Getting in sync: measuring perceived technological sustainability in organisations through the lens of employee well-being

Contemporary organisations operate in an era of rapid technological advancement, particularly in the field of artificial intelligence, which presents both opportunities and challenges related to the effective implementation of such innovations. These technologies carry both positive and negative implications for organisations (den Hond and Moser, 2023) and employee well-being (Urien and Erro-Garcés, 2024). In both academic literature and business practice, technology is often described as having a “Janus face,” metaphorically reflecting its dual nature: its implementation can either exacerbate stress or enhance satisfaction among employees, making their work either easier or more difficult (Arnold, 2003).

Industry reports confirm these observations, underlining the significant role of technology in shaping the well-being of employees. The report published by the EY Business Academy (2024) indicates that over 50% of the respondents perceive technology as invasive and report persistent technostress. One in three respondents claims that technology limits their free time, while one in four admits to struggling to keep up with new technologies, which leads to frustration. As a result of these rapid technological transformations, organisations are also facing an increase in work pace (33%), a growing need for continuous upskilling (23%) and concerns over employees' decreasing labour market value (20%), especially among older generations (Beane and Anthony, 2024; EY Business Academy, 2024). The Digital Workplace Productivity Report (2022)suggests that the mismanagement of technology (its over- or underuse) may result in serious consequences, including the deterioration of employees' well-being and their retention rate. Notably, 36% of the respondents reported considering a job change due to “unsatisfactory technology experiences in the workplace.” In this context, the term “technological (un)sustainability of organisations” has emerged in managerial and consulting discourse, although it remains under-explored in academic literature.

To date, organisational sustainability has been defined in the literature primarily through objective frameworks (Dahlsrud, 2008; Dyllick and Hockerts, 2002; Mariappanadar, 2003), focusing on two dimensions: internal (e.g. sustainable human resource management and the prevention of employee overexploitation) and external (e.g. minimising negative environmental impacts). Internal sustainability has been linked directly to employee well-being, leading to higher identification with the organisation (Fu et al., 2014), reduced turnover intentions (Du et al., 2015), and increased job satisfaction, as well as affective commitment through organisational pride (Zhou et al., 2017; Jenkin et al., 2011).

Despite increasing interest in combining internal organisational sustainability, employees' supportive technologies and well-being-oriented managerial practices (well-being-oriented management – WOM), research is still lacking in terms of tools that connect these constructs and explore employees' subjective experiences of how technology over- or underuse impacts their well-being in the workplace. Moreover, the existing approaches to technology adoption have largely emphasised experiences formed before the technology implementation while overlooking the evolving nature of employee experience and well-being during actual use in dynamic and changing environments. To date, there is no validated scale that captures such perceptions. This study, based on the Job Demands–Resources (JD–R) theory, addresses that gap by introducing and operationalising the concept of the Perceived Technological Sustainability of Organisations (PTSO). The authors define this construct as the subjective experience of the balance between technological demands and resources in the work environment, which arise from the implementation and use of technology within a given organisation.

Building on the assumption that sustainable work environment positively affects employee well-being (Haar et al., 2014), the primary aim of this study is to conceptualise and develop a valid measurement scale for the PTSO, addressing the noted gap in the existing literature. Its correlation with the TENS-Life Scale (TLS) (Burnell et al., 2023) – grounded in the Self-Determination Theory (Ryan and Deci, 2000) and designed to assess the satisfaction and frustration of basic psychological needs in everyday life – demonstrates that the PTSO captures such domains of technological application in organisations that are directly linked to employee well-being. Additionally, convergent validity was tested through correlation with the Technology Readiness Index (TRI) – a tool that accounts for both the stressor and inhibitors of technology adoption. These two validation constructs were purposefully selected due to their conceptual alignment with the theorised nature of the PTSO. Specifically, we hypothesised that employees who perceive their organisation as technologically sustainable would exhibit higher levels of technology readiness and would report greater satisfaction of basic psychological needs, along with lower levels of need frustration. In contrast, the perceptions of technological unsustainability were expected to be associated with lower technology readiness and elevated need frustration. By selecting the TRI 2.0 and the TENS-Life Scale as validation measures, we aimed to assess the convergent validity of our newly developed construct in relation to the established indicators of technology orientation as well as subjective well-being.

The theoretical and practical contributions of this article are threefold. First, in contrast to previous approaches, the PTSO focuses on employees' subjective perceptions and experiences related to technology adoption. It acknowledges both the opportunities of technology that enhance sustainability and well-being as well as the threats that contribute to perceived unsustainability and lowered well-being. Second, the study offers a measurement scale that distinguishes between the opportunities and threats to employee well-being arising from technology use in the workplace. This scale can be practically applied by managers as a diagnostic tool to assess and monitor their employees' perceptions of workplace technologies, which would constitute an essential step in designing technology strategies that foster well-being and align with the principles of well-being-oriented management (WOM). Third, previous approaches emphasised fostering positive attitudes during the initial adoption phase (Kędziora et al., 2024), but early perceptions can fail to predict later satisfaction. Since impact on well-being becomes clear only through use, the PTSO framework offers practical value by helping managers assess and respond to their employees' experiences during the actual use of technology.

The remainder of this paper is structured as follows. First, the interconnections between technology, organisational sustainability and well-being are examined. Next, the existing constructs and models related to workplace well-being are reviewed to highlight gaps in the current literature. Based on this, an overview is provided of both the focus group interviews (aimed at identifying when an organisation is perceived as technologically sustainable) and the quantitative study (conducted to develop and validate a scale measuring perceived technological sustainability). Finally, the findings are discussed in relation to the existing literature, outlining the theoretical and practical contributions of the study, its limitations, as well as directions for future research.

Contemporary approaches to employee well-being increasingly draw on the Job Demands–Resources (JD–R) theory, originally proposed by Bakker and Demerouti (2007). The theory posits that every occupation involves specific job demands and job resources. Job demands refer to those aspects of the work environment that require sustained physical or psychological effort, such as workload, time pressure, or interpersonal conflict. In contrast, job resources involve the physical, psychological or organisational aspects of work that help employees cope with demands and foster personal and professional development. The examples include job autonomy, social support, constructive feedback and opportunities for learning. The JD–R framework outlines two parallel processes that shape employee functioning: the health impairment process (triggered by high demands and a lack of resources), and the motivational process (activated by access to sufficient resources).

In later theoretical extensions, Bakker and Demerouti (2024) introduced the concepts of the “loss spiral” and the “gain spiral.” The former one emerges when employees are exposed to chronic job demands while lacking the resources needed to manage them, which leads to the escalation of psychological strain, emotional exhaustion and, eventually, burnout. This deterioration in well-being may result in lower job satisfaction, increased absenteeism and higher turnover intentions. Conversely, the gain spiral describes a dynamic in which access to resources promotes work engagement and motivation, facilitating the accumulation of further resources and the development of competencies. As a result, employees not only experience greater job satisfaction and a sense of efficacy, but also receive increased investment from the organization, such as developmental opportunities. From this perspective, employee well-being is understood as a dynamic outcome shaped by the ongoing interaction between job demands and job resources. Although the JD–R theory directly links such concept as sustainability, balanced management and well-being, no research to date refers directly to technological sustainability in the workplace and its connection with employees well-being.

A sustainable organisation is described in the current literature as one that, in its market behaviours, considers and balances market factors, efficiency, growth, and well-being at the individual, organisational, and societal levels (Eriksson et al., 2017). In a sustainable organisation, the natural, financial, human and social capitals are managed in such a way that their exploitation at present does not result in the capital's wire in the future (Dyllick and Hockerts, 2002). Taking into account the assumption that an organisation possesses both internal and external resources that can be balanced and managed, organisational sustainability has both external and internal dimensions.

The internal dimension of organisational sustainability primarily pertains to human resources management. According to the “sustainable human resources management” approach, organisational decisions should consider profit maximisation while minimising the negative impact of decisions on employees (Mariappanadar, 2003). As assumed by the JD–R theory, maintaining organisational sustainability in human resources management involves an attempt to keep a balance where negative aspects of work are countered through stimulating incentives (Kramar, 2014) and leads to the creation of an employee-oriented work climate (Annell et al., 2018).

In today's increasingly digital work environments, this balance must also account for the complex relationship between employees and technology. Several constructs have been developed to capture different facets of this human–technology interface, including Technology Acceptance (Davis, 1989), Technology Readiness (Parasuraman, 2000; Blut and Wang, 2020), Organisational Digital (Technology) Climate (Avtalion et al., 2024) and Digital Well-Being (Valkenburg, 2022; Büchi, 2024). Understanding these constructs is essential for grasping how technology adoption impacts employees' attitudes, organisational climate and, ultimately, well-being.

While all of these constructs relate to the human–technology interface, they differ markedly in their levels of analysis, focus and explicit connections with well-being. To start with, Technology Acceptance (Davis, 1989) is a construct that operates at the individual level and focuses on an individual's attitudes and intentions towards adopting a specific technology, based on their perception of its characteristics (specifically, its perceived usefulness and ease of use). In contrast, Technology Readiness (Parasuraman, 2000; Blut and Wang, 2020), while also focused on the individual, emphasises personality traits or characteristics that influence a person's general propensity to embrace or resist technology. Thus, Technology Acceptance focuses on the perception of the technology itself, whereas Technology Readiness highlights individual differences in traits that affect technology adoption more broadly. Importantly, neither construct explicitly addresses well-being.

This gap is addressed by Digital Well-Being (Valkenburg, 2022; Büchi, 2024), which also focuses on the individual but broadens the scope to explicitly address psychological and emotional well-being across all life domains affected by digital media use, both inside and outside the workplace. It reflects the overall subjective well-being of an individual in the context of their complete interaction with digital technologies.

Moving beyond the individual level, Organisational Digital (Technology) Climate (Avtalion et al., 2024) shifts the focus to the collective perspective. This construct captures employees' perceptions of organisational norms, regulations and managerial practices that facilitate or hinder technology acceptance. Notably, Organisational Digital Climate is confined to the workplace context and reflects a climate that either supports or impedes technology use. However, it does not explicitly consider well-being outcomes the way that Technology Acceptance and Technology Readiness do.

The Perceived Technological Sustainability of Organisations (PTSO), conceptualised and operationalised in this article, adopts a distinctive approach by combining individual and organisational perspectives within the workplace. It aims to assess employees' subjective experiences regarding the balance between technological demands and available resources in their work environment. The PTSO directly links organisational technology implementation and use with employee well-being, explicitly distinguishing between “Opportunities” and “Threats.” Grounded in the JD–R theory, it uniquely integrates well-being with organisational dynamics by evaluating how the organisation manages this balance during the post-implementation phase of technology adoption. Table 1 provides a structured comparison of the constructs discussed above.

As assumed by the JD–R theory, technology plays a significant role in fostering internal organisational sustainability (Chen et al., 2024) and thus enables the positive stimulation of employees to maintain health (Forberger et al., 2022), increase safety (Zhong et al., 2024), train and develop their competencies (Radhakrishnan et al., 2021), facilitate work by delegating repetitive and tedious tasks to machines (Anagnoste, 2017), balance the time spent using mobile devices (Almourad et al., 2021) and construct a hybrid space so that remote work is linked to higher work-life balance, social support and satisfaction with life (Mishra and Bharti, 2024). Nevertheless, technology in the workspace may also induce stress in employees (La Torre et al., 2019), arouse a sense of permanent control (Plester et al., 2024), force them to exceed their own limits (Bloomfield and Dale, 2015) and violate the fine line between work and private life (Wang et al., 2019). Moreover, Thurik and colleagues (2024) demonstrate that techno-overload is associated with disruptions in employees' well-being, while Ter Hoeven et al. (2016) found that employee well-being increases when technology is accessible and efficient, but it may also decrease when interruptions and unpredictability are on the rise. These assumptions are further corroborated by Kot (2022) as well as Hang and colleagues' (2022) research on the impact of technology on employees, suggesting the existence of another duality, namely techno-stressors and technostress inhibitors. Techno-stressors include techno-overload, techno-invasion, techno-complexity, techno-insecurity and techno-uncertainty, i.e. those factors related to technology that negatively affect employees. Technostress inhibitors include literacy facilitation, technical support provision and involvement facilitation, i.e. technology-related factors that positively influence employees. Additionally, Mäkiniemi (2022) shows that technology can enhance work engagement if it facilitates tasks, enables progress, provides novelty, supports teamwork and creates a pleasant working atmosphere. The juxtapositions of these pieces of research suggests that technology can restore or upset balance in an organisation, and positively or negatively influence its employees.

These findings suggest that the sustainable use of technology in the workplace is significantly associated with employee well-being. Within this context, the role of managers who adopt the well-being-oriented management approach is to monitor the extent to which workplace technologies remain non-intrusive and do not contribute to the erosion of resources or the demotivation of employees.

In the literature, there is no scale measuring the perceived technological sustainability of organisations. Nor is there a single theory indicating directly this construct and its components, although earlier authors had provided indirect evidence of its existence (Modliński et al., 2023; Hang et al., 2022; Ter Hoeven et al., 2016). Earlier studies show that technology may differently influence employees, depending on the context (Gardner et al., 2017), their level of expertise (Allen and Choudhury, 2022) or workspace type (Gonsalves, 2023). Thus, various employees may perceive technological sustainability differently, and this is why their perceptions regarding technological sustainability should be more examined. Although previous authors had shown such objective factors related to the adoption of technology into organisations that affect employees positively or negatively, no one had previously paid attention to employees' perceptions of technological sustainability, much less attempted to measure it. Through numerous fieldwork endeavours and conversations with employees in contemporary organisations, the authors of this article have observed that new technologies may either disrupt internal organisational sustainability or aid in achieving it. Moreover, during business courses and workshops conducted by the authors, employees frequently mentioned the concept of a technologically sustainable organisation, which lacks a definitive description in the current literature (Vacchi et al., 2021). Embracing the interpretative approach of Barbara Czarniawska's (2014)“window of opportunities”, the authors decided to delve into the meaning of a technologically sustainable organisation by performing the groundwork. Operating under the assumption that good theory comes from practice, the authors conducted multi-stage research on the perceived technological sustainability of organisations, exploring the meaning of this construct and the symptoms suggesting that an organisation is either technologically sustainable or technologically unsustainable. The ultimate research goal behind the authors' endeavour was to develop a scale that would enable the measurement of the perceived technological sustainability of an organisation by its employees.

The development of the PTSO procedures followed a two-stage process was: construction and validation. Therefore, the study (1) generated a set of items, (2) revealed the factor structure, (3) tested the factorial composition of the generated items by using Exploratory Factor Analysis (EFA, N = 271) and (4) confirmed the invited construct structure (CFA, N = 376). In both cases, the sample sizes met the requirements for scale development procedures (Hoe, 2008; Pett et al., 2003). The procedures, data collection and results are described in detail in the next subchapters. Based on the analysis, the two-dimensional PTSO scale was developed. Each stage of the research was conducted in Poland.

The aim of the initial stage of the research was to explore employees' perceptions of technological sustainability in their organisation. To conceptualise the construct, seven focus group interviews were conducted with 56 corporate employees (29 of whom were female), gathered through the snowball sampling method. The cross-section of nationalities was as follows: 32 of the respondents were Polish, 8 were Ukrainian, 7 were Turkish, 4 were Indian, 3 were Pakistani, 1 was German and 1 was Spanish.

The participants ranged in age from 20 to 49 years old; the absence of older individuals resulted from the snowball sampling approach and constitutes a limitation of this phase. To address this gap, the subsequent survey phase incorporated respondents aged up to 77 years old, thereby enabling the inclusion of more diverse age-related perspectives during the validation process. On average, a focus group interview session lasted 30–40 min. During the semi-structured interviews, the participants were asked to define what a technologically sustainable organisation means to them and, then, to identify the symptoms of such sustainability. The examination of the obtained data in chronological order revealed that all major thematic categories had emerged by the third session. The remaining focus groups reiterated these core themes without yielding novel insights, thereby indicating that thematic saturation had been reached. Consequently, it was deemed methodologically appropriate to discontinue additional focus group sessions at that point.

The data obtained from the focus group interviews were independently analysed by two researchers in order to extract and name the components standing behind the perceived technological sustainability of an organisation. Each researcher independently analysed the collected material to identify recurring themes. Following this individual coding phase, both coders engaged in joint discussions in order to synthesise their findings. Any initial discrepancies were resolved through discussion, ensuring that each of the fourteen identified components was clearly grounded in the focus group data. This consensus-based approach, combined with the established coding procedure adapted from Modliński and colleagues (2022), enhances the transparency and methodological rigour of the qualitative analysis.

Based on this approach, the researchers identified fourteen components describing the perceived technological sustainability of an organisation. As inferred from the literature, the respondents utilised both restraining and hindering forces to determine when an organisation is technologically sustainable. The following components influencing their perceived technological sustainability were found in the interviewees' statements: (1) the organisation develops its employees' competencies to use implemented technologies; (2) the technology used in the organisation positively stimulates human creativity; (3) the organisation puts people at the centre, while technology is a tool to support them; (4) the organisation brings employees closer and integrates them through technology; (5) the organisation increases motivation through technology; (6) the organisation does not increase the number of responsibilities when technology is implemented; (7) the implemented technology has positive or neutral influence on the body and mind of the employees; (8) the technology adopted by the organisation positively stimulates communication between co-workers; (9) the organisation does not contribute to the exclusion of less IT-focused employees despite adopting technology; (10) loyalty and commitment to the company are both increased due to the adopted technology; (11) the employees are willing to stay in the company due to the technology used; (12) the organisation uses technology that is complementary and not substitutive to the human workforce; (13) technology does not perform tasks which the employees consider to be typically human; (14) technology has positive impact on perceived joy from work.

The initial pool of items corresponding to the aforementioned constructs was elaborated. These items were assessed in terms of content validity and linguistically verified by psychologists, psychometrists and management experts involved in digital workforce adoption (five people altogether). These experts were asked to add or remove a given item if it was not in line with their perception. After this phase, no items were added and fourteen items were removed. Due to the requirements to construct a tool guaranteeing a more balanced measurement, it was decided to formulate half of the items in a positive way (emphasising a benefit/chance) and half of the items in a negative way (emphasising a difficulty/threat) (Clark and Watson, 1995). Reversing some items allowed the diversity of answers (the respondents must read the questions with understanding) and helped minimise the impact of random errors (the respondents are unable to predict which questions will be reversed). Subsequently, the authors of this study consulted such a pool of items with external experts (different from those involved in the earlier stage) as well as with students in order to check their understanding. The final pool used in the analysis consisted of fourteen items (see  Appendix).

Eight hundred and seventy-two subjects (48.2% female) voluntarily participated in the study. The participants were recruited via advertisements posted in various Internet groups on social networking sites and discussion boards. Their age (657 valid data components) ranged from 18 to 77 (M_age = 42.22, SD = 13.08). The participants varied in terms of the education level. The sample allowed the detection of the population correlation coefficient of r > 0.16 with the power of 1 – β = 0.90 and the level of α = 0.05 (two-tailed test). The participants were informed that the aim of the study was to learn about their opinions on the adoption of technology in business organisations. They completed three scales: the PTSO, the TRI 2.0, and the TENS-Life Scale. Finally, they provided information about their gender, age, education, seniority and workplace.

The initial sample (N = 872) was checked for biased responses. The study excluded from the statistical analyses any respondents who had provided identical responses across all items on one of the measures (the Sustainable Scale, the Technology Readiness Index 2.0, or the TENS-Life Scale). The final sample included in the statistical analyses consisted of 647 individuals (320 females and 327 males). Age was measured as an ordinal variable with six values indicating age intervals: 15–17 years old, 18–24 years old, 25–34 years old, 35–44 years old, 45–54 years old and 55 years old or more. In the final sample, Me_age = 25–34 years old and Mo_age = 18–24 years old, with Min. = 15–17 years old and Max. = 55 years old or more.

All statistical analyses were conducted using the R programme and RStudio, harnessing various packages for comprehensive analysis. The tools included (in alphabetical order): corrplot for correlation visualisation, dplyr for data manipulation, haven for data import/export, Hmisc for advanced statistical procedures, lavaan for Confirmatory Factor Analysis, mvnTest for multivariate normality testing, PerformanceAnalytics for performance analysis, psych for psychometric analysis, RColorBrewer for colour schemes and semTools for structural equation modelling tools. To ensure transparency and reproducibility, the authors meticulously deposited the dataset and R-scripts encompassing all analyses presented in the results section into the following repository [https://doi.org/10.5281/zenodo.15805677]. This action guarantees the accessibility of the methodology and enables fellow researchers and practitioners to verify the results.

The Perceived Technological Sustainability of Organisations (PTSO) – this scale consisted of 14 items. The participants responded to the questionnaire's items on a 7-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree.

The Technology Readiness Index 2.0 (TRI 2.0) – the Polish version of the TRI 2.0 was used (Rudnicka, 2021). It consisted of 16 items grouped into four equal subscales measuring the dimensions of readiness towards technology: optimism (TRI_OPT) and innovation (TRI_INN) as activators, as well as uncertainty (TRI_INS) and discomfort (TRI_DIS) as inhibitors. The participants responded to the questionnaire's items on a 5-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree. The final result of the scale was obtained for the global construct of readiness towards technology (TRI_OG) as well as four subscales. In this study, the reliability scores of the scale range from α = 0.70 (TRI_INS) to α = 0.81 (TRI_OG).

The TENS-Life Scale (TLS) – this scale consisted of 38 items designed to measure the broad effects of a technology on the satisfaction and frustration of psychological needs in everyday life (Burnell et al., 2023). This scale was improved and validated as part of the METUX model of technology interaction that measures psychological needs satisfaction and frustration at life levels. The scale's construction is based on the Self-Determination Theory (Ryan and Deci, 2000), according to which people have three basic psychological needs that mediate the effects of technology on well-being and motivation: autonomy, competence, and relatedness. The TLS consists of six subscales, with each measuring one psychological phenomenon: autonomy satisfaction (TLS_AS; 5 items), autonomy frustration (TLS_AF; 5 items), competence satisfaction (TLS_CS; 7 items), competence frustration (TLS_CF; 7 items), relatedness satisfaction (TLS_RS; 7 items) and relatedness frustration (TLS_RF; 7 items). The participants responded to the questionnaire's items on a 7-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree.

The full sample (N = 647) was divided into two subsamples using a pseudo-random number generator with the Mersenne Twister method. The first one was used (N = 271) to conduct Exploratory Factor Analysis (EFA), while the second one (N = 376) was utilised for Confirmatory Factor Analysis (CFA).

To ensure the robustness of the assessment, a comprehensive psychometric strategy was employed, involving a series of statistical analyses. In the first step, on the first subsample (N = 271), inter-item correlation analysis and subsequently factor-extraction analyses were performed based on Kaiser's eigenvalues-greater-than-one rule (K1), parallel analysis (PA), optimal coordinates (OC) and the acceleration factor (AF). The next step involved EFA with promax rotation. Subsequently, the reliability analysis utilising both Cronbach's alpha and McDonald's omega was performed. The subsequent phase of the analysis was conducted using the second subsample (N = 376), encompassing CFA as well as comprehensive examinations of validity.

In the first step, the authors computed the correlation coefficients between the PTSO's items (Table 2).

Items formulated as opportunities – i.e. S1, S4, S5, S8, S10, S12 and S14 – correlate significantly and positively. Pearson's coefficients range from 0.24 (for the S4–S12 pair) to 0.64 (for the S5–S14 pair). Correlations between items formulated as challenges – i.e. S2, S3, S6, S7, S9, S11 and S13 – are also significant and positive, ranging from 0.16 (for the S3–S11 pair) to 0.59 (for the S7–S9 pair). Interitem correlations between opportunity-formulated and challenge-formulated items are predominantly insignificant or significantly negative (ranging from −0.13 to −0.33), except five cases (the following pairs: S1–S3, S3–S5, S3–S10, S4–S13) with significantly positive correlations (ranging from 0.12 to 0.27). In the second step of the analysis, the traditional EFA was conducted as well as, subsequently, factor-extraction analyses.

In the case of EFA, Bartlett's test of sphericity was significant: χ²(91) = 1402.41. P < 0.001. The overall Kaiser–Meyer–Olkin criterion (KMO) exceeded the acceptable minimum value and equalled 0.852. Both statistics suggest that the data is suitable for factor analysis. The above goodness-of-fit criteria set the stage for the next step of EFA – one related to estimating the number of factors that build the Sustainability Scale – using Kaiser's eigenvalues-greater-than-one rule (K1), parallel analysis (PA), optimal coordinates (OC) and the acceleration factor (AF).

All factor-extraction analyses consistently confirmed that the optimal number of factors is two (Figure 1). Consequently, in the next stage, EFA with Promax rotation and a two-factor solution was undertaken to identify items assignment to the factors, based on factor loadings (Table 3).

The factor analysis allowed assigning all items formulated as Opportunities – i.e. S1, S4, S5, S8, S10, S12 and S14 – to the first factor (F1), while all items formulated as Threats – i.e. S2, S3, S6, S7, S9, S11 and S13 – to the second factor (F2). Both factors explain a significant and moderate portion of the variability within the items, since the common part accounted for the (CAF) index indicates 0.50; df = 64 (Table 4).

To confirm the earlier decision considering the two-factor solution, an additional analysis was performed which involved averaging results from multiple EFAs conducted with oblique rotations (average EFAs). The analysis investigated the alignment of the items identified within the previous step of the analyses with their respective factors. Examining confidence intervals of the items' factor loadings aimed to provide insights into the robustness and consistency of the identified factor structure (Figure 2).

The plot in Figure 2 displays the average pattern of items' loadings coefficients and their ranges. Reviewing the graph depicted above offers insights into the relative positioning of the Sustainability Scale's items within the two extracted factors, especially regarding the distribution of their average values alongside their minimum and maximum values. The results confirm that items formulated as opportunities (S1, S4, S5, S8, S10, S12 and S14) align with the first factor (F1) rather than the second factor (F2), while items formulated as challenges (S2, S3, S6, S7, S9, S11 and S13) align with the second factor (F2) rather than the first factor (F1).

In the next step, the reliability of the PTSO's subscales was tested. The used indices included Cronbach's alpha and Omega total coefficient (see Table 5).

The reliability analysis was based on the criteria for goodness-of-fit, which included Cronbach's alpha >0.80 and McDonald's Omega >0.70. The reliability indices for the full scale and for both subscales extracted based on the two-factor model are acceptable (Cronbach's alpha ≥0.80 and McDonald's omega ≥0.80). The Sustainability Scale as a two-dimensional measure has acceptable reliability (Cronbach's alpha ≥0.80 and McDonald's omega ≥0.75).

The subsequent phase of the analysis assessed the adequacy of the model to the data for the both unidimensional and two-dimensional versions of the Sustainability Scale. For each model assessed in this analysis, the goodness-of-fit criteria comprised the significance of the Chi-square test along with the Chi-square/df ratio, the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI), the Tucker–Lewis Index (TLI) and the Standardised Root Mean Square Residual (SRMR) (see Table 6). An acceptable fit was determined by the following benchmarks: a Chi-square/df ratio <3, CFI and TLI values equal to or exceeding 0.90, as well as RMSEA and SRMR values below 0.08. Meanwhile, the indicators of a strong fit include a Chi-square/df ratio below 2, a CFI value equal to or surpassing 0.95, as well as RMSEA and SRMR values below 0.05.

Based on the findings from the assessments of normality – which encompassed Mardia's multivariate skewness and kurtosis tests, the Henze–Zirkler Test, and the Doornik–Hansen Test – parameter estimation was conducted using the Maximum Likelihood with Robust Standard Errors (MLR) method. This choice was made due to the absence of multivariate normality, as recommended by Muthen and Muthen (2017). The MLR, an estimation technique based on rescaling and suitable for non-normally distributed data, provides standard errors and a Chi-square test, distinguishing it from similar methods.

The analysis of goodness-of-fit indices showed that in the case of the one-factor version of the method, the obtained parameters (CFI, TLI, RMSEA and SRMR) were far from satisfactory and did not allow for accepting this solution (see Table 6). The analysis of the two-factor version showed the almost acceptable goodness-of-fit properties: χ² (76) = 207.69, p < 0.001, χ²/df = 2.73, CFI = 0.907, TLI = 0.889, RMSEA = 0.076 95% CI [0.064, 0.088], SRMR = 0.081. The difference in the Chi-square values between both solutions was statistically significant: Δχ² (1) = 454.26, p < 0.001. For the two-factor version of the PTSO, the standard errors of items' loadings were within acceptable limits (see Table 7).

While the two-factor model proved to be significantly better fitted to the data when compared to the one-factor model, due to the indicators bordering the acceptable range, a decision was made to verify modification indices in order to examine potential correlations within the residuals. The analysis provided insights into the correlation of residuals between the pairs of items 4–8 and 4–10 for Factor 1. as well as the pairs 3–11 and 3–13 for Factor 2. Allowing these correlations within the residuals resulted in acceptable fit indices of the modified two-factor model (see Figure 3): χ² (72) = 141.48, p < 0.001, χ²/df = 1.97, CFI = 0.951, TLI = 0.938, robust RMSEA = 0.057 95% CI [0.043, 0.070], and SRMR = 0.076.

A gender measurement invariance analysis was then conducted by selecting men and women from the full sample (N = 647). The goodness of fit was assessed at each stage of the measurement invariance analysis (configural, metric, scalar, strict) using the Chi-squared test and several fit indices including CFI, TLI, RMSEA and SRMR. However, the Chi-squared statistic is known to be sensitive to minor deviations from the model, which may not be practically meaningful. Fit indices are considered more appropriate for evaluating model fit in measurement invariance analysis. An acceptable model fit in the subsequent steps of the analysis was based on specific criteria: CFI and TLI ≥0.90, as well as RMSEA and SRMR <0.08.

To test metric invariance followed by scalar and strict invariances, the ΔCFI, ΔRMSEA and ΔSRMR cutoffs were applied. Since the sample sizes for both male and female groups were adequate (N > 300) and there were no statistically significant differences in group sizes between males and females, the criteria were adopted for large and equal group sizes: ΔCFI ≤0.010, ΔRMSEA ≤0.015 and ΔSRMR ≤0.030 for metric invariance; as well as ΔCFI ≤0.010, ΔRMSEA ≤0.015 and ΔSRMR ≤0.010 for scalar and strict invariances.

This approach required meeting at least two out of three criteria (ΔCFI, ΔRMSEA, ΔSRMR) at each analysis stage to establish measurement invariance. In examining gender measurement invariance, CFA was conducted for the two-factor model that allowed correlations between residuals separately for men and women to assess the model fit within each subgroup. Subsequently, a standard gender measurement invariance analysis was performed for the entire sample, beginning with the configural invariance stage.

The modified two-factor model demonstrated the acceptable fit within the male group, meeting the criteria of CFI and TLI ≥0.90 as well as RMSEA and SRMR ≤0.08. Within the female group, on the other hand, the TLI and SRMR were slightly beyond the acceptable limits (see Table 8). Following the gender measurement invariance analysis, the configural, metric, scalar, and strict models met the criteria for an acceptable fit, with CFI and TLI ≥0.90 as well as RMSEA ≤0.08, but SRMR stayed above the limit of 0.08 in all cases. In assessing metric gender measurement invariance, all cut-off criteria were successfully met: ΔCFI ≤0.010, ΔRMSEA ≤0.015, and ΔSRMR ≤0.030. Moving on to scalar and strict gender measurement invariances, all cut-off criteria were also met: ΔCFI ≤0.010, ΔRMSEA ≤0.015 and ΔSRMR ≤0.010. Based on the evaluations of the cut-off criteria, it may be concluded that strict gender measurement invariance was achieved, indicating that the residual variances of the observed scores not attributed to the factors remain consistent across men and women.

In the final step, to assess construct validity, we examined the relationships between the two PTSO factors and external criteria that are theoretically expected to be associated with them, specifically: technology readiness and need satisfaction/frustration (as the indicators of convergent validity), as well as age (as an indicator of discriminant validity). A convergent validity analysis was performed on the same subsample which was used in Confirmatory Factor Analysis (N = 376). In the divergent validity analysis, the sample size was smaller (N = 249) due to the lack of information regarding age (in years) in some cases.

The correlation matrix for the convergent and discriminant validity of the PTSO is shown in Table 9. Factor F1, which groups items formulated as Opportunities, shows a high and significantly positive correlation with TLS_AS, TLS_CS and TLS_RS, as well as a significantly negative but very weak correlation with TLS_AF, TLS_CF and TLS_RF. Factor F2, which groups items formulated as Threats, shows a high and significantly positive correlation with TLS_AF, TLS_CF and TLS_RF, but does not correlate with TLS_AS, TLS_CS and TLS_RS. This pattern of correlations between the PTSO and the TLS indicates good convergent validity for the former.

The Opportunities factor shows a moderate and significantly positive correlation with the overall TRI, TRI_OPT and TRI_INN, as well as a significantly negative but weak correlation with TRI_INS and TRI_DIS. On the other hand, the Threats factor weakly correlates negatively with the overall TRI and very weakly negatively with TRI_OPT, but does not correlate with TRI_INN. Contrary to the Opportunities factor, the Threats factor weakly correlates positively with TRI_INS and TRI_DIS. The observed pattern of correlations between both PTSO factors and the TRI 2.0 also indicates good convergent validity for the PTSO.

Neither the Opportunities nor the Threats factors correlate with the age of the participants. The lack of correlation between the PTSO and the participants' age indicates good discriminant validity for the scale.

The rapid advancement of new technologies has increasingly positioned them as a fundamental component of the organisational environment shaping employee well-being (Urien and Erro-Garcés, 2024; Lopes et al., 2022). While extensive research highlights the impact of resource balance on employee well-being (Dahlsrud, 2008; Fu et al., 2014; Zhou et al., 2017), significantly less attention has been given to the internal technological sustainability of organisations, representing a critical research gap. This paper addressed this gap, offering theoretical and practical contributions to the fields of employee relations and sustainable management.

Firstly, our study conceptualised a technologically sustainable organisation as one that ensures that technology adoption does not negatively affect employees' workload, well-being or job satisfaction but, rather, supports a balanced and inclusive work environment. In this context, key characteristics include the positive effects of technology on employees' skill development (i.e. technology is used to develop competencies and enhance creativity), support for collaboration among employees (i.e. technology facilitates communication and fosters closer relationships), support for employees' mental and physical well-being (i.e. technology does not increase workload or cause strain), inclusivity (i.e. technology does not exclude less tech-savvy employees), a complementary role of technology relative to the human workforce (i.e. technology complements rather than replaces human labour) and the preservation of human tasks (i.e. technology does not supplant tasks that employees enjoy and perceive as inherently human). It is important to underline that technological sustainability is subjective rather than objective, reflecting employees' various perceptions and experiences that may change over time. These elements are consistent with earlier findings suggesting that maintaining organisational sustainability in human resources management involves striking a balance in which the negative aspects of work are countered by stimulating incentives (Kramar, 2014). While previous research had indicated that – in order to maintain internal organisational sustainability – organisations should develop employees' long-term potential, enhance participation, cultivate partner-based relationships between employees and employers, ensure conducive working conditions through work-life balance and create a friendly working environment (Stankevičiūtė and Savanevičienė, 2019), our study highlights that technology plays a key role in this process.

In addition to the above, previous studies had suggested that technology can stimulate employee relations both positively (Chen et al., 2024) and negatively (Lopes et al., 2022). However, they did not directly associate these effects with a technological balance point that managers should actively pursue to maintain employee well-being. Thus, measuring employees' perceptions, expectations and attitudes regarding the scale and type of utilised technologies becomes a crucial dimension for ensuring organisational well-being. Unfortunately, few organisations systematically assess how employees perceive the technological infrastructure of their workplace and how these perceptions impact their well-being. This represents a significant limitation in designing effective interventions to support employee well-being (Kedziora et al., 2024). The lack of such assessments may stem from limited awareness of the connection between technological design and well-being, or from the absence of reliable instruments to measure technology-related well-being. Although some researchers have referred to phenomena such as techno-overload (Thurik et al., 2024), techno-inhibitors, and techno-stressors (Kot, 2022; Hang et al., 2022), what was missing was the construct that would directly connect the technological infrastructure of an organisation with its employees' well-being as well as a scale measuring such technologically oriented well-being.

By synthesising insights from previous research on technology's influence on employee well-being and organisational dynamics (Eriksson et al., 2017; Zink, 2014), our study provides a comprehensive framework for assessing perceived technological sustainability as a subjective construct. The exploration of technological sustainability is particularly pertinent in the contemporary digital age, where technology plays an increasingly pivotal role in organisational operations.

Our conceptualisation and operationalisation of the Perceived Technological Sustainability of Organisations (PTSO) is grounded in the JD–R theory. This framework assumes that every occupation is characterised by specific job demands and job resources (Bakker and Demerouti, 2007). The Opportunities dimension of the PTSO scale – encompassing positive technological influences such as skill development, collaboration, creativity, enhanced communication, motivation and both psychological and physical well-being – aligns with job resources. These elements may activate the motivational process and initiate the gain spiral, fostering employee engagement and the development of professional competencies.

Conversely, the Threats dimension of the PTSO scale – which includes negative aspects such as increased workload, social exclusion, the replacement of human tasks or technostress – corresponds directly to job demands or stressors. When such demands are not adequately counterbalanced by resources, they may trigger the health impairment process or the loss spiral, ultimately leading to psychological strain, exhaustion and burnout. This dual structure shows that technology is not neutral; it has the capacity to act both as a resource and as a demand, reinforcing the importance of balanced, well-being-oriented management.

Importantly, our results extend beyond the PTSO by demonstrating meaningful correlations with both the TRI 2.0 and the TLS. Employees who perceive their organisation as technologically sustainable also score higher on optimism and innovativeness – which are the enabling dimensions of readiness – while reporting lower discomfort and insecurity. This suggests that readiness acts as the psychological amplifier of technological sustainability – those who are more open to technology interpret organisational systems as more supportive of their well-being, whereas low readiness may cause even well-designed systems to be experienced as threatening. These findings reinforce meta-analytic evidence (Blut and Wang, 2020) that technology readiness is the central determinant of adoption outcomes and highlight its role in shaping subjective well-being at work. In particular, higher scores on the Opportunities factor of the PTSO were associated with optimism and innovativeness in the TRI, whereas higher scores on the Threats factor were linked to discomfort and insecurity. This demonstrates that the readiness to adopt technology moderates the impact of organisational systems on well-being and may either buffer or exacerbate technostress. Our results further suggest that the PTSO helps to distinguish between situations where technology generates technostress and situations where it produces techno-eustress, i.e. a positive form of stress which can stimulate competence development, resilience, and engagement. This interpretation resonates with the dual systems perspective (Cenfetelli and Schwarz, 2011), according to which technologies simultaneously activate enabling and inhibiting forces shaping employee outcomes.

At the same time, the correlations with the TLS emphasise that technology in organisations is deeply intertwined with employees' psychological needs, as defined by the Self-Determination Theory (Ryan and Deci, 2000). Opportunities identified by the PTSO – such as communication support, competence development and motivational enhancement – were positively associated with autonomy, competence, and relatedness satisfaction. Threats, by contrast, were strongly linked to the frustration of these needs. This convergence provides robust evidence that the PTSO is not only a technological construct but also a well-being construct, capturing whether technology supports or undermines the basic psychological mechanisms of human flourishing.

From the perspective of Positive Organisational Psychology 2.0 (van Zyl et al., 2024), which emphasises both individual flourishing and systemic sustainability, our findings show that technological infrastructures should be managed as part of organisational well-being strategies. The PTSO scale provides a tool for identifying whether digital systems contribute to resource gain spirals (e.g. mastery, collaboration, empowerment) or loss spirals (e.g. stress, exclusion, demotivation). By integrating JD–R, SDT and POP 2.0, this study offers a systemic lens on employee well-being in technologically intensive workplaces, underscoring that technology management must address not only operational efficiency but also the psychological experience of its users.

While the JD–R theory is frequently used to examine sustainable work, management practices and employee well-being, previous research had not directly addressed technological sustainability in the workplace and its implications for well-being. Our study addresses this gap by developing and validating the PTSO scale, which captures the impact of employees' subjective perceptions of the positive and negative impacts of workplace technology on their well-being in line with the JD–R's emphasis on the dynamic interplay between job demands and resources.

Finally, our study makes a significant practical contribution by providing managers with a comprehensive framework to assess and enhance technological sustainability within their organisations. Specifically, the research introduces a measurement tool that captures employees' perceptions of how technology impacts their workload, well-being and job satisfaction – information that is critical for developing technological infrastructure. Earlier approaches to workplace technology development had focused primarily on designing adoption methodologies aimed at maximising positive attitudes and intentions at the initial stages of implementation. One example is the Collaborative Robot Implementation approach, which emphasises involving employees in the early stages of the adoption process in order to enhance their experience during the usage phase (Kedziora et al., 2024). However, fostering positive perceptions at the outset does not necessarily translate into satisfaction during actual use. Furthermore, employees may not initially recognise the implications of specific technologies for their well-being, as such effects often become apparent only through direct experience. The PTSO framework supports managers in analysing their employees' experiences during the usage phase and can therefore serve as a valuable tool for guiding technology implementation once it is already in use. By delineating the dual aspects of technology (Opportunities that foster skill development, collaboration and creativity versus Threats that can induce stress and overload), the framework offers actionable insights for tailoring technology management practices. Based on our findings, managers should regularly evaluate their employees' experiences with technological systems, implement targeted training programmes to bolster technology-related skills and design work processes that mitigate stressors while enhancing collaborative opportunities. These actions will not only help mitigate the negative impacts of technology adoption but also maximise its benefits, ultimately cultivating a more resilient and sustainable work environment.

The design and findings of the present study open multiple avenues for future research. First, although the qualitative phase achieved thematic saturation within a diverse international sample of employees, it did not include individuals over the age of fifty. Future qualitative research could specifically target older workers to gain deeper insight into their perceptions of new technologies and their role in fostering technological sustainability within organisations.

Second, the PTSO scale should be validated across various industry sectors and cultural contexts to assess its generalisability beyond the current sample, which was composed primarily of participants from Poland and neighbouring countries. Longitudinal research would be particularly valuable, e.g. by tracking how workplace technological changes or well-being initiatives influence the PTSO scores over time. Such studies could also investigate whether elevated levels of the PTSO are associated with positive outcomes such as increased employee engagement, improved mental health or higher retention rates.

Third, future studies should examine how individual-level factors shape employees' perceptions of an organisation's technological sustainability. Of particular interest are the potential influences of personality traits, prior technological experience, and educational background. It would also be important to explore differences in perception among employees within the same organisation and identify the factors contributing to such divergences.

Moreover, further research is needed to investigate the consequences of an organisation being perceived as technologically unsustainable. This includes examining the impact on internal dynamics – such as interpersonal relationships and team cohesion – as well as on external dimensions, such as the organisation's employer brand and reputation.

Finally, future studies should aim to identify effective managerial responses following a decline in perceived technological sustainability. Research in this area could evaluate which interventions are most successful in restoring technological credibility and enhancing employee well-being in the context of the emerging or evolving digital tools.

• S1.

  The company provides me with the development of skills in the use of implemented technologies

• S2.

  Technology in my company stifles my creativity

• S3.

  Everything in my company revolves around technology, and I am just its complement.

• S4.

  Thanks to the technology used in my company, I feel that we are closer to each other as employees

• S5.

  The technology implemented in my company motivates me to work

• S6.

  Implementing new technologies in my company means that I have more responsibilities

• S7.

  The technology used in my company is making me feel worse (e.g. I feel pain in my body)

• S8.

  Thanks to the technology used in my company, it is easier for me to communicate with my colleagues

• S9.

  I feel technologically excluded in my company

• S10.

  The technology introduced in my company makes me feel more connected to my employer

• S11.

  My company's approach to new technologies makes me think about changing my job

• S12.

  The technologies implemented by the employer complement my competences

• S13.

  In my company, technology performs tasks that, in my opinion, should be performed by a human being

• S14.

  The technology implemented in my company makes my work more enjoyable