Constructing and validating a scale for technostress and employee behavior: evidence from business schools in a developing country

Abstract

This research aimed to adapt and validate the Techno-Stress Construct in Higher Education (TCHS) for business school faculty in Pakistan. The study involved 300 faculty members from Khyber Paktunkhawa (KP) and Islamabad, who helped translate and adapt the TCHS into a 16-item Pakistani version.

Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) using AMOS 23 were employed to validate the factor structure. EFA identified four factors: techno-overload, techno-invasion, techno-complexity, and techno-uncertainty. CFA confirmed the structural model with excellent fit metrics.

Reliability analysis showed strong Cronbach’s alpha coefficients for both total and subscale measurements. Construct validity tests indicated significant relationships between TCHS components and faculty emotional exhaustion, work-life balance, and commitment.

The findings suggest that the adapted TCHS is a valid tool for identifying techno-stress among educators in Pakistan, which can aid in developing interventions to enhance faculty welfare and productivity. Future research should explore its applicability across different populations.

Introduction

In the aftermath of the COVID-19 pandemic, the rapid advancement of technology in education has transformed classrooms into digital environments, where technostress emerges as a pervasive challenge. The influence of technology on higher education has garnered significant interest in recent years. The increasing use of technology has fundamentally changed the way people work, learn, and play in the information age, as highlighted by [1]. The recent development of technology has automated several processes, resulting in the emergence of newer occupations and job responsibilities, frequent shifts in career paths, and reducing the distinction between work and personal life. Information technology has become essential in work environments, influencing how the entire nature of tasks performed [2]. The rapid shift to remote teaching and the extensive use of technology-mediated communication during the COVID-19 pandemic have greatly intensified technostress among faculty and teaching staff in schools and universities globally [3].

Technostress is characterized by negative psychological and physiological effects from technology use, has been extensively studied across different contexts. Research indicates that technostress can lead to reduced satisfaction, commitment, and performance among individuals [4–6]. Research by [7] found that university requirements related to Information and Communication Technology (ICT) and its suitability for teachers’ work are critical factors affecting job performance. The results of [8] emphasized the importance of studying technostress in today’s knowledge-based society, noting that the flood of information and increased use of technology can lead to frustration, anxiety, and fear.

The study of [9] focused on the negative impact of technology-enabled teaching on students’ satisfaction and performance, underscoring the need to identify and support students facing technostress. Similarly, the study [10] on Brazilian public higher education students highlighted the presence of technostress, which was found to have a significant relationship with generalized anxiety disorder, affecting students’ quality of life. Researchers [11] investigated the role of technostress, cognitive appraisal, and coping strategies on students’ learning performance, highlighting the impact of technology-enhanced learning environments on students’ satisfaction and performance.

In addition to students, teachers in higher education settings are also susceptible to technostress. The study of [12] examined the relationship between technostress levels and job satisfaction of teachers during the COVID-19 period, focusing on distance education. Further [13] assessed burnout and technostress experiences among higher education teachers and researchers during the pandemic, highlighting challenges related to education, training, decent work, and well-being. The study of [14] explored the bright side of technostress in higher education, identifying antecedents and outcomes of techno-eustress among teachers.

In Pakistan, an underdeveloped country, the higher education sector similarly shifted toward online education in response to the pandemic. This sudden transition posed significant challenges, as many faculty members were unfamiliar with frequent technology use, resulting in increased stress, anxiety, depression, emotional exhaustion, and decreased commitment, satisfaction, and innovative work behavior. The quest for management education in Pakistan is usually challenging, thus exerting significant academic stress on learners and instructors. Business school students are supposed to be academically and professionally competitive, thus putting the students in a competitive and stressful environment. This primacy of academic achievement and career progression increases stress rates among students and faculty members.

The results of [15] identify that digital competencies help to depict the predictors for technostress amongst individuals in the context of higher education. More specifically, the research conducted for the article implied that increased levels of operational digital competence helped to overcome the main difficulties related to technology utilization and decrease the detrimental effects of technostress. A study by the [16] noted that technostress was positively associated with anxiety-depression among college students, thus confirming the fact that technostress has adverse impacts on students’ health and wellbeing.

Business schools in Pakistan can use the job demands-resources model from [17] as an effective model to study technostress among their faculty members. This framework divides working characteristics into two essential categories including work demands that represent employee pressures and work resources that provide them with support. Authorization between work demands and resources becomes vital for faculty wellbeing. Increased technostress will appear among faculty members when technology expectations grow beyond their available support systems.

According to the person-environment fit theory [17–19] stress develops when people cannot match their needs with their environmental resources [20]. The misalignment between faculty members and their technological environment defines how we view technostress in this situation. Technology creates stress for faculty members through organizational expectations and works together with the collaborations between teaching staff members. Both insufficient technological resources and mismatched faculty skills in technology lead to stress that reduces performance while affecting their health.

The overall techno-eustress experienced by teachers, along with the effectiveness of technology interventions in teaching and learning processes, is influenced by four specific categories of factors: the technological environment, individual characteristics, organizational nature, and the supply context. A synthesis of the research and literature has indicated that improving the technology-related competencies of teachers helps to tame these negative effects left to enhance the applicability of such digital competencies into teaching contexts [21]. Furthermore, the level of technostress affects views held by the teachers on the ICT effectiveness, pointing out the necessity of evaluating the teachers’ competence towards technology [22].

The study of [23] highlights the importance of digital transformation to learn English language, while the findings from [24] indicate that student satisfaction with online learning depends upon various influential factors.

Besides, factors like information and communication technology self efficacy and perception, and teaching competency enhance use of technology among university lecturers and make a difference in the digital competence of higher learning institutions [25].

In conclusion, the literature on technostress in higher education highlights the urgent need for research and initiatives to address this growing issue. Educational institutions should implement measures to minimize technostress by enhancing digital competencies, providing supportive learning environments, and managing potential stressors. This is particularly relevant in Pakistan’s evolving educational landscape, where technostress has become a significant barrier for business school faculty. The increasing reliance on digital tools and online platforms has elevated stress levels among educators, adversely affecting their productivity and overall well-being. Moreover, the lack of a validated construct to specifically measure technostress within this context underscores the necessity for targeted research to develop an appropriate framework that addresses the unique challenges faced by faculty in business schools.

Most importantly Due to the fast and constantly evolving pace of these technologies, it influences academic workforce’s daily tasks, thereby negatively affecting the faculty members’ well-being. It is therefore imperative to effectively address these challenges, in order to enhance the well-being and effectiveness of faculty perception in the Pakistan’s management education system. Recognizing these stressors enables authorities to launch programs that will improve the resilience of teaching staff and enhance their instructional accomplishments.

Research Questions.

1. What distinctive types of technostress exist among Pakistan business school faculty members?

2. How well do the elements of the redesigned technostress construct connect to employees work-related psychological responses?

3. How business schools need to identify strategies that can effectively decrease the levels of technostress among their faculty members?.

The existing literature on technostress in higher education reveals a critical gap in understanding the specific challenges faced by business school faculty, particularly within the context of Pakistan’s rapidly evolving educational landscape. While previous research has documented the general impact of technostress on educators, there is a notable absence of a validated construct tailored to measure technostress specifically for business school faculty. This lack of a focused framework limits the ability to effectively assess and address the unique stressors associated with the increasing reliance on digital tools and online platforms in business education. The need for a newly designed technostress construct is paramount, as it would enable researchers and educational institutions to better understand the factors contributing to technostress among business school faculty. Such a construct would facilitate the identification of specific stressors—including technological environment, individual characteristics, organizational nature, and supply context—allowing for targeted interventions. By addressing these unique challenges, institutions can enhance digital competencies, provide supportive learning environments, and manage potential stressors effectively. Developing a validated technostress construct for business school faculty is significant for several reasons: it will provide a robust framework for measuring and analyzing the impact of technostress on faculty well-being and productivity, inform the creation of tailored interventions aimed at mitigating technostress, and contribute to the broader discourse on technostress in higher education. Addressing this gap is essential not only for improving faculty well-being but also for enhancing the overall quality of education in business schools.

Literature review

Person-environment fit theory

Technostress plays a critical role in the educational landscape, particularly as technology continues to evolve. The research conducted by [26] indicate a phenomenological study exploring the experiences of language learners in the digital age, highlighting the intersection of digital literacy, technostress, online engagement, autonomy, and academic success. Their findings underscore the multifaceted nature of technostress and its significant impact on students’ learning experiences. This understanding is essential for educators and policymakers to enhance language learning in a digital context.

Similarly [27], examined the technostress faced by academics, revealing that while technology facilitates more efficient and flexible work, it also introduces challenges that can lead to stress. Their research identified specific techno-stressors—such as techno-overload and techno-complexity alongside factors like ICT self-efficacy and work-home conflict that influence psychological well-being. These insights are echoed in the broader literature, where misalignment between individuals and their environments exacerbates technostress. For instance [7], proposed a multidimensional framework identifying person-organization (P-O), person-technology (P-T), and person-people (P-P) misfits as key contributors to the stress experienced by university teachers.

In a study involving 620 university students in China, researchers developed an 8-item technostress scale based on Person-Environment Fit Theory, demonstrating robust psychometric properties (28). Similarly, a factorial analysis conducted with 1,744 university students in Spain adapted the technostress scale, supporting a five-factor model while maintaining internal consistency (29). Moreover [28], explored the factors influencing technostress in K-12 teachers, reinforcing the relevance of Person-Environment Fit Theory in understanding these dynamics.

These insights collectively underscore the critical need for supportive environments that can mitigate technostress and enhance overall educational outcomes.

Technostress across the globe

The validation of technostress constructs demonstrates that this phenomenon exists in multiple complex ways throughout different educational and cultural settings. The study of [29] created a specialized scale for hybrid education student assessment by adapting original questions from the Chinese university professor questionnaire. The adapted version of the research established three fundamental elements that demonstrate how individual abilities fail to match the requirements of their environment and produced results that increased local understanding about technostress effects on Chilean academic institutions. While [30] developed the Digital Stressors Scale (DSS) in the United States by reducing a large item pool until they finalized an operational instrument with ten digital stress categories. The scale exhibited substantial relations with job satisfaction alongside emotional exhaustion thus providing organizations with an effective means to deal with workplace technostress. A psychometric scale designed by [31] assessed university students who used technology-enhanced learning through an 8-item approach which yielded valid measurements of student well-being initiative among university participants. The study of [32] translated and adapted a technostress questionnaire for Spanish students which they verified through psychometric testing for use in educational environments. Researchers [33] successfully translated and validated the Technostress Scale for elderly Turkish citizens while proving its cultural suitability. These studies demonstrate how essential it is to adapt technostress concepts to specific populations since this approach helps create better educational and workplace settings (see Table 1).

Table 1.

Latest studies related to technostress construct validation and its relation with outcome variables

Author(s)-Year	Methodology	Target Population	Sample Size	Country	Outcome Variables	Technostress Dimensions	Findings
[29]	Exploratory and Confirmatory Factor Analysis	University students in Chile	212	Chile	Technostress scale validation for university students	Abilities-Demands Techno-Educational, Needs-Supplies Resources, Person-People Factor	Validated a reliable scale for measuring technostress in Chilean university students, reduced original five factors to three.
[30]	Exploratory Factor Analysis (EFA) and PLS-SEM	Employed individuals in the US	1,998	United States	Perceived stress from digital technology in the workplace	Digital Stressors (10 categories) linked to emotional exhaustion, innovation climate, job satisfaction, user satisfaction	Developed Digital Stressors Scale (DSS) with 10 categories and 50 items; strong explanatory power for emotional exhaustion and user satisfaction.
[31]	Factor Analysis, Rasch Model, Multilevel Linear Regression	University students in China	620	China	Technostress levels in technology-enhanced learning	Person-Environment Fit theory-based technostress scale	Finalized 8-item technostress scale with robust psychometric properties, valid across demographics.
[32]	Factorial Analysis	University students in Spain	1,744	Spain	Adaptation of technostress scale for Spanish university students	Person-Environment Misfit theory (5 factors)	Reduced the original scale, maintaining good internal consistency and supporting a five-factor model for Spanish students.
[33]	Exploratory and Confirmatory Factor Analysis (EFA & CFA)	Older adults using ICT in Turkiye	551	Turkiye	Adaptation of technostress scale for Turkish older adults	Five-factor model for older adults	Turkish version of the Technostress Scale validated with acceptable factorial validity and high reliability.
[34]	Quantitative approach, Cross-sectional survey	Pakistani teachers	242	Pakistan	Impact of computer-assisted teaching on technostress, teachers’ self-efficacy, motivation to leave teaching	Learning Teaching Process Oriented, Technical Issues Oriented	Teachers’ self-efficacy in using computers negatively associated with technostress. Need for instructional coaching to mitigate stress.
[35]	Cross-sectional study, Thematic analysis	Medical students in Pakistan	369	Pakistan	Medical students’ willingness to use online learning, perception of technology-related stress, role of self-efficacy	Technology-stress negatively associated with intention to use online learning, self-efficacy as a moderator	Students with higher computer self-efficacy experienced lower technostress and were more willing to use online learning.
[36]	Correlational study, SmartPLS analysis	University students in Pakistan	165	Pakistan	Problematic internet use as a mediator between internet usage and technostress, role of personality type	Problematic internet use mediates relationship between internet use and technostress, moderated by personality traits	Problematic internet usage partially mediates relationship between internet use and technostress. Personality traits play a moderating role.
[37]	Confirmatory Factor Analysis (CFA), Internal consistency analysis	Adolescents (18–29 years)	450	Pakistan	Validation of Multidimensional Digital Stress Scale (MDSS) in Urdu	Five sub-scales of MDSS (digital stress categories)	MDSS validated in Urdu, strong internal consistency. Positive correlation with loneliness and social anxiety, negative correlation with psychological well-being.
[38]	Descriptive cross-sectional study, Chi-square test	University students in Pakistan	165	Pakistan	Gender and family differences in ICT use, technostress, personality, and psychological well-being	Significant gender differences in technostress, family system differences in personality and well-being	High levels of internet use, problematic internet use, and technostress found among university students. Significant gender and family differences.

The Table (1) presents a summary of current research about technostress throughout various educational sectors in Pakistan which includes information about study approaches and participant demographics and sample sizes alongside key research outcomes. The study of [34] conducted a cross-sectional survey of 242 teachers which revealed that computer-based self-efficacy showed a negative relationship with technostress and underlined the educational requirement for instructional coaching. The research carried out by [35] dealing with 369 medical students through thematic analysis showed self-efficacy levels improved while technostress lowered and students demonstrated greater enthusiasm for online learning. The research conducted by [35] demonstrated using 165 university students that problematic internet use functions as a mediator between internet usage and technostress based on personality traits. In contrast [36] tested the Multidimensional Digital Stress Scale (MDSS) using Urdu with 450 adolescent participants and verified its strong reliability and correlation effects with feelings of loneliness and mental state wellness. The study by [37] analyzed 165 university students through a descriptive cross-sectional research design to identify major differences between genders and family structures regarding technostress alongside psychological well-being. In contrast, Menhas et al. (2024) assessed the strength of nature-based social prescriptions (NBSP) as mental health interventions that could enhance traditional treatment strategies. Younas et al. (2023) analyze students’ physical and psychological health during COVID-19 times through their research on coping behaviors and their protective value for student welfare. Noor et al. (2022) explore how digital tools with educational applications affect student learning patterns together with motivational factors alongside knowledge expansion in Pakistan’s higher education space because technology revolutionizes contemporary educational systems. These investigations demonstrate that technostress affects people in multiple ways because they showcase how self-efficacy and personality variables combined with social influences help reduce technological distress effects.

Present study

In Pakistan, pursuing business education involves intense competition and significant pressure. Business students face high academic expectations due to the importance placed on performance by both educational institutions and the job market. This competitive environment generates considerable stress, as students strive to meet societal and academic standards that value achievement and career advancement. Cultural factors also influence interactions within business schools. Ethnic values such as hard work, performance, and family obligations shape the roles of students and faculty. These cultural dynamics can affect how faculty address student challenges, potentially increasing stress levels for students.

Technostress, defined as stress related to technology use, is becoming increasingly prevalent in educational settings. While technology usage in education is rising, its implementation varies across Pakistan, causing faculty to experience stress in integrating these tools into their teaching. This technostress, combined with other academic and cultural pressures, highlights the need for targeted interventions and support programs to improve faculty well-being and effectiveness. Researchers recommend developing a new technostress scale tailored to the Pakistani context, reflecting the local technological landscape. This scale would aid in understanding the specific challenges faced by business school faculty. To ensure its validity, exploratory and confirmatory factor analyses should be conducted to assess its relationship with overall stress, work-family conflict, emotional exhaustion, and demographic factors like age and gender. Such research efforts aim to identify faculty needs and enhance their health and performance within Pakistan’s business education system.

Materials and methods

Participants

The current study aims to validate the Technostress (TCHS) Pakistan version questionnaire using data collected from 300 faculty members working in business schools. The population of the study comprise of business schools in Islamabad and Khyber Pakhtunkhwa and response from 300 respondents from both KP and Islamabad business schools were targeted. However after removing the missing values and incomplete constructs finally 250 respondent data was used for further analysis through EFA & CFA to validate the newly design technostress construct. The selection of sample size from business schools is depend upon the notion that business faculty members works on multidimensional tasks i.e. (teaching, supervising students projects, internship guidance, updating own research record as well as working on national and international projects through frequent interaction with all stakeholders through digital medium).

Systematic random sampling approach was applied to obtained the required sample from the targeted business schools. The evaluation instrument consisted of four Likert-scale items for each dimension, which specifically assessed techno-overload, techno-invasion, techno-complexity and techno-uncertainty. The researchers implemented a thorough back-translational process, which established equivalent meaning across both Urdu and English versions of the questionnaire.

The research employed a positivist framework because it depended on measurable objective data analysis through statistical methods. This approach of knowledge acquisition supports observations about real-world phenomena and accepts measurable definitions of reality. The study implements various components that correspond to these philosophical agreements:

Standardized procedures along with participant consent procedure helped the research maintain its objective. The study used empirical survey data to establish facts related to technostress within faculty member groups. The study’s cross-sectional design establishes the capability to apply research results towards a wider faculty demographic found within similar educational environments.

The demographic breakdown of the respondent’s reveals that a significant portion, 125 individuals (50%), fall within the age range of 36 to 45 years. Following closely behind, 75 respondents (30.00%) are aged between 46 and 55 years, and 50 respondents (33.33%) are aged between 25 and 35 years. In terms of gender distribution, the faculty sample consists of 221 male staff members (88.44%) and 29 female staff members (11.60%). Regarding professional experience, the majority of respondents, 140 individuals (56.67%), have more than 10 years of experience in the field. This is followed by 72 respondents that is (28.8%) have between 6 and 10 years of experience, while 40 respondents (16.22%) have between 3 and 5 years of experience. This demographic profile provides a comprehensive overview of the sample used in the study, highlighting the diversity in age, gender, and professional tenure among faculty members in Pakistani business schools. Such detailed demographic insights are crucial for interpreting and contextualizing the findings related to technostress and its impact on faculty members in this academic setting.

The participants in this study were duly informed and consent was sought from all of them before they agreed to participate in the study prior to their involvement. The objectives and all the possible beneficial and disadvantageous consequences of this study had been outlined in the consent.

Measures

The questionnaire that was used in this study to measure the sources of technostress was adopted from the one used by [38, 39] and validated by [5]. In particular, techno-overload (TO) was measured through four questions while techno-invasion (TI) through four questions. Regarding techno-complexity (TC), this construct was evaluated using four items, including a sample item such as “It takes me a long time to understand and use new technologies.” The reliability of this measure was α = 0.820, indicating a high level of internal consistency among the items assessing techno-complexity. Similarly, techno-uncertainty (TUC) was assessed using four items, with a sample item like “I struggle to find sufficient time to study and keep pace with the latest technological advancements.” The reliability coefficient for this construct was 0.798, suggesting strong reliability among the items measuring techno-uncertainty. Participants responded to these items using a Likert scale ranging from “Strongly disagree” to “Strongly agree,” providing a quantitative assessment of their agreement with each statement. These measures were crucial for exploring the perceived challenges related to techno-complexity and techno-uncertainty among the study participants. It is, however, noteworthy that all items regarding the concept of techno-insecurity was deliberately excluded from the questionnaires. This decision was made because the study sample was confined to public servants of universities who derive their job security from Pakistan’s higher education institutions. Techno-inefficacy, in many cases, implies vulnerability in relation to the risk of losing job to the more competent technology specialists. Proposing such items can also act as a restriction on the analysis since all the participants in the study were from organizations that face Techno-insecurity [5].

Employee emotional exhaustion, commitment, and work-family conflict (WFC) were assessed using established measures. Emotional exhaustion was measured with items such as “I feel used up at the end of a workday,” based on the scale developed by [40], yielding a reliability coefficient of 0.811. Commitment was evaluated using items like “I find that my values and the organization’s values are similar,” adapted from the scale by [41] which demonstrated a reliability coefficient of 0.789. These measures were employed to ensure the reliability of the constructs under investigation.

Translation procedure

In developing a new questionnaire to assess techno-stress within the Pakistan higher education sector, a rigorous back-translation procedure was implemented to ensure linguistic and conceptual equivalence [42]. A rigorous back-translation process was used to develop a new questionnaire assessing technostress in the Pakistan higher education sector through language and conceptual equivalence verification. The instrument started with bilingual experts who translated material from the researcher’s speaking language into official Urdu for Pakistan. The third group of bilingual experts translated the Urdu version of the questionnaire in reverse to return to the original language. The comparison process allowed us to recognize any discrepancies which existed between dimensions or meaning. The translation process demanded various round of adjustments to normalize linguistic and conceptual discrepancies between the original document and its Urdu adaptation. Translation experts gave their final feedback on the questionnaire that was later modified through extensive critique to achieve valid measurements of technostress in Pakistani higher education [43].

Psychometric analysis procedure

Exploratory factor analysis

Exploratory Factor Analysis (EFA) is a crucial technique for validating a newly developed construct, such as a four-dimension techno-stress construct in higher education in Pakistan. EFA helps in identifying the underlying factor structure by examining the correlations among measured variables [44]. In the context of this study, the four dimensions of techno-stress might include techno-overload, techno-invasion, techno-complexity, and techno-uncertainty. By applying EFA, researchers can determine whether the items designed to measure each dimension indeed load onto distinct factors [45], thus confirming the theoretical construct [46]. This process involves several steps, including ensuring sample adequacy, extracting factors, and rotating them to achieve a clear structure [47]. For instance, in a study on techno-stress among university students, EFA can reveal whether items related to feeling overwhelmed by technology (techno-overload) are distinct from those related to technology invading personal life (techno-invasion), thereby validating the multidimensional nature of the construct [39].

CFA

Confirmatory Factor Analysis (CFA) is a statistical technique used to test whether the data fit a hypothesized measurement model [48–51], based on the theoretical understanding of the construct. For validating a newly developed four-dimension techno-stress construct in higher education in Pakistan, CFA is essential to confirm the factor structure identified by EFA.

CFA involves specifying the expected factor structure and then testing how well the collected data fit this model [52]. In this context, the four dimensions—techno-overload, techno-invasion, techno-complexity, and techno-uncertainty—are hypothesized to represent distinct but related aspects of techno-stress. Using CFA, researchers can assess the model’s fit by examining various fit indices such as the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA) [53]. For example, in a study on techno-stress among university students, CFA would confirm whether items designed to measure techno-overload load exclusively on that factor and not on techno-invasion, thus validating the hypothesized factor structure [54–56]. Additionally, CFA allows for the assessment of convergent and discriminant validity, ensuring that the items not only correlate highly with their respective factor but also demonstrate low correlation with other factors, thereby confirming the distinctiveness of each dimension within the construct [57].

Results

Exploratory factor analysis

Table 2 related to the exploratory factor analysis (EFA) results. The KMO value is 0.845, which exceeds the recommended threshold of 0.60 (Kaiser, 1974). A KMO value greater than 0.80 is considered “meritorious” [58], indicating that the sample size is adequate for factor analysis. The high KMO value of 0.845 suggests that the partial correlations among the variables are sufficiently high, supporting the factorability of the data.

Table 2.

KMO and Bartlett’s test

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy.	0.845
Bartlett’s Test of Sphericity	6832.742
Df	120
Sig.	0.000

Bartlett’s Test of Sphericity produces a Chi-square value of 6832.742 with 120 degrees of freedom. The associated p-value is 0.000, which is less than the commonly used significance level of 0.05. This significant result indicates that the correlation matrix is not an identity matrix, and there are significant relationships among the variables, making them suitable for factor analysis [59].

The combination of a high KMO value (0.845) and a significant Bartlett’s Test of Sphericity (p-value < 0.001) provides strong evidence that the data is appropriate for conducting exploratory factor analysis (EFA) [60]. These results suggest that the variables are sufficiently correlated and that the sample size is adequate to extract meaningful factors from the data.

The EFA results presented in this table lay the foundation for the subsequent analysis and provide confidence in the underlying factor structure of the newly constructed 4-factor questionnaire for managing technostress among students.

The factor loadings for the items under each construct are all above the recommended threshold of 0.70 [54], indicating that the items are strongly associated with their respective constructs. For instance, the factor loadings for the “Technological Overload” construct range from 0.869 to 0.908 (Table 3), suggesting that these items are reliable indicators of the technological overload experienced by the respondents.

Table 3.

Factor loadings, Cronbach alpha reliability, CR and AVE

Constructs	Items	Loading	CA	CR	AVE
Technological Overload	TO-1	0.869	0.95	0.93	0.77
	TO-2	0.860
	TO-3	0.908
	TO-4	0.879
Technological Invasion	TI-1	0.908	0.96	0.94	0.81
	TI-2	0.885
	TI-3	0.925
	TI-4	0.883
Technological Complexity	TC-1	0.908	0.91	0.93	0.77
	TC-2	0.846
	TC-3	0.830
	TC-4	0.928
Technological Uncertainty	TU-1	0.900	0.98	0.94	0.81
	TU-2	0.905
	TU-3	0.891
	TU-4	0.906

CA = Cronbach Alpha; CR = Composite Reliability; AVE = Average Variance Extracted

The Cronbach’s Alpha (CA) values for all the constructs exceed the recommended threshold of 0.70 [61], indicating high internal consistency reliability. Specifically, the CA values range from 0.91 for “Technological Complexity” to 0.98 for “Technological Uncertainty” (Table 3), demonstrating the reliability of the measurement scales.

Furthermore, the Composite Reliability (CR) values for all the constructs are above the recommended level of 0.70, ranging from 0.93 for “Technological Overload” and “Technological Complexity” to 0.94 for “Technological Invasion” and “Technological Uncertainty” (Table 3). These high CR values provide additional evidence of the reliability of the measurement scales.

Regarding convergent validity, the Average Variance Extracted (AVE) values for all the constructs exceed the recommended threshold of 0.50 [55], ranging from 0.77 for “Technological Overload” and “Technological Complexity” to 0.81 for “Technological Invasion” and “Technological Uncertainty” (Table 3). These high AVE values indicate that the constructs capture a significant portion of the variance in their corresponding items, further supporting the convergent validity of the measures. Fig. 1 presents the EFA scree plot obtained for this study.

Fig. 1.

EFA plot

The robust psychometric properties demonstrated in Table 3, including the strong factor loadings, high reliability coefficients (CA and CR), and good convergent validity (AVE), provide confidence in the quality and appropriateness of the measurement scales (technostress 4 dimension questionnaire) used in this study [54, 55, 57]. CFA result further confirm the notion that technostress consist of 4 different dimensions that existed among business schools of Pakistan and the answer to first research question (What distinctive types of technostress exist among Pakistan business school faculty members?) is hereby proved.

The results of the factor loading that are presented in the Table (4) give an understanding of the structure of the items of the questionnaire. Factor loading refers to the extent of relationship between a variable, or measure (items or questions in a questionnaire), and a factor. It is a significant process of conduct in EFA as it ensures separation of the items that are closely linked to the factors being identified.

Table 4.

Factor loading of initial EFA

Statement	Factor 1	Factor 2	Factor 3	Factor 4
TO-1; The information technology used at work forces me to work at a faster pace.	0.754	-0.205	0.31	0.402
TO-2; The information technology used at work compels me to work more than I can comfortably handle.	0.778	-0.246	0.288	0.364
TO-3; The information technology used at work requires me to work within very tight schedules.	0.785	-0.239	0.311	0.42
TO-4; The information technology used at work necessitates that I adapt my work habits to new technologies.	0.705	-0.264	0.265	0.46
TI-1; The information technology used at work causes me to spend less time with my family.	0.774	0.107	-0.566	0.125
TI-2; The information technology used at work keeps me in constant contact with work, even during my vacations.	0.759		-0.551	0.114
TI-3; The information technology used at work forces me to sacrifice my vacation time and weekends to stay up-to-date with new technologies.	0.769	0.106	-0.593	0.105
TI-4; The information technology used at work invades my personal life.	0.649		-0.641
TC-1; It takes me a long time to understand and use new technologies.	0.37	0.796	0.256
TC-2; I do not find enough time to study and keep up with new technologies.	0.414	0.743	0.167
TC-3; I believe the newcomers in this organization are more knowledgeable about technology than I am.	0.377	0.735	0.173
TC-4; I often find understanding and using new technologies to be very complex.	0.375	0.823	0.236
TU-1;It takes me a considerable amount of time to comprehend and utilize new technologies.	0.815	-0.211	0.125	-0.468
TU-2; I struggle to find sufficient time to study and keep pace with the latest technological advancements.	0.814	-0.227	0.152	-0.46
TU-3; I believe the newer members of this organization possess greater expertise in technology compared to myself.	0.826	-0.246	0.153	-0.425
TU-4; I frequently find the process of understanding and using new technologies to be highly complex.	0.81	-0.235	0.21	-0.444

It is possible to extract four factors in this analysis, and the results displaying the factor loadings of each item with respect to the four factors are presented. This means that a value which lies above 0. 5 can be interpreted to mean that the item in question is heavily loaded on the corresponding factor. On the other hand low factor loading less than 0.3 is an indication that either the item does not belong to the factor or it does not load well on the expected factor.

For example, the item TO-1: ‘The information technology used at work forces me to work at a faster pace’ gives a factor loading of (0.754) on Factor 1. This indicates that this item closely relates to Factor 1, which could mean Techno Overload In the same way, the item TC-1: It takes me a long time to understand and use new technologies has a factor loading of 0. 796 in Factor 2, this suggests that it is significantly associated with the factor “Techno-Complexity”.

The result forms for the factor loading too depict the multi-dimensional aspect of the constructs that the scales are trying to capture. There are some widely factors that create important cross loadings, this means that these items might be representing constituents of other factors. This information can be helpful in fixing the disturbances in the questionnaire and enhancing general measurement model.

Thus, based on the factor loadings, researchers are able to select distinct items that best define the factors, in order to create a brief and valid measuring tool for techno-overload, techno-invasion, and techno-complexity faced by employees.

Confirmatory factor analysis

Confirmatory factor analysis was performed using AMOS 23 to assess the factor structure and confirm the validity of the constructs. The model featured four latent variables that included Techno-Overload, Techno-Invasion, Techno-Complexity and Techno-Uncertainty. CFA included Goodness of Fit Index (GFI > 0.95), Incremental Fit Index (IFI > 0.95), Tucker-Lewis Coefficient (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA > 0.95). Initially, the value of CFI and RMSEA were not falling under the threshold. The researchers covariate the items (TU4 & TU3) as well as (TC2 &TC3) using modification indices. It is evident at table that values of all the indices are falling under the threshold (CMIN/df = 2.084; GFI = 0.935, CFI = 0.985, RMSEA = 0.05) fulfilling the criterion for a good model fit [62]. The measurement model of the newly developed four-dimensional technostress construct achieves strong fit metrics according to Table 5. The chi-square value of 193.799 reveals a suitable model fit through 93 degrees of freedom as the CMIN/df ratio shows 2.084 below the typical 3 threshold. The measurement model displays very good fit based on the Incremental Fit Index value of 0.985 and the Goodness of Fit Index value of 0.935 since results above 0.90 are typically accepted. Measurement model fitness indicators demonstrate a strong fit as TLI stands at 0.981 and CFI stands at 0.985 because results near 1.0 are desired. A Root Mean Square Error of Approximation (RMSEA) value at 0.05 shows minimal errors exist in the approximation process. These findings show that the measurement model successfully measures technostress dimensions which establishes the construct validity during this specific research investigation. Fig. 2 illustrates the structural model of the four factors.

Table 5.

Model fit

Model	Chi-Square	DF	CMIN/df	IFI	GFI	TLI	CFI	RMSEA
Default Model	193.799	93	2.084**	0.985	0.935	0.981	0.985	0.05

Fig. 2.

Structural model

The standardized residual covariance matrix presented in Table 6 provides valuable insights into the fit of the confirmatory factor analysis (CFA) model. Standardized residuals represent the difference between the observed covariance’s and the model-implied covariance’s, standardized by the estimated standard errors [63].

Table 6.

Standardized residual covariance

	TU1	TU2	TU3	TU4	TC1	TC2	TC3	TC4	TI1	TI2	TI3	TI4	TO1	TO2	TO3	TO4
TU1	− 0.001
TU2	0.003	0.001
TU3	− 0.002	0.000	0.000
TU4	0.001	0.008	− 0.008	0.000
TC1	0.340	0.223	0.137	0.429	0.000
TC2	1.767	1.590	1.394	1.556	− 0.012	0.000
TC3	1.283	1.008	0.688	1.059	− 0.106	0.000	0.000
TC4	0.197	0.032	− 0.110	0.242	0.004	− 0.014	0.013	0.000
TI1	0.407	− 0.114	0.037	− 0.747	− 0.053	2.088	1.736	− 0.099	0.000
TI2	0.714	0.342	0.144	− 0.329	− 0.135	2.001	1.551	− 0.050	− 0.011	0.000
TI3	0.364	− 0.106	− 0.004	− 0.737	− 0.255	2.001	1.499	− 0.112	0.003	0.003	0.000
TI4	0.478	0.107	0.491	0.108	− 0.753	0.793	0.701	− 0.599	0.023	− 0.018	0.000	0.000
TO1	− 0.019	0.023	0.418	0.514	0.907	1.142	1.212	0.533	0.415	0.310	− 0.018	-1.422	0.000
TO2	0.356	0.553	0.891	0.800	0.254	1.080	0.701	− 0.062	0.060	0.467	0.083	− 0.481	− 0.036	0.000
TO3	− 0.204	− 0.364	0.080	0.162	0.017	1.133	1.116	− 0.098	0.047	0.237	− 0.349	-1.727	0.005	− 0.003	0.000
TO4	− 0.597	− 0.602	− 0.175	− 0.042	− 0.665	0.550	0.424	-1.011	0.322	0.520	− 0.075	-1.522	− 0.055	− 0.087	0.037	0.000

Thus, most of the fitted values should be small, preferably less than 2, if the model under discussion is well fitting. The value 58 in absolute is still acceptable and implies that the model has a pretty good fit in reproducing the observed covariance’s although it may not have represented the two half part perfectly well [53].

Examining the values in the matrix, we can observe the following: All the diagonal elements (for example, TU1-TU1, TU2-TU2…) are almost equal to 0; this means that the model conveys the actual variances of the items very accurately as calculated by the formula VAR [52].

All the values in the matrix are above 0.1, which is advantageous because of the model’s ability in determining the interactions of the items and because majority of the off-diagonal elements, which depicted the covariance between the items, are reasonably small.

Yet, there are a few large standardized residuals; all of them belong to the rows/columns connected with the “Techno-Complexity” (TC) and “Techno-Invasion” (TI) factors. For example, the residual between TC2 and TI1 is 2.088 which shows that the faculty has slightly higher average than the recommended figure of 2.58 [63].

These larger residuals may indicate that the items may not be being represented by the model as well as they could be and there might be something that could be improved on the model such as change in cross-loadings or the number of factors [52].

In a general sense the standardized residual covarience matrix is very helpful in diagnose the overall fit of the CFA and to identify development potential for the model [64].

The data presented in Table 7 shows the item-to-factor loadings for the technostress construct in the confirmatory factor analysis (CFA) model. This table provides valuable information about the relationship between the observed variables (items) and the latent factors (dimensions) of technostress.

Table 7.

Item to factor Loading of the technostress construct

			Estimate	S.E.	C.R.	P
TO4	<---	F1	1.000
TO3	<---	F1	1.098	0.038	28.860	***
TO2	<---	F1	0.948	0.038	24.947	***
TO1	<---	F1	1.045	0.044	23.617	***
TI4	<---	F2	1.000
TI3	<---	F2	1.190	0.043	27.679	***
TI2	<---	F2	1.160	0.052	22.517	***
TI1	<---	F2	1.187	0.048	24.580	***
TC4	<---	F3	1.000
TC3	<---	F3	0.743	0.048	15.605	***
TC2	<---	F3	0.785	0.046	17.016	***
TC1	<---	F3	0.970	0.031	31.544	***
TU4	<---	F4	1.000
TU3	<---	F4	1.008	0.023	44.747	***
TU2	<---	F4	0.991	0.023	42.576	***
TU1	<---	F4	0.951	0.025	37.586	***

All the items used to measure Techno-Overload (TO1, TO2, TO3, TO4) have large positive coefficient which further indicates that all the items are loaded on to the Techno-Overload factor and the coefficient is statistically significant at 0.001 level. All the items used in establishing Techno-Invasion (TI1, TI2, TI3, TI4) have positive coefficients and are statistically significant at p < 0. 001, hence indicating that the items have a good fit in establishing the Techno-Invasion factor.

The results show that the factor loadings of the items used in defining Techno-Complexity (TC1, TC2, TC3, TC4) are statistically significant at p < 0. 001 hence evidencing that these items are properly captured by the Techno-Complexity factor. The alpha coefficients, Cronbach’s alpha, for Techno-Uncertainty (TU1, TU2, TU3, TU4) are all positive and statistically significant with p < 0.001 which shows high internal consistency as well as detailed reliability analysis for the Techno-Uncertainty items.

The standardized factor loadings, standard errors (S.E.), critical ratios (C.R.), and p-values provided in the table indicate that the measurement model for the technostress construct is well-defined, with the observed items strongly related to their respective latent factors [52].

Construct validity

 8 presents the bivariate correlations among the technostress construct attributes and employee behaviors.

Table 8.

Bivariate correlation among technostress construct attributes and employees behavior

	Age	Gen	TU	TO	TI	TC	EE	WFC	CMT
Age	1
Gen	0.018	1
TU	0.222**	0.133*	1
TO	0.412**	0.112*	0.165	1
TI	0.198*	0.089	0.105	0.0112	1
TC	0.178*	0.076	0.189**	0.0430	0.078	1
EE	0.222**	0.133*	0.188**	0.567**	0.465**	0.677**	1
WFC	0.366**	0.465**	0.277**	0.611**	0.411**	0.430**	0.520**	1
CMT	0.411**	0.278**	-0.034	-0.23*	-0.133	-0.43*	-0.22*	-0.33**	1

Notes: *p < 0.01; **p < 0.001;

TO = Techno Overload; TC = Techno Complexity; TI = Techno Invasion; TU = Techno Uncertainty; Gen = Gender; EE = Emotional Exhaustion; WFC = Work family Conflict; CMT = Commitment

The results show that age is positively correlated with (TU; r = 0.222), (TO; r = 0.412, p < 0.001), (TI, r = 0.198), and (TC, r = 0.178) [52, 56]. This suggests that older employees tend to experience higher levels of technostress. Gender is positively correlated with TU (r = 0.133, p < 0.01) and Emotional Exhaustion (r = 0.133, p < 0.01), indicating that gender differences may play a role in technostress and emotional exhaustion [52, 56]. The technostress dimensions (TU, TO, TI, TC) are positively correlated EE (r = 0.188, p < 0.01; r = 0.567; r = 0.465; r = 0.677, p < 0.001) respectively as well as Work-Family Conflict (r = 0.277; r = 0.611; r = 0.411; r = 0.430), suggesting that higher levels of technostress are associated with increased emotional exhaustion and work-family conflict among employees [52]. In contrast, the technostress dimensions (TU, TO, TI, TC) are negatively correlated with Commitment (r = -0.034; r = -0.23; r = -0.133: r = -0.43, respectively), indicating that higher technostress is linked to lower organizational commitment among employees [41]. The findings further validate the research question No 2 that (How well do the elements of the redesigned technostress construct connect to work-related psychological responses?) technostress attributes has significant relationship with work related attributes of employees.

Discussion

The analysis process was conducted in two primary phases: Exploratory Factor Analysis (EFA) and (CFA), aimed at developing and validating a scale for measuring technostress. EFA was employed to uncover the underlying structure of the scale, identifying the dimensions present and how items grouped together [58], while also ensuring that the sample size was adequate for reliable results. Following this, CFA was used to assess whether the data aligned with the proposed model [53], which included various latent variables related to technostress. Results indicate that where the initial model did not fit well, adjustments were made to enhance alignment, such as modifying relationships between specific items. Ultimately, the model met the necessary validation criteria, it confirmed the newly developed scale’s reliability and validity [52], establishing a robust tool for future research into technostress in the educational context of business schools in developing country prospective. Number of research studies investigate the dynamic role of TCHS among faculty members [34, 65] and students [66] of higher education through various TCHS scales [67]. However, due the challenging environment for online learning and coping with stress appropriately it is needed to develop and validate such an instrument that can effectively measure the stress faced by faculty members due to highly connectedness with technology.

In this regard after studying number of studies that used TCHS 5 factor scale developed [38, 39] and validated by [5] was confirmed through back translation procedure. Based on the study of [20, 21] one of the factor titled “Techno-inefficacy” was related to face job insecurity issue was removed due to the reason that the data was obtained from all the faculty members that are performing their job on permanent basis. Moreover, during initial analytical procedure one items from TC and TO were also removed.

The results of the current study investigates technostress in business schools through four distinctive factors including techno overload, techno complexity, techno invasion and techno uncertainty, which create unique construct for business school faculty staff members, while techno overload primarily results from additional work, demands because of new technologies. Hence, the current study answer the first research question by confirming 4 different attributes of technostress construct in the work context of Pakistani business schools. Previous studies showed that technostress appears with different patterns between educational and cultural settings and this complexity matches the results of the current research.

A research team adapted a questionnaire originally used by Chinese university professors to explore the mismatch between human capabilities and environmental factors, resulting in enhanced understanding of technostress within Chilean higher education institutions. Similarly, the Digital Stressors Scale (DSS), developed in the U.S., links technostress to job satisfaction and emotional exhaustion, offering organizations strategies to manage workplace stress effectively. Psychometric testing has confirmed the cultural relevance of these constructs for different demographics, including Spanish students and older Turkish citizens. These findings underscore the importance of tailoring technostress assessments to specific demographic characteristics for more effective results in both educational and digital work environments. Thus, the newly developed and validated construct for business schools in higher education is essential for addressing the unique challenges faced by students and faculty in today’s technologically driven landscape.

The results of the newly developed technostress construct align with and expand upon findings from various studies that address the impact of technostress in higher education contexts. Research by (7) indicates that requirements related to Information and Communication Technology (ICT) significantly influence teachers’ job performance. This complements the findings of the current study, which highlights techno overload as a major stressor stemming from the pressures of adapting to new technologies. Both studies emphasize that when technological demands exceed employees’ capacities, job performance suffers, leading to emotional exhaustion.

The study from (8) underscores the importance of examining technostress in today’s technology-driven society, noting that increased information flow can result in frustration and anxiety. This is consistent with the current findings that identify techno uncertainty and techno complexity as key contributors to employee stress, leading to feelings of insecurity and diminished self-confidence. Further, the research by (9) focuses on the negative effects of technology-enabled teaching on student satisfaction and performance, highlighting the necessity of recognizing and addressing technostress in students. This parallels the current study’s findings that persistent technostress negatively impacts organizational commitment and engagement among employees, suggesting that both students and faculty face significant challenges in technology-rich environments.

The study (10) on Brazilian students shows a strong correlation between technostress and generalized anxiety disorder, affecting students’ overall quality of life. Similarly, the current research illustrates how technostress can lead to burnout and reduced job satisfaction among faculty, demonstrating that the repercussions of technostress extend across both student and faculty populations. In contrast, the study conducted by (11) further investigates how technostress, cognitive appraisal, and coping strategies influence students’ learning performance, again resonating with the current study’s focus on the detrimental effects of technostress on engagement and commitment.

Moreover, studies (12) and (13) explore technostress among teachers during the COVID-19 pandemic, highlighting its relationship with job satisfaction and burnout. These findings reinforce the current study’s assertion that technostress adversely affects employee morale and organizational behavior, particularly in high-pressure educational settings.

Finally, the work of (14) discusses the positive aspects of technostress—techno-eustress—among teachers, suggesting that while technostress can be detrimental, there are also potential benefits when managed effectively. This perspective adds nuance to the current findings, indicating that not all technological challenges lead to negative outcomes, but rather, proactive management can foster resilience and growth.

Theoretical contribution

The current study makes a significant theoretical contribution by introducing a novel construct for measuring technostress among faculty members in business management schools in Pakistan. Through the investigation of four distinctive factors—techno-overload, techno-complexity, techno-invasion, and techno-uncertainty—the study confirms the unique manifestations of technostress within the specific context of Pakistani business schools. This contribution addresses the first research question by establishing these four attributes as integral to understanding technostress in this educational setting.

Previous research indicates that technostress manifests differently across various educational and cultural contexts, and the findings of this study align with this complexity. For instance, a research team adapted a questionnaire originally used by Chinese university professors to explore the mismatch between human capabilities and environmental factors, which enhanced the understanding of technostress within Chilean higher education institutions. Similarly, the Digital Stressors Scale (DSS) developed in the U.S. links technostress to job satisfaction and emotional exhaustion, providing organizations with strategies to manage workplace stress effectively. Such constructs have undergone psychometric testing to confirm their relevance across diverse demographics, including Spanish students and older Turkish citizens.

These findings highlight the necessity of tailoring technostress assessments to specific demographic characteristics for more effective outcomes in both educational and digital work environments. The newly developed and validated construct for business schools is essential for addressing the unique challenges faced by faculty in today’s technologically driven landscape.

Moreover, the importance of technostress in the educational sector is underscored by studies that explore its intersection with digital literacy and academic success. For example, research conducted by Feng and Liu (2024) emphasizes the multifaceted nature of technostress, demonstrating its significant impact on students’ learning experiences. This understanding is crucial for educators and policymakers aiming to enhance educational practices in a digital context.

Similarly, Wang and Yao (2025) examined technostress among academics, revealing that while technology can enhance work efficiency, it also introduces challenges that may lead to increased stress. Their identification of specific techno-stressors—such as techno-overload and techno-complexity—along with factors like ICT self-efficacy, reinforces the broader literature on the misalignment between individuals and their environments as a contributor to technostress.

Furthermore, studies involving 620 university students in China and 1,744 university students in Spain have validated the importance of culturally relevant technostress scales. These insights collectively underscore the critical need for supportive environments that can mitigate technostress and enhance overall educational outcomes, thereby further solidifying the theoretical foundations of the constructs introduced in this study.

Practical implications

The current study finding provide several practical implications as mentioned below;

In conclusion, the results of the current study are consistent with existing literature, reinforcing the critical need for institutions to address technostress through targeted interventions and support systems for both students and faculty in higher education.

The findings regarding technostress in higher education highlight a critical need for targeted policy and practice interventions specifically within business schools in developing nations. Given the unique pressures faced by faculty and students in technology-intensive environments, it is essential for these institutions to implement comprehensive training programs that enhance digital competencies and facilitate effective use of technological tools. Additionally, business schools should establish clear guidelines and support systems that promote work-life balance, addressing stressors such as techno overload and techno invasion that adversely affect employee well-being and academic performance. By fostering a supportive culture that values mental health and resilience, business schools can mitigate the negative impacts of technostress, thereby enhancing both faculty engagement and student satisfaction. Such proactive measures will not only improve the educational experience but also prepare future business leaders to navigate the complexities of a rapidly evolving digital landscape.

Limitations and future research

Several limitations should be acknowledged in this study. Firstly, the sample was drawn from economically advanced regions in Pakistan, such as Islamabad and the Punjab provinces, which raises concerns about the generalizability of the findings. Future research should include a more diverse geographic representation to capture a broader range of socio-economic backgrounds [7]. Secondly, data obtained through self-reporting may introduce response biases, questioning the robustness of the results; therefore, triangulating data from organizational management could provide a more nuanced understanding. Additionally, the study’s focus on business school audiences may not reflect the broader population, as varying degrees of technostress might exist in different educational settings [7, 68, 69]. While positive relationships between technostress factors and employee behavior were identified, causality remains undetermined, necessitating longitudinal assessments to observe changes over time. Moreover, specific characteristics, such as technological experience and coping mechanisms, were not fully explored, potentially skewing the evaluation of technostress effects. The research is also limited to the Pakistani higher education context, which may not account for cultural and organizational differences that influence technostress. Lastly, the lack of measurement invariance testing for the Techno-Stress Construct in Higher Education (TCHS) complicates meaningful comparisons across different demographic groups. Future studies should aim to establish this invariance to enhance the validity and reliability of findings across diverse educational contexts [4, 25, 27, 39, 70, 71].

Recommendations

The insights about technostress enable policymakers to establish the following actionable recommendations for educational institutions:

Administrative departments should introduce wide-ranging training programs that focus on developing abilities of faculty and staff members to use new technologies. The ability to use complex systems and reduce techno-complexity and uncertainty will improve through this approach.

Organizations should create communication systems which provide quick information about technological advancements along with reliable transmission capabilities. These measures reduce the anxiety and fear experience by employees and staff due to techno-uncertainty during system transitions.

Organizations should develop official work-life balance policies which define rules about technology boundaries during non-work times. Such work-life balance policies help diminish the negative impacts of techno-invasion which results in less work-family conflicts and better individual health outcomes.

Existing institutions should develop cultural systems, which prioritize staff health and understand the hurdles technology introduces to their daily operations. The organization should discuss technology-related stress with their employees because this creates space to assist workers when they need help because of their stress.

Surveys and feedback mechanisms need to operate as a system for regularly evaluating faculty and staff technostress levels. Such studying enables institutions to modify their strategies through updated understanding of their evolving needs.

Workplace intervention programs should focus on developing specific solutions to identified stressors through which staff could learn time management skills and create peer groups for technology stress relief.

Morover as Pakistani work culture comprises of male dominant society, hence the sample size indicate that mostly male respondents are included in the current study. Hence, future researchers may validate the newly developed construct among female faculty members of business schools across the other province of Pakistan as well as across other countries.

Organizations should create collaborative platforms for faculty members and support staff to exchange skills and information thus improving mutual ability to resolve technological challenges and reducing feelings of professional loneliness.

Academic institutions that implement these strategies will achieve relief from technostress thereby improving their total productivity together with employee job satisfaction and organizational loyalty especially across technology-dense business school programs.

Conclusion

We adapted and tested the Techno-Stress Construct in Higher Education (TCHS) among faculty members of business schools in Pakistan. Our process involved translating and culturally adapting the original TCHS items to ensure they were relevant and comprehensible to the Pakistani context. The resulting 16-item Pakistani version of the TCHS was then subjected to rigorous psychometric evaluation.

Our analysis revealed that this adapted version retained a four-factor structure, aligning with the original dimensions of techno-stress: (TO, TI, TC and TU). This structure was confirmed through Confirmatory Factor Analysis (CFA), which demonstrated excellent fit indices, indicating that the model was a good fit for the data.

The reliability of the scale indicate that all the items properly loaded well on their relevant factors suggesting that the items consistently measured the proposed questionnaire effectively. The construct validity of the TCHS was supported by evidence that the scale accurately reflected the theoretical dimensions of techno-stress as hypothesized.

In summary, the 16-item Pakistani version of the TCHS is a psychometrically sound instrument. It can be effectively used to measure techno-stress among faculty members in higher education institutions in Pakistan, providing valuable insights into how technology-related stress affects educators in this specific cultural and professional context. This tool can aid in identifying stressors and developing interventions to mitigate techno-stress, ultimately contributing to better mental health and productivity among faculty members.

The results reaffirm the ubiquitous presence of technostress in business schools and its impact on emotions, work-life interference, and organizational commitment. Techno overload, techno complexity, techno invasion and techno uncertainty present a lot of challenges which adversely affect the employee and organization by increasing the level of emotional exhaustion, work family conflict and reducing overall job satisfaction. It also reveals that working-life interference is a growing problem due to blurring work-life borders and the mental pressure exerted by changes in technology. As a result of these unhealthy trends, business schools need to find ways of combating them through offering employee training, supportive organizational policies, and work intercessions that enhance employee health and preparing them to face the challenges of technology. The management of technostress in its every manner will help attain a motivated and a productive force that is committed to the accomplishment of organizational goals, thus boosting institutional performance and stability.

Abbreviations

CFA

Confirmatory factor analysis

CFI

Comparative Fit Index

CMT

Commitment

EE

Emotional Exhaustion

EFA

Exploratory factor analysis

Gen

Gender

GFI

Goodness of Fit Index

IFI

Incremental Fit Index

RMSEA

Root Mean Square Error of Approximation

TC

Techno Complexity

TI

Techno Invasion

TLI

Tucker-Lewis Coefficient

TO

Techno Overload

TU

Techno Uncertainty

WFC

Work family Conflict

Author contributions

Dr. Naveed Saif and Dr. Sadaqat Ali designed the study's framework. Dr. Gerald Goh and Dr. Shan Cao provided technical guidance while Dr. Mudassir Hussain, Dr. Rao Bakhat Yawar and Dr. Faheem Khan provided technical assistance as well as software for analysis. All the authors agreed to their active role in finalizing the article.

Data availability

The data will be available from author on appropriate request.

Declarations

Ethical approval

The procedures used in this study adhered to the ethical standards set out in the Declaration of Helsinki. As the study was not medical research nor regarded human experimentation as stated in the Declaration of Helsinki, and the questions in the questionnaire did not have any adverse effect on the mental health status of the respondents, ethical approval was not required for this questionnaire-based study according to the regulations of the authors’ institution - Institute of Management Sciences, University of Science and Technology, Bannu, Pakistan.

Informed consent

Allrespondents were at least 18 years old and consented to participate in the research study by completing the questionnaire. The information collected is strictly confidential, anonymous, and used for research purposes only. Informed consent was obtained from all participants. All participants were informed about the purpose and aims of this study, that their participation was entirely voluntary, and the ways the data would be used.

Consent for publication

NA.

Competing interests

The authors declare no competing interests.

Note

AI was used to correct only grammatical issues.