environment of constant and unexpected change by responding rapidly and flexibly to changing markets (Gunasekaran, 1999). Moreover, Ivaldi et al. (2022) argued that, even though technology integration may bring significant competitive advantages, industrial flexibility is still largely dependent on people since humans operate technology. The three aspects of Workforce Agility are proactivity, adaptability, and resilience (Sherehiy & Karwowski, 2014).
Proactive conduct helps people to confront unplanned concerns, adapt to a dynamic or changing environment, and solve problems successfully (Qin & Nembhard, 2015).
Proactive might bring about adaptable behaviour in workers (Griffin & Hesketh, 2003). Adaptive behaviour refers to the ability of individuals to operate in dynamic circumstances via continuous learning, the performance of various jobs, and participation in diverse teams (Qin &
Nembhard, 2015). Organizations maintain their resilience by adjusting to substantial obstacles to preserve their long-term viability (Leask & Ruggunan, 2021).
Additionally, a resilient staff is essential for boosting an organization's competitiveness in this firm market rivalry (Cooke et al., 2019). Using social cognitive theory, which explains human functioning in terms of triadic reciprocal causation between individuals, behaviour, and external environment (Wood & Bandura, 1989), the hypothesis between NWW and Workforce Agility is as follows:
H1: New ways of working practices through ICT, professional autonomy, flexible worktime, flexible workplace, and physical workplace have positive effect on Workforce Agility through proactivity, adaptivity, and resilience.
Workforce Agility and Psychological Empowerment
Empowerment techniques benefit adaptive performance, one of the characteristics of Workforce Agility (Charbonnier-Voirin &
el Akremi, 2011). It reflects the intrinsic task's motivating aspects and reveals their cognitive orientations toward their work function (Spreitzer, 1995). Muduli (2017) discovered that Psychological Empowerment might encourage or inhibit adaptable attitudes and behaviour. J. H.
Coun et al. (2021) discovered that Psychological Empowerment completely mediates the relationship between NWW practices and a proactivity environment.
Psychological Empowerment is the experience of intrinsic motivation based on an individual's understanding of their professional position (Muduli, 2017).
Spreitzer (1995) proposed that Psychological Empowerment is exemplified by meaning, competence, self- determination, and impact, demonstrating cognitive orientation in the workplace.
Meaning is described as the importance of work purpose, competence as self-efficacy, self-determination as autonomy in initiating and sustaining work behaviour, and impact as the extent to which a person may affect others in the workplace (Spreitzer, 1995).
Psychological Empowerment directly impacts and indirectly affects new capacities, autonomy, emotional intelligence, and employee engagement.
Consequently, it becomes an enabler of Workforce Agility (Menon & Suresh, 2020;
Qin & Nembhard, 2015).
Psychological Empowerment has been proven to positively influence Workforce Agility (Menon & Suresh, 2020; Muduli, 2017; Qin & Nembhard, 2015). Muduli and Pandya (2018) contended that genuinely motivated individuals are required for Workforce Agility.
The Importance of New Ways of Working to Influence Workforce Agility... 269 According to Spreitzer's (1995)
Psychological Empowerment theory, the most influential aspects are meaning, self- determination, and impact (Muduli &
Pandya, 2018). Psychological Empowerment is an important aspect that mediates the connection between organizational practices and Workforce
Agility (Muduli, 2017). Further, Muduli (2017) defined organizational practices as learning and training, employee participation, cooperation, and information sharing. However, flexibility, adaptability, and dynamism are becoming essential for new kinds of employment because of globalization, fast technological progress, and protection against disruptive occurrences (Mitev et al., 2021). J. H. Coun et al. (2021) research revealed a substantial correlation between workplace flexibility, professional autonomy, and access to information through ICT and Psychological
Empowerment. Furthermore,
Psychological Empowerment mediates the connection between NWW and workplace proactivity (J. H. Coun et al., 2021).
Sherehiy et al. (2007) posit that proactiveness, adaptability, and resiliency
are components of Workforce Agility.
External and internal motivation are essential for adaptive behaviour (Peters et al., 2014; Wood & Bandura, 1989). In addition, Peters et al. (2014) demonstrated that using NWW could enhance individuals' innate drive. Hence, using Psychological Empowerment theory,
which includes specific motivating components for a person to recognize intrinsic activities (Spreitzer, 1995) and acknowledged by the motivational aspect of social cognitive theory (Wood &
Bandura, 1989), the hypothesis is as follows:
H2: Psychological Empowerment through meaning, competence, self- determination, and impact mediates the relevance between new ways of working and Workforce Agility.
Research Method
Measurement
The respondents faced four sections of the questionnaire. The first section is about demographic needs: gender, age, education, tenure, position in the organization, and whether she/he works remotely, on-site, or
Psychological Empowerment
New Ways of Working
Workforce Agility
Meaning Competence Self-determination Impact
Proactivity
Adaptivity
Resilience ICT
Professional Autonomy
Flexible Worktime
Physical Workplace
H1
H2
Flexible Workplace
Figure 1. Conceptual Framework
hybrid to distinguish the workplace type.
The second section is about New Ways of Working (NWW) questions. The third section is Psychological Empowerment questions. Furthermore, in the last section, the respondents are questioned about Workforce Agility behaviour.
NWW becomes the latent variable measured through manifest variables: ICT, professional autonomy, flexible worktime, physical workplace, and flexible workplace. The latent variable of NWW is the independent variable. Professional autonomy and ICT are measured using the questionnaire proposed by J. H. Coun et al.
(2021). Flexible and physical workplaces used measurements from Palvalin (2015).
A five-point Likert scale was used for all items (from completely disagree to completely agree).
Psychological Empowerment as the mediating variable was measured with the questionnaire proposed by Spreitzer (1995). This questionnaire consists of four subscales: meaning, self-determination, competence, and impact. Spreitzer (1995) suggested using these four subscales in one construct. A seven-point Likert scale was used for all items (from completely disagree to completely agree).
Workforce Agility is the latent variable measured through manifest variables:
proactivity, adaptivity, and resilience. This latent variable of Workforce Agility is the dependent variable. Sherehiy &
Karwowski's (2014) measurement for Workforce Agility was used here: proactive refers to the situation when a person initiates activities that have positive effects on the changing environment; adaptive refers to the modifying of one's behaviour to better fit a new environment; resilience describes the ability to function efficiently under stress, despite changing the environment, or when strategies applied to solve a problem have failed. The items were measured through the five-point scale
representing frequencies (from never to always) and intensities (from low to high).
Subject and Data Collection
This study was conducted in Indonesia because the manufacturing industry is the top contributor to Indonesia’s gross domestic product (Ministry of Industry Republic of Indonesia, 2017). The authors administrated a questionnaire to 250 respondents for the employee in the manufacturing industry in Indonesia from October until December 2022. This study surveyed through a questionnaire with non- probability sampling as the authors desire to learn in the manufacturing sector specifically. A purposive sampling technique was executed as specific criteria are required for this study: (1) work in Indonesia; (2) work in a manufacturing company; and (3) the manufacturing company where the respondent work has been implementing hybrid work for at least two years. This study used Google Forms as a tool for providing online questionnaires. The authors also prepared an offline questionnaire for the employees who are not used to the online form. The authors did not specify the type of manufacturing since when conditions vary contributes to generalizability (Polit &
Beck, 2010). Therefore, this study is expected can represent the manufacturing sector in general. Among 250 questionnaires collected, 17 were rejected due to incompleteness. Hence, 233 questionnaires were appropriate for further assessment.
Results
Characteristics of Respondents
For this study, the respondents' characteristics are determined by gender, age, tenure, work type, and manufacturing type as the primary data. Based on Table 1, from 233 respondents, 57.94% are female, 75.11% are 20 – 30 years old, 63.52% are working for 0 – 5 years, most of them are working hybrid (46.78%), lastly, each
The Importance of New Ways of Working to Influence Workforce Agility... 271 primary and support nearly half of the
respondents.
Descriptive Analytics
The perception of 233 respondents is represented in Table 2 in this study based on the mean average for each variable. For the latent variable NWW, Professional Autonomy has the highest mean, while Flexible Worktime is the lowest.
Psychological Empowerment as the latent variable has Competence as the highest manifest variable and Impact as the lowest.
For the latent variable Workforce Agility, Adaptivity is the highest manifest variable, and Resilience is the lowest.
Structural Equation Modelling-PLS Outer Model
This study used the variance-based or component-based with Partial Least Square (PLS) method. Evaluation of Structural Equation Modelling with the PLS approach calculates the outer and inner models.
Figure 2 represents the algorithm path diagram using PLS. For the outer model, this study used convergent validity, discriminant validity, and composite reliability. Convergent validity is used to find the item which can be applied as the indicator for the latent variable. The testing is based on outer loading from the construct indicator and Average Variance Extracted (AVE). Convergent validity requires the number of factor loading > 0.7 for each
indicator and AVE > 0.5 for each manifest variable (Hair et al. 2019). From Figure 2 and Table 2, the indicators of NWW, Psychological Empowerment, and Workforce Agility converge to represent the underlying construct statistically. All factor loadings are more than 0.7, and the value of AVE is higher than 0.5. It means the indicators valid to represent the manifest variable. After the model fulfils the requirement of convergent validity, this study tested the model in discriminant validity to ensure that the constructs are distinct from each other. From Table 3, Table 4, and Table 5, it is concluded that all manifest variables are distinct since the correlation for each manifest variable is higher than the correlation with others. It is also applied to the manifest variable:
NWW, Psychological Empowerment, and Workforce Agility (Table 6). Composite reliability and Cronbach’s Alpha were conducted to evaluate each construct in this study. The construct is reliable if composite reliability > 0.7 and Cronbach’s Alpha >
0.6 (Hair et al. 2019). Table 7 represents the composite reliability and Cronbach’s Alpha value for each manifest variable. All of the composite reliability is more than 0.7, and all of Cronbach’s Alpha is more than 0.6.
Therefore, all of the manifest variables for NWW, Psychological Empowerment, and Workforce Agility are reliable for measuring its latent variable.
Variable Description Frequency (%)
Gender Male 98 42.06
Female 135 57.94
Age 20 – 30 years old 175 75.11
31 – 40 years old 42 18.03
41 – 50 years old 14 6.01
> 51 years old 2 0.86
Tenure 0 – 5 years 148 63.52
6 – 10 years 54 23.18
11 – 15 years 15 6.44
> 16 years 16 6.87
Work type On site 90 38.63
Remote 34 14.59
Hybrid 109 46.78
Manufacturing Primary 111 47.64
Support 122 52.36
Latent Variable Manifest Variable Mean Average AVE
NWW ICT 3.881 0.727
Professional Autonomy 3.980 0.725
Flexible Worktime 3.240 1.000
Physical Workplace 3.835 0.670
Flexible Workplace 3.891 0.696
Psychological Empowerment Meaning 5.705 0.784
Competence 5.873 0.820
Self-determination 5.687 0.781
Impact 5.499 0.813
Workforce Agility Proactivity 3.781 0.708
Adaptivity 4.041 0.677
Resilience 3.554 0.687
ICT PA FWT PW FWP
ICT 0.852
PA 0.749 0.852
FWT 0.733 0.762 1.000
PW 0.693 0.727 0.693 0.819
FWP 0.778 0.755 0.708 0.778 0.834
M C S I
M 0.885
C 0.670 0.906
S 0.666 0.664 0.884
I 0.616 0.565 0.634 0.901
Table 2. Descriptive Analytics and AVE Convergent Validity Table 1. Respondent Characteristics
Table 3. Discriminant Validity for NWW
Table 4. DiscriminantValidityforPsychological Empowerment
The Importance of New Ways of Working to Influence Workforce Agility... 273
Inner Model
In a structural equation modelling (SEM) analysis, the inner model specifies the relationships between the latent variables that comprise the model. This study used R- Square to calculate the effect on an endogenous latent variable in Table 7. R- Square value for Psychological Empowerment is 0.560 and for Workforce Agility is 0.563. It means that Psychological Empowerment is strong enough to become mediating variable between NWW and Workforce Agility. In addition, Workforce Agility is also strong enough as a latent variable since it is affected by NWW and Psychological Empowerment for 56.3%.
Using Smart-PLS 3.0, Table 7 also shows the value of R-Square for each manifest variable. When the value of R-Square on the manifest variable increases, the model is better at representing the latent variable.
R-Square of all manifest variables is more than 0.6, which means NWW, Psychological Empowerment, and Workforce Agility are formed by strong manifest variables. Consequently, manifest variables could describe each latent variable effectively.
HypothesesTesting
This study used T-Test with a significance level of 0.05 to evaluate the hypotheses.
Figure 3 represents bootstrapping path diagram inner model using Smart-PLS 3.0, and the value of path coefficient, T- Statistics, and P-value are shown in Table 8. The result supports H1 since P-value is less than 0.05 with a path coefficient of 0.421 and T-Statistics is greater than its critical value. So, NWW has a direct positive relationship with Workforce Agility, which is statistically significant. It also occurs for the second hypothesis, which is the indirect effect of NWW on Workforce Agility. Psychological Empowerment is mediating variable for this model. The P-value for H2 is less than 0.05 with a path coefficient of 0.286, and T- Statistics is greater than its critical value.
Hence the result also supports H2.
Psychological Empowerment could mediate the relationship between NWW and Workforce Agility even though it is not as significant as the result of H1.
P A R
P 0.841
A 0.748 0.823
R 0.649 0.762 0.829
NW PE WA
NW 0.757
PE 0.748 0.762
WA 0.706 0.697 0.752
Table 5. DiscriminantValidityforWorkforceAgility
Table 6. DiscriminantValidityforConceptualModel
Figure 2. Algorithm Path Diagram
The Importance of New Ways of Working to Influence Workforce Agility... 275
Figure 3. Bootstrapping Path Diagram
Discussion and Implication
The first thing to emphasize from these findings is the effect of NWW on Workforce Agility directly in hypothesis 1.
Implementing NWW practices would enable personnel in the manufacturing industry to have agile behaviour in any majorly challenging environment. It entails maximum ICT utilization, autonomy, flexible work schedule, optimization in the physical workplace, and a flexible workplace in which managers may immediately communicate with their staff.
Information technology enables the manufacturing industry to create more efficient processes, such as digitizing work instructions that improve the performance in execution time (Letmathe & Roßler, 2022). The result supports the study from Ivaldi et al. (2022) about how technology addresses the agile approach to make humans at the centre of the technological optimization process. Hence, manufacturing should optimize its ICT to have another option of NWW regarding
achieving Workforce Agility. A study by Hedman et al. (2022) posit that activities in a digital environment can only efficiently occur if digital maturity in the organization is high. Simultaneously, ICT can improve employees' autonomy, enabling them to reduce bureaucracy so that decisions may be taken rapidly, particularly amid urgent environmental shifts. This supports Zuber et al.'s (2022) statement that the critical factor in forming agile teams is that employees can make decisions autonomously. This includes when the employee decides the place and time they work at. Hence, the place and time at work become more flexible. Flexible workplaces offer real-time collaboration in work practices without a physical presence in the exact location, or vice versa, participation in a work practice at a specific time regardless of location (Aroles et al., 2021).
With real-time collaboration, the employee becomes more agile in creating decision and come for more innovation. Bal & Izak (2020) proposed the benefit of flexible work: organizational and employee Variable CompositeReliability Cronbach’sAlpha R-Square
NWW 0.974 0.972 -
ICT 0.941 0.924 0.789
ProfessionalAutonomy 0.949 0.937 0.820
FlexibleWorktime 1.000 1.000 0.679
PhysicalWorkplace 0.934 0.918 0.783
FlexibleWorkplace 0.941 0.927 0.844
Psychological Empowerment 0.943 0.934 0.560
Meaning 0.916 0.862 0.749
Competence 0.932 0.890 0.728
Self-determination 0.914 0.859 0.755
Impact 0.929 0.885 0.677
Workforce Agility 0.981 0.980 0.563
Proactivity 0.964 0.959 0.759
Adaptivity 0.971 0.968 0.895
Resilience 0.963 0.958 0.782
Hypothesis PathCoefficient T-Statistics P-Value Result
H1 0.421 5.055 0.000 Supported
H2 0.286 3.150 0.001 Supported
Table 7. Composite Reliability and Inner Model (R-Square)
Table 8. PathCoefficient,T-Statistics,andP-Value
The Importance of New Ways of Working to Influence Workforce Agility... 277 flexibility and flexible work arrangements
for both organization and employees. This flexibility is believed to be the required ability to transform from traditional processes to smart manufacturing (Sassanelli et al., 2023). On the contrary, flexible work time has the lowest point in developing NWW. As expected from the manufacturing core process, which has many subsystems in primary activities, they interact in an integrated and determined whole system in combining the components of a system based on input-output performance characteristics that are assumed to be constant at all times and on an average basis: steady-state design (Parnaby, 1979). So, the employees in the primary activities do not have the privilege to choose when they work because they are attached to a specific system in manufacturing. Nevertheless, employees in the primary activities are the most important for the manufacturing process.
Hence, the digital environment in the physical workplace should also be optimal.
The result of NWW in the physical workplace, which supports Workforce Agility, is supported by a study from Hedman et al. (2022) about how operator teams should be provided with training, education, tools, time, space, and managerial support for exploration and creative thinking.
Even though NWW positively impacts Workforce Agility as one construct, proactivity is the lowest point that develops Workforce Agility. It is aligned with J. H.
Coun et al. (2021) study, which found that HRM practices (along with NWW and empowering leadership) have no potential motivation regarding workplace proactivity directly. For this study, we build Workforce Agility from three manifest variables:
proactivity, adaptivity, and resilience.
Based on the results, adaptivity is the highest dimension of Workforce Agility. In comparison, Shoss et al. (2022) suggested that adaptive and proactive behaviour are connected in the context of organizational
learning to change and improve in the dynamic environment. The results support a study by Ozdemir et al. (2022) that proactivity, adaptability, and resilience are the main characteristics of an agile workforce that could embrace changes in pursuing supply chain resilience.
Interestingly, hypothesis 2 shows that Psychological Empowerment could mediate the relationship between NWW and Workforce Agility but less strongly than how NWW could affect Workforce Agility directly. On the other hand, J. H.
Coun et al. (2021) have found that NWW (along with empowering leadership) could not affect workplace proactivity directly.
But in the end, NWW affects proactivity mediated by Psychological Empowerment.
Indeed, as Zuber et al. (2022) put forward about Psychological Empowerment, it can strengthen agile behaviour, and the result of this study also shows so. However, the direct relationship between NWW and Workforce Agility is stronger. We suggest it could not be separated from the rapid development of ICT. The pandemic of COVID-19 accelerated digital transformation (Aroles et al., 2021). The rapid development of ICT and how people can be connected so fast are the main point to adjust to any disruptive situation.
Because ICT can make diversification and evolution of work and enable new forms of collaboration (Aroles et al., 2021). It triggers more uncertainty and complex situation. In addition, a study by Ivaldi et al.
(2022) found that the challenge of the high level of uncertainty and complexity is the innovation of condition that needs convergent evolution of different approach.
The respondents from their study said how the way of work has changed and had to speed up until individuals have greater flexibility. Consequently, NWW practices itself strong enough to foster Workforce Agility in coping with the devastating situation directly in no time without getting through intrinsic motivation.
From a theoretical perspective, this study contributes to the development theory of NWW, Psychological Empowerment, and Workforce Agility as pandemic learning.
This study initially shows the feasible NWW in manufacturing, which has primary and supporting activities. The findings become an additional reference to examine NWW as the trigger of Workforce Agility in coping with any destructive situation.
From a practical perspective, the findings put up in this study demonstrate the importance of manufacturing implementing NWW in their human resource management practices in this dynamic era.
The point is not only about how an organization quickly adapt to the situation but also how to balance the change with the stability in the organization by understanding the NWW (Lindskog &
Netz, 2022). Managerial and human resources professionals could utilize their ingenuity to foster a decent workplace.
Notably, in the manufacturing sector, which consists of primary and support operations, NWW should be coupled in a particular way to influence the employees' optimal agility. Physical workplaces also play an essential role in triggering employee innovation and privacy.
Providing employees with the option to determine their work-life balance is also advantageous for human sustainability and peak performance. However, implementation is one of many considerations. A supportive culture is also vital, allowing management to monitor the efficacy of the NWW.
Conclusions, Limitations, and Further Research
Conclusions
This study focused on NWW in the manufacturing industry, its relation to Workforce Agility, and the mediating impact of Psychological Empowerment to become more resilient in any destructive
environment. As the dominant industry that supplies many products for human needs, manufacturing must consider dynamic situations in order to sustain the business.
The data used primary data from 233 people who work in Indonesia’s manufacturing and at least have been implementing NWW for two years.
Using SEM-PLS, the results show that NWW affects Workforce Agility directly and indirectly (H1 and H2 are supported).
Psychological Empowerment mediates the relationship between NWW and Workforce Agility. However, the direct relationship is stronger than the indirect relationship between NWW and Workforce Agility. On the one hand, a flexible workplace is the highest dimension of NWW, and flexible work time is the lowest. And in the other hand, proactivity becomes the lowest, and adaptivity is the highest dimension for Workforce Agility.
Limitations
This study is only limited to the manufacturing sector since the sector is prominent in economic development.
Further, this study has not considered respondents' position level because the initiation distinguishes the primary and support activities.
Further Research
Given the information underlying the proposed conceptual model, this study discusses the significance of implementing NWW, particularly in the manufacturing sector. The rapid development of information, communication, and technology has become the driving force behind the new understanding of how work is performed and how people behave. Since information, communication, and technology may alter the pattern of the manufacturing process in a digital environment, there is a need for more exploration and longitudinal analysis of manufacturing operations. It will assist the industrial sector in establishing a
The Importance of New Ways of Working to Influence Workforce Agility... 279 collaborative, adaptable environment and
managing corporate agility in preparation for the future of work.
Notes on Contributors
Fransiska Cicilia Pembayun Noviansista Cornelis is a student from Master of Science in Management, School of Business and Management, Institut Teknologi Bandung, Indonesia.
Hary Febriansyah is a lecturer and an Assistant Professor at the School of Business and Management Institut Teknologi Bandung. The area of interest is people and knowledge management.
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