Lecturers’ technostress at a South African university in the context of coronavirus (COVID-19)

Abstract The COVID-19 pandemic has forced all universities to move fully online and adopt more technological resources for effective online learning (e-learning). Lecturers have become frustrated, anxious and rebellious because they are bombarded with different technological demands and contexts, ranging from learning management systems to social media sites, video communication technologies and others. Availability of various technological resources is the only solution to enhance e-learning during the COVID-19 lockdown period, but it can sometimes lead to throughput losses due to technostress. To bridge this gap, the current qualitative case study utilises the person-environment fit framework of stress to explore lecturers’ technostress during e-learning during the COVID-19 shutdown/lockdown. E-reflective journals and Zoom one-on-one semi-structured interviews were used to generate data from 13 lecturers who were conveniently and purposively selected because they were accessible and used various technological resources to offer online lectures during the COVID-19 shutdown/lockdown. Findings showed that information, communication, and technology features mainly caused technostress to lecturers, and they were unable to deal with it because of pressure to meet due dates stipulated by university management. This study concludes by recommending lecturers to teach from the inner-self/self-identity in dealing with levels of technostress (institutional, social and technological resources) and for universities to provide possible control measures to deal with lecturers’ technostress.


Introduction
The rapid development of technology has completely changed the manner in which teaching and learning are conducted in the higher education landscape. Technology is now so integrated into the existence of both lecturers and students that it is quite impossible to do anything without it in this digital age (Bates, 2018;Prensky, 2001). The academic literature (Cavus & Zabadi, 2014;Selwyn & Stirling, 2016;Wolfe, 2019) on e-learning further outlines that the advent of new educational technologies with the development of the Internet has provided an alternative to universities so that they may easily move from face-to-face lectures to online lectures (e-learning) and vice versa. This is achieved through the adoption of various technological resources, ranging from software resources such as learning management systems (LMS) (Moodle, Canvas, Blackboard and others), social media sites (SMS) (Facebook. WhatsApp, YouTube and others), video communication technologies (VCT) (MS Teams, Zoom, Television and others), and hardware resources like laptops, tablets, smartphones and more. Clement (2020) shares the same sentiment as Govender (2021), that the technological resources have the power to influence both lecturers and students for collective e-learning (connectedness) rather than individual learning and provide the convenience of enhanced flexibility (lectures are not fixed), reduced costs (transportation), and electronic documentation (materials are stored online).
On 11 March 2020, the World Health Organization WHO (2020) declared COVID-19 a pandemic, and everyone was advised to avoid close contact with others, especially those showing symptoms. Universities across the globe, therefore, had to shut down. Similarly, in South Africa, the President called for all universities to shut down (called lockdown) and find ways to offer lectures online from 18 March 2020 as a precautionary measure (DHET, 2020). This resulted in no face-to-face lectures, and lecturers were required by university management to offer lectures fully online. As such, they were bombarded with various hardware and software technological resources to enhance effective e-learning within a short space of time, without any adequate training or clear guiding e-learning policies. This situation resulted in unintended consequences of technology overuse and the inability to cope with new technologies, termed technostress (Chiappetta, 2017). Brod (1984) first coined the term technostress to refer to the stress associated with using technology and its impact on a practitioner's psychological and physical aspects; this is a disease of adaptation caused by an inability to cope with varied and emerging technological developments in a workplace. The two American psychologists Weil and Rosen (1997) further refined the definition of technostress to include any negative influence on practitioners' attitudes, thoughts, behaviours or psychology caused directly or indirectly by the use of technological resources. Chiappetta (2017) agrees with Lee et al. (2016) that technological resources evolve too quickly and come with new usability/functions which need time to be learnt and mastered by lecturers who are digital refugees (need training before technology use) in their environment. Hence this situation automatically leads to the development of psychological pressures related to physical harm. In other words, the bombardment of various technological resources on lecturers, without adequate training by universities to enhance effective e-learning within a short period of time in the context of COVID-19, automatically results in their developing technostress (Chiappetta, 2017;Mpungose, 2020b).
The Education, Training and Development Practices Sector Education and Training Authority (ETDP SETA; ETDP SETA Report, 2021) conducted a study among academic staff members across twenty-six (26) South African Universities and revealed fifty-seven (57) different technologies have been deployed to facilitate teaching and learning activities at Higher Education Institutions (HEIs). Among these technologies are Zoom, MS Teams, WhatsApp, etc., which were adapted/introduced for teaching and learning (Govender & Khoza, 2022). Further findings reveal that while most faculty members (59%) are aware of emerging technologies, some do not keep abreast of emerging technologies (41% ; Table 1).
Consequently, lecturers can feel discomfort, frustration, anger, anxiety, fatigue, depression, nightmares and other symptoms, which can lead to low university throughput and many other drawbacks, hindering the achievement of effective e-learning (Mpungose, 2020a;Şahin & Çoklar, 2009;Salanova et al., 2013). A contributing factor is the competency level of individuals (Table 2) which influences the level of confidence when using a particular digital technology in the teaching and learning process (Govender, 2021).
The awareness of emerging technologies and technology competency levels can cause a conundrum when selecting what technology to use. In as much as studies (Dhir et al., 2018;Duvenage et al., 2020;Karr-Wisniewski & Lu, 2010) have been carried out on technostress in the educational landscape, there is little or no research on technostress caused by technology overuse by lecturers in their university environment for e-learning in the context of COVID-19 lockdown. Considering that stress is subjective, with self-feelings of tiredness perceived between a person (lecturer) and the environment (use of technological resources at university/home; Salanova et al., 2013), this study intends to answer the following research questions: (1) What are the experiences of lecturers' technostress in e-learning during the context of COVID-19 lockdown?
(2) Why have lecturers' technostress levels in e-learning during the context of COVID-19 lockdown, manifested in particular ways?
In unpacking these research questions, the person-environment (P-E) fit framework of stress is used to theorise lecturers' technostress (Edwards, 1991;Lazarus, 1995), as described in the following section.   O'Brien and Cooper (2022) and Czaja (2021) argue that the P-E fit framework provides guidelines in understanding the formulation of stress and its different consequences to find mitigating measures. These authors further argue that every individual (lecturer) has personal attributes in a working environment, which include but are not limited to personal needs, knowledge/skills/ values and abilities, while the working environment (university/home) also burdens with its attributes such as supplies and demands. Consequently, the lecturers' well-being and satisfaction in a working environment lead to P-E fit, which occurs when individual lecturer attributes are compatible with e-learning environment attributes demanded by the university (Wang & Li, 2019;Yu, 2016). This suggests that P-E fit is formed when there is equilibrium between lecturers' attributes and e-learning environment attributes. However, Penado Abilleira et al. (2021) assert that P-E misfit occurs when the equilibrium between an individual (lecturer) attribute and the environment (e-learning) attribute shifts or is broken and this condition can generate stress and lead to a strain. In other words, P-E misfit results due to the imbalances of attributes (person and environment) which may generate stress to lecturers and lead to the loss of self-identity while causing university throughput loss and lowered university integrity. The P-E fit framework outlines that stress is caused by the misfit between a person and the environment; thus, both a person and environment contribute to the formation of stress in a particular situation (Ayyagari et al., 2011;Wang & Li, 2019).

Person-environment fit framework of stress
According to Edwards and Billsberry (2010), the P-E fit framework recognises the multidimensional characteristics of fit between a person and environment, which include personvocation (lecturer and professional occupation) fit, person-job (lecturer and e-learning skills/pedagogy) fit, person-organisation (lecturer and institution) fit, and person-people (lecturers and others -students and colleagues) fit. In this case, according to Wang and Li (2019), if the individuals (lecturers) are simultaneously inhibiting a number of these dimensions in an environment, stress or satisfaction arises from a misfit between a person and multiple dimensions of the environment. Thus, the multi-dimensional aspect of the P-E fit framework and the definition of technostress assert that technostress is not only related to misfit caused by the individual lecturer, it is also caused by the changing environment (face-to-face to online lectures) in which technology is used. This is influenced by the institution (university) policies, other people (society) and adopted technologies (technological resources).
In the context of this study, lecturers are demanded by a university to move from face-to-face lectures to working fully online because of the COVID-19 lockdown; hence, a misfit (gap) between lecturers' abilities and the demands of the university e-learning environment can exist. This can be influenced by the structured nature of the university, which requires lecturers to follow stipulated policies (e-learning, module outline/plan, assessment, and others) for effective e-learning, and lecturers are expected to familiarise themselves with and abide by the policy rules and regulations (Govender, 2021;Mpungose, 2020b).
Penado Abilleira et al. (2021) further argue that stress is often witnessed when lecturers are bombarded with many different ideas from colleagues, organised online training workshops, communiques/e-mails from university management and other resources and demands. As such, when too many technological resources (LMS, SMS, VCT and others) are adopted with diverse technological features/functions, this can cause a misfit between lecturer usability and adopted technology (Edwards & Billsberry, 2010;Khoza, 2019). Consequently, this study submits that three dimensions of technostress can influence lecturers: institutional technostress (misfit between lecturer and university policies), social technostress (misfit between lecturer and social ideas), and technological resources technostress (misfit between lecturer and adopted technological resources), as depicted in Figure 1 below. To understand the influences of technostress the SOR (stimulus-organism-response) model was superimposed on the P-E fit framework. The stimuli are represented by the form of technostress that causes an influence on the consumer's (lecturer) attitude (Mehrabian & Russell, 1974). This affects the emotional state (organism) thereby influencing a particular behavioral response, either approach response or avoidance response hence affecting job performance of the lecturer. Ragu-Nathan et al. (2008) argue that even though the research area of stress is broad, technostress has not been extensively studied, particularly in the context of the COVID-19 lockdown period. The study further outlined that technostress negatively impacts teachers (university lecturers) because it lowers their productivity and job satisfaction and decreases commitment to teaching. This is evident in the qualitative study conducted by Al-Fudail and Mellar (2008) to explore teachers' technostress when teaching using technology in their classroom. After analysing data generated by nine teachers in classroom observations and one-on-one interviews, it was found that teachers suffered from technostress. Technostress was caused by the extra time needed to use technology for class preparation, unexpected errors and low user-friendliness of technology, inadequate effective training in the use of adopted technologies, and lack of clear manuals or guiding technology policy. This suggests that in a university context where there are no clear e-learning policy guiding lecturers (person-organisation misfit) and no adequate training on the adopted technology (person-vocation misfit), lecturers are likely to feel work overload having no direction, influenced by both institutional and social technostress. According to Ayyagari et al. (2011), this leads to a context where individual lecturers perceive gaps/misfits between their abilities and technological resources which the institution requires them to use to enhance e-learning, and lecturers can experience difficulties ranging from headaches to tiredness, resistance, annoyance, nervousness and others.

Review of previous studies
Mpungose (2020a) painted a clear picture of the issues that cause lecturers' technostress in his qualitative case study of two South African university contexts. The study explored 31 lecturers' experiences on Moodle uptake for teaching online lectures. Findings revealed that the top-down imposition of mandatory Moodle implementation led to technostress, and lecturers were frustrated, which hindered Moodle uptake. Similar findings from research conducted by Ayyagari et al. (2011) to build a model of technostress with data generated from 661 professionals proposed that any adopted technology has different characteristics, which include but are not limited to the usability (usefulness, complexity, and reliability), intrusiveness (presenteeism, anonymity), and dynamism (pace of change). These characteristics play a huge role in causing stress because professionals can feel overloaded and perceive role ambiguity, while technology can invade their privacy and work-home conflict can exist. This suggests that technostress caused by technological resources negatively impacts lecturers' personal, social and professional lives and the success of universities. For instance, if lecturers are instructed to use Moodle LMS, Zoom VCT, and Facebook/WhatsApp SMS for e-learning, they are likely to experience technological resource- related technostress because these resources have different attributes, demanding different knowledge and skills. Chiappetta (2017) argues that technostress requires a new description and understanding in this digital age, where information is rapidly shared in connected networks through emerging technologies (SMS, LMS, and VCT). As such, Karr-Wisniewski and Lu (2010) assert that technology overload is a condition where the environment (university) forces people (lecturers) to use technology to work much faster and longer than their capabilities allow while under pressure in order to meet due dates or demands of the institution/environment. Thus, technology overload is identified as a stressor (demands or situations in the environment that can generate stress) that leads to people's dissatisfaction and frustration. In line with this, Lee et al. (2016) quantitative survey study investigated 201 individuals' stressors regarding the use of technology. The study concluded by identifying three dimensions of technology overload: information technology overload (exposed to more information), communication technology overload (daily bombardment of announcements), and technology feature overload (exposed to more new system functions/features). For instance, different instructions from university management and training workshops via e-mails and other modes of communication can lead to lecturers' technostress; this also includes the complex technology features of Moodle LMS, Zoom VCT and other technologies adopted for e-learning. These overloads can lead to negative consequences of e-learning during the COVID-19 lockdown. As a result, Karr-Wisniewski and Lu (2010, p. 1062) opine that "technology use, once exceeding the optimum level, can actually incur negative outcomes". This shows that being bombarded with new technology and demanded to enhance effective e-learning without adequate training makes lecturers lose their optimism and start to become rebellious and resistant to all systems in the environment/institution.
Mitigating technostress in a working environment (university) is a must that seeks university management and individual lecturers to strategically plan for innovation and understand technology use for today and in the future. Chiappetta (2017) agrees with Lee et al. (2016) on the need to identify general technostress symptoms before mitigation initiatives can be attained. Such symptoms may include physical symptoms (increased heart rate, muscle tension, pain, insomnia, headache and sweating) and psychological symptoms (behavioural and cognitive) such as depression, irritability, behavioural changes, decreased sexual desire, apathy and others. This suggests that ignoring these symptoms resulting from the technostress caused by institutional, social and technological resources can lead to negative effects that harm lecturers' personal, professional and social life, resulting in poor work performance (e-learning). Delpechitre et al. (2019) and Shim (2015) suggest ways to cope with technostress. The latter authors articulate that universities must implement prevention strategies, such as the provision of adequate training on e-learning and other measures, and ensure proper distribution of workload. Further to this, lecturers must take relevant remedies such as sports techniques (sports and walking), holistic techniques (meditation), and regenerative techniques (eating healthy food). Recently Alam (2020) suggested ways in which lecturers can control technostress to ensure effective e-learning in the context of COVID-19. These include 1. using LMS to send e-mails to all students; 2. creating WhatsApp groups for convenient communication with students; 3. preparing both asynchronous and synchronous activities before online classes using VCT; 4. making sure that all software and hardware technological resources are in order before online lectures begin; 5. keeping online lectures short; 6. engaging students in a dialogue and 7. clarifying assessment tasks and providing effective feedback.
Irrespective of all prevention and control strategies mentioned above, Korthagen et al. (2013) still believe and argue that teaching any subject/module in the online environment is not only about the outer self (content, pedagogy and technological resources) but also about the lecturer's inner-self (identity or drive/rationale), which is the core element in teaching that is driven by heart, passion, love and courage to cope with the different forms of technostress resulting from misfits between a lecturer and the environment. Khoza (2019) and Mpungose (2020a) further argue that the lecturers' inner-self is the fundamental remedy to technostress because it seeks lecturers to do self-introspection to find their identities (purpose, values, vision, goals, motivations and beliefs) and the drive/motive for selfdirection to go beyond limits to enhance effective e-learning even in difficult times (COVID-19).

Study context
In transitioning from the face-to-face to online environment because of the COVID-19 pandemic, a university in South Africa adopted other technological resources such as VCT (Zoom, Microsoft Teams Kaltura and others), SMS (WhatsApp, Facebook, YouTube and others) to supplement its Moodle LMS. Laptops and routers with data bundles for Internet access were provided to be used during the one-month dry-run period for piloting e-learning during COVID 19. The unavailability of a guiding online learning policy and lack of training for lecturers put pressure on them, which ignited challenges (technostress), which was evident in their use of LMS, VCT and SMS (Mpungose, 2019).
From 2019 to 2020, lecturers' reflections on using an LMS in a School of Education (SoE) were collected. A case of 13 lecturers was chosen to explore their technostress in e-learning in the context of the COVID-19 lockdown period. The SoE is at a South African university located in the province of KwaZulu-Natal and offers a broad range of degree programme courses across various fields of study. It prepares more than 7000 students, mostly disadvantaged black students from poor socio-economic backgrounds, followed by other minorities (Indian, Coloured (mixed race) and White students) for professional teaching careers in Education Studies and other disciplines. The SoE mainly offers lectures face-to-face, while the LMS is used as a depository (holding lecturers' notes) for student access. The eruption of the COVID-19 pandemic forced the SoE to move all lectures online since students were instructed to remain at home for the lockdown. All lecturers (mostly female) were compelled to offer lectures online from home. This study's main objective is to describe and understand lecturers' technostress and the reasons for that technostress during e-learning in the context of COVID-19.

Research methods and data collection
The study is a small-scale qualitative interpretive case study of 13 lecturers who were purposively and conveniently selected because they were accessible and offered lectures online during the COVID-19 pandemic period (dry run). In practicing social distancing because of COVID-19, electronic flyers for recruitment were sent to 21 participants via e-mail, including consent letters; after agreeing and e-signing consent letters, they were sent back to the researchers electronically. For privacy and confidentiality, pseudonyms were used (from L1 up to L13, L stands for Lecturer) to protect the identity of the lecturer participants, and they were informed of their right to voluntary participation and to withdraw at any stage if they so wished.
Interpretivism is used not to predict lecturers' experiences but to understand and describe how they make meaning of their actions during e-learning in the context of the COVID-19 lockdown at an SoE (Creswell, 2014). Using a more explorative case study design resulted in a rich and deep description of lecturers' experiences, which resulted in understanding their technostress to find possible control measures (Yin, 2013).
Lecturers were given an e-reflective journey to be completed in two weeks and one-on-one semi-structured Zoom 1 interviews were held for 45 minutes (Creswell, 2014;Yin, 2013). These instruments were vetted and evaluated by peers within the researchers' disciplines to ensure that the data collected would be relevant, of quality and suitable to efficiently and effectively respond to the research questions.
Zoom was used to record online interviews for direct auto transcription. In addition, recordings and transcription matching were reviewed twice to ensure trustworthiness (transferability, dependability, confirmability and credibility). Inductive and deductive processes of qualitative thematic analysis were followed to make sense of lecturers' technostress through coding to form categories and themes (Creswell, 2014;McMillan & Schumacher, 2006). The data generated by the two instruments were directly and openly coded from the recorded source in addition to the auto transcription to avoid loss of meaning during transcription. Open coding was used to connect codes to categories. Thereafter the codes were deductively mapped onto the set categories (from the theoretical framework and the literature) to form themes. However, an inductive process was used to recapture the remaining codes, which were not deductively analysed during the prior analysis, to form categories. After using these processes as a guide, categories were focused and sharpened to form three themes, as indicated in the findings section below.

Results
This study explores lecturers' technostress in e-learning during the COVID-19 lockdown period. The generated data formed categories, which were then sharpened to form three main themes which coincide with the stimulus depicted in the conceptual framework ( Figure 1). The three themes formed are reflections on access to technological resources related to the technology technostress stimuli, access to information related to the social technostress stimuli and Communication related to the institutional technostress stimuli.

Reflections on access to technological resources
Lecturers found it easy to reflect on the accessibility or availability of various technological resources for e-learning. As such, both L7 and L11 agreed with L13, who articulated, "I waited for three weeks to access the router with loaded data bundles for Wi-Fi access, and this disturbed proper planning for online lectures". In other words, the delayed provisions of necessary hardware resources to access the internet disrupted lecturers from conducting effective e-learning. Moreover, all lecturers painted a clear picture regarding the access to laptops, and they all agreed with L4, who opined, "I am using a Dell laptop provided by the university management to all staff . . . the battery lifespan does not last more than an hour if there is load shedding [electricity cuts]". This shows even though lecturers have access to laptops, due to low quality, it becomes a hindrance, particularly when there is load shedding. This forces lecturers to use their own available resources to mitigate the situation. For instance, L1 outlined, "I sometimes use my own smartphone or tablet to access the Internet to do school work when conditions compel me . . . " Most lecturers (9 out of 13) struggled to use adopted software technologies to effect e-learning. As a result, L2 agreed with L5, who opined, "While I am still struggling to create quizzes and assignments on Moodle LMS, I also battle to create group breakouts on Zoom". In other words, the provision of necessary software resources such as LMS and others does not imply that there will be effective e-learning, especially where there is inadequate training.
Similarly, L9 added, "I don't know how to create short or chunks of videos using Kaltura . . . I tried Qualtrics for a survey, but it did not work". Such conditions create strain and fatigue, which leads to lecturers' technostress. As a result, lecturers prefer to use the most common and easy-to-use software to affect e-learning. As such, L12 opted to use SMS and articulated, "WhatsApp works for me, but finding phone numbers for 1400 students and creating groups it is really tiring . . . some students do not have accounts on Facebook". Thus, even though SMS can be conveniently used to enhance effective online learning, there are still challenges that lecturers face, leading to technostress. It is good for universities to provide lecturers with all necessary hardware and software resources. However, provisions of different resources simultaneously, without proper training, bring technostress to lecturers. As L4 articulated, "I ended up being confused as resources work best for me because the university has exposed us [lecturers] to different kinds of technological resources within a short period of time".

Access to information
Using adopted technological resources (hardware and software) requires planning with clear guidelines from university policies and other written guides. As such, lecturers were frustrated with the detailed e-learning guidelines stipulated in the dry-run document. As L2 asserted. "I am confused which assessment type to use in my module out of many assessment activities suggested from the dry-run document" While other lecturers agreed with this assertion, contact with students seemed to be the major challenge because not all students had access to the internet to execute these suggested assessment activities. As such, L11 outlined, "The policy demands us to have access to all students registered in the module; this is impossible because not all students have access to Wi-Fi at home". The Covid-19 outbreak made universities provide lecturers with different training to enhance e-learning. This was emphasised by L8, who indicated, "I am overloaded with much information from Moodle training workshop and instructions from the training manual on how to create quiz and assignment within a week period of time".
Similarly, L3 agreed with L10, who said, "I only attended Zoom training ones and I am still not clear about its functions . . . ". In other words, giving much information for effective e-learning to lecturers within a short period of time seems to lead to technostress. As such, lecturers indicated contradicting information received from different sources or training workshops; for instance, L1 attested after a cluster meeting on e-learning was held, " . . . the policy it not clear about lecture hours but the cluster instructed us to use 1 hour. What if I want more time?" This shows that if access from e-mails, policy documents, meeting are contradicting, lecturers may be confused and end up having technostress. This is seen when L13 added, "The policy document states different types of assessment, but it not clear how many will be done on my module and for how many marks."

Communication
Being exposed to different sources or modes of communication with different instructions within a short time can overload lecturers and affect their readiness and attainment of effective e-learning. L4 indicates, "I always received call from cluster leader to submit recorded lecturers and assessment tasks prepared for online learning . . . ". Thus, calls received by lecturers demanding submissions causes pressure on them, which can influence them to make mistakes while preparing for e-learning. While lecturers are busy responding to calls, they are also expected to respond to e-mails simultaneously. All of the other lecturers agreed with L12, who said, "I always receive weekly e-mail communiques from the office of teaching and learning alerting me of different organised e-learning workshops, and some of their times are clashing." Thus, all workshops conducted are vital to lectures but when attendance time clashes it becomes too difficult for lectures to manage, and the strain and anxiety begin. This causes more technostress to lecturers since they are being given much information while they are also expected to make submissions within a short period. Similarly, lecturers also highlighted many different submissions being requested within a short period of time, L3 Said, "I am asked to submit module outline having details of content to be covered and assessment activities and the mode through which my module will be executed online . . . '. Moreover, much information on Covid-19 brings fear and stress to lecturers; this is witnessed when L2 and L9 agreed with L5, who said, "Even if I opened the university website or newsletter, all issues address e-learning and COVID-19, and this is strenuous to me."

Discussion of results
The rapid development of technology (internet, laptops, smartphones, SMS, VCT, LMS and others) has fundamentally changed the manner in which information is produced and consumed in the higher education landscape (Bates, 2018;Govender, 2021). There has been an acceleration in the development and use of digital technologies in teaching and learning given the pandemic during this 4 th Industrial revolution era boosting an educational revolution (Govender, 2021). Technology allows fast access to information, irrespective of space and time. However, Matthes et al. (2020) argue that too much information at a time is too much to handle and can cause an imbalance (misfit) between the environment (technology) and the users' cognitive levels. Consequently, Lang (2000) asserts that people have limited cognitive capacity to input, process, store and retrieve information; thus, too much information can lead to information overload on the use of technology.
This suggests that information overload may lead to lecturers' institutional technostress, where lecturers receive too much information from the university policy document with details on how to go about enhancing e-learning. Evidently, research findings indicated that lecturers' cognitive levels could not process information from the dry-run document sufficiently because it had many details. For instance, one lecturer showed confusion about which assessment task to use out of those stipulated in the policy document for e-learning. When individual lecturers give more energy to process assessment issues, they are likely to have less energy to process other curriculum/subject concepts for effective e-learning, leading to technostress (Matthes et al., 2020;Mpungose, 2020a).
Findings further indicated that lecturers' technostress is caused by university policies that seek lecturers to be "magicians" and to do what is beyond their scope (cognitive level). For instance, university policies demanded that lecturers get hold of all registered students in a module before e-learning commenced, while the majority of students stay in remote areas where there is no access to the Internet, and no provision made by the university for students to have access to Wi-Fi. Referred by Delpechitre et al. (2019) as the dark side of technology because information overload can force employees to be selective and biased to opt for one source of information over another, and this can harm the organisation. In other words, when lecturers are overloaded with ambiguous or too much information on ways to effect e-learning by the university (policies), they are likely to be biased, select what works for them, and ignore other information. This can lead to misinterpretation of information because of institutional technostress.
For example, lecturers had to choose either quizzes or assignments on Moodle for online assessment in their modules/subjects because they were bombarded with different online assessment tasks to select from, some of which are not applicable in their modules/subjects. Moreover, the availability of information has a relationship with lecturers' performance; once there is information overload, this puts additional demands on the lecturers. As a result, their performance declines because of technostress. Thus, e-learning can hardly be attained in the context of COVID-19 in South Africa because of different stressors which cause technostress (Harris et al., 2015;Khoza, 2019). The findings indicated that demands were made on lecturers by training workshops and by the management to use Zoom for online lecturers; however, it was unclear how many hours should be spent per lecture. As a result, lecturers were frustrated and stressed while resisting attendance to organised e-learning trainings/workshops, which negatively impacted their e-learning performance. Harris et al. (2015) agree with Karr-Wisniewski and Lu (2010) that the high levels at which practitioners (lecturers) receive communication can lead to communication overload since they are expected to adhere or respond to multiple modes of communication, including but not limited to e-mails, text messages and calls on their mobile devices, newsletters, and communiques to enhance e-learning. Studies have shown that interpretations of these communication modes consume much of the lecturers' time and can sometimes be ambiguous. Hence this has a negative impact on lecturers' performance on e-learning (Dhir et al., 2018;Matthes et al., 2020). Lecturers indicated that while they were at home because of the lockdown and frightened by the COVID-19 death toll, they kept receiving e-mails and calls from cluster leaders and the office of teaching and learning seeking submission of recorded lectures and assessment tasks for e-learning. This led to lecturers' social technostress, which, according to Chen and Yan (2016), is enhanced by multitasking which seeks lecturers to switch activities or tasks on a single or different device. In other words, social technostress is influenced by simultaneously handling mobile calls while busy interpreting e-mails on the laptop to affect e-learning. Moreover, this information overload has the potential to invade (techno-invasion) lecturers' personal life. For instance, a call or an e-mail regarding a work activity can be received while a lecturer is at home serving family needs (Fuglseth & Sørebø, 2014).
Social technostress results from communication overload because of the overwhelming amount of information that is produced by e-mails, mobile phones and other technologies used by university management; this has an adverse impact on lecturers' performance (Delpechitre et al. (2019);(Fuglseth & Sørebø, 2014). Evidently, lecturers were being asked to attend various e-learning training workshops (Zoom and Moodle) and cluster meetings to discuss issues pertaining to e-learning. According to Matthes et al. (2020), this has a negative impact on lecturers' e-learning planning and time management and consequently affects their performance. In this case, lecturers end up confused and not knowing what to do, or when and how, because they depend on the "noise" (ideas from others) from the surroundings (colleagues and workshops) to address the needs of the society they function in (Khoza, 2019;Mpungose, 2020b). In other words, social technostress ignores the presence of the subject need and personal needs of lecturers and puts the focus on others. This results in lecturers attending workshops to please others (cluster leaders or colleagues) instead of taking information from the workshop that can address their own needs and subject need in order to effect e-learning.
Lecturers indicated that even though it took some time to gain access to some of the technological hardware resources like routers with data bundles for Wi-Fi access, they did gain access to it, including university-provided laptops. However, the findings showed that when using new software technologies like the Zoom VCT and Kaltura video software, including Moodle LMS, which come with unique system features, this can lead to a misfit between these features and lecturers' abilities. Thus, when lecturers face difficulties in handling system features, they experience technology overload; this leads to technological resources technostress, and thus a decrease in the level of e-learning performance (Khoza, 2019;Lee et al., 2016). Moreover, the adoption of new technological resources comes with new and complex system functions capable of enhancing the misfit with lecturers' abilities to effect e-learning. Thus, it is vital for the university to adopt and use technologies that can facilitate e-learning and not frustrate lecturers.
The findings showed that even though Zoom VCT, Moodle LMS, WhatsApp and Facebook (SMS) were adopted by the university and provided to the lecturers for use, the majority were struggling to create quizzes and assignments on Moodle LMS and to create group breakouts on Zoom, and struggling to respond to all chats on WhatsApp. In other words, the adoption of excessive technological resources for teaching and learning within a short period can crowd out the usability of the individual lecturer and hinder the effective implementation of e-learning, resulting in a reduction in university throughput (Harris et al., 2015;Selwyn & Stirling, 2016). This suggests that universities should adopt technologies that can fit a specific task in a particular context in order to yield maximum benefits rather than supplementing the existing LMS with new software that has challenging system features that will cause lecturers' technological stress.
During the interviews, lecturers indicated that they were aware that they were facing institutional, social and technological resources technostress because, during the first few weeks of e-learning, they had different physical and psychological technostress symptoms, including headache, insomnia, backache, anxiety and general body pains. Even though they consulted medical doctors/physicians, they were not aware of other controlling or mitigating measures. Consequently, Ragu-Nathan et al. (2008) suggest three possible measures to reduce technostress, which are referred to as inhibitors. These include technical support provision (institutionalised support), literacy facilitation (support of lecturers' levels of information communication technology literacy) and involvement facilitation (strategies that strengthen lecturer engagement in new technology). In other words, universities should not only provide new technologies to lecturers for e-learning but should also be aware of lecturers' technostress and be able to put in place mitigating measures for technostress in the context of COVID-19.
The university should provide technical support by employing educational technologists-not technicians-to support lecturers with online pedagogies on newly adopted technologies and a 24hour technology help desk. Sufficient workshops according to lecturers' needs can be organised to capacitate lecturers with technology skills (literacy facilitation) to deal with all hardware and software demands to enhance effective e-learning. While technology has the potential to improve learning, the appropriate implication and planning are vital for the successful attainment of knowledge development (Govender, 2021). Lecturers need to be involved in decision-making before the type of technology is adopted, and incentive systems must be in place to motivate lecturers; in this way, they can feel involved and welcomed and thus effectively utilise any adopted technology for e-learning. Vithal (2018) further argues that the scholarship of teaching and learning, ranging from broad areas of teaching and learning: research and innovation, to recognition, rewards and academic promotions; professional development and practice, and policy review and development, can bring radical transformation provided it is well conceptualised and planned inclusively and multidimensionally in Higher Education. Hence this suggests that to bring effective radical technological changes, it is imperative that universities in South Africa follow suit and devise IT policies like the leading universities in the country and around the world who have demonstrated their readiness to migrate online. A foundational step is to introduce Educational Technology Centres across campuses at a University. As discussed, technostress can cause serious harm to IT users when left unattended; these centres will conduct research to establish relevant theories, hardware and software for teaching, learning, research, and assessment/evaluation, besides raising techno awareness. These centres can also train staff and students in all relevant types of technology, especially online technology, in anticipation of uncertainty in education, as occurred during the COVID-19 outbreak.
In compiling these findings, the P-E fit model showed up a misfit between the lecturers' abilities to use adopted technology and the environmental demands caused by the lecturers' technostress (institutional, social and technological resources). This implies that lecturers are the ones who are at the centre of all different types of technostress, experiencing these levels of technostress at the grassroots level during teaching and learning (Figure 1). Both Korthagen et al. (2013) and Khoza (2019) believe that the major control or prevention inhibitor/remedy for technostress in e-learning resides with the lecturer, who must use the inner-self/self-identity to teach from within, being driven by love, passion, creativity, resilience, flexibility, motivation, beliefs, goals, rationale and others. This suggests that through lecturers 'self-identities, they can self-direct to propel themselves, irrespective of challenges faced in the context of COVID-19.
In summary, answering the research questions, the findings proposed three levels of technostress (institutional, social and technological resources) infused or caused by information, communication and technology (system) overload. All of these revolve around the lecturers, who have unique abilities because of self-identity, that can help to overcome these challenges ( Figure 2). The overload induces the stimuli, which is stress in the environment. Adept individuals have less to no fluctuations in performance.

Conclusion
This qualitative case study used the P-E fit framework of stress and the SOR model to explore lecturers' technostress in e-learning during the COVID-19 shutdown/lockdown. Notwithstanding methodological limitations (small-scale study), findings suggest three levels of technostress that lecturers can experience during e-learning. Possible inhibitors were recommended, such as the provision of technical support, literacy facilitation, involvement facilitation and others. However, lecturers should closely consider themselves as the major inhibitor in mitigating technostress by teaching from within (self-identity) to consider values (love, passion and others) in order to address personal needs (strengths and weaknesses) before addressing the module/subject and society needs. Theoretically, the P-E fit framework integrated with the SOR model proposes a conceptual model that has revealed itself to be the best to understand and ease levels of technostress while ensuring interactive and effective e-learning through the use of adapted and available technological resources.
Online pedagogic practices suppress or diminish human expression. Hence, research is needed around the ability to realign and readjust to face-to-face lecturing, which can be challenging, significantly when an individual has evolved from incept with online digital technologies to an online digital native. Thus, the possibility of resulting in a wave of anxiety and emotional stress when normality is returned in Higher Education Institutions.
Finally, most of the instructional and theoretical interventions provided in this article require more empirical research. For this research to advance the field of educational technology in the use of technologies such as the Zoom VCT, Moodle LMS, SMS and others, mere participation in complicated conversations is not enough, and further research is required.