Rapid screening of benzyl dodecyl dimethyl ammonium bromide co-metabolic degrading bacteria based on NIR hyperspectral imaging technology.

As a typical disinfectant, the use of benzyl dodecyl dimethyl ammonium bromide (BDAB) has dramatically increased since the emergence of SARS-CoV-2, posing a threat to environmental balance and human health. Screening BDAB co-metabolic degrading bacteria is required for efficient microbial degradation. Conventional methods for screening co-metabolic degrading bacteria are laborious and time-consuming, especially when the number of strains is large. This study aimed to develop a novel method for the rapid screening of BDAB co-metabolic degrading bacteria from the cultured solid medium using near-infrared hyperspectral imaging (NIR-HSI) technology. Based on NIR spectra, the concentration of BDAB in the solid medium can be well predicted by partial least squares regression (PLSR) models, non-destructively and rapidly, with Rc2 > 0.872 and Rcv2 > 0.870. The results show that the predicted BDAB concentrations decrease after degrading bacteria utilization, comparing with the regions where no degrading bacteria grew. The proposed method was applied to directly identify the BDAB co-metabolic degrading bacteria cultured on the solid medium, and two kinds of co-metabolic degrading bacteria RQR-1 and BDAB-1 were correctly identified. This method provides a high-efficiency method for screening BDAB co-metabolic degrading bacteria from a large number of bacteria.

Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil.

In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

Electrolyte Analysis in Blood Serum by Laser-Induced Breakdown Spectroscopy Using a Portable Laser.

The fast and reliable analysis of electrolytes such as K, Na, Ca in human blood serum has become an indispensable tool for diagnosing and preventing diseases. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated as a powerful analytical technique on elements. To apply LIBS to the quantitative analysis of electrolyte elements in real time, a self-developed portable laser was used to measure blood serum samples supported by glass slides and filter paper in this work. The partial least squares regression (PLSR) method was employed for predicting the concentrations of K, Na, Ca from serum LIBS spectra. Great prediction accuracies with excellent linearity were obtained for the serum samples, both on glass slides and filter paper. For blood serum on glass slides, the prediction accuracies for K, Na, Ca were 1.45%, 0.61% and 3.80%. Moreover, for blood serum on filter paper, the corresponding prediction accuracies were 7.47%, 1.56% and 0.52%. The results show that LIBS using a portable laser with the assistance of PLSR can be used for accurate quantitative analysis of elements in blood serum in real time. This work reveals that the handheld LIBS instruments will be an excellent tool for real-time clinical practice.

Integrated System for On-Site Rapid and Safe Screening of COVID-19.

Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin.

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture.

BACKGROUND: Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form. OBJECTIVE: In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation. METHOD: For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 µg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS. RESULTS: The models' validation criteria displayed excellent recoveries and lower errors of prediction. CONCLUSIONS: The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results. HIGHLIGHTS: Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.

Analytical Performance and Greenness Evaluation of Five Multi-Level Design Models Utilized for Impurity Profiling of Favipiravir, a Promising COVID-19 Antiviral Drug.

In 2018, the discovery of carcinogenic nitrosamine process related impurities (PRIs) in a group of widely used drugs led to the recall and complete withdrawal of several medications that were consumed for a long time, unaware of the presence of these genotoxic PRIs. Since then, PRIs that arise during the manufacturing process of the active pharmaceutical ingredients (APIs), together with their degradation impurities, have gained the attention of analytical chemistry researchers. In 2020, favipiravir (FVR) was found to have an effective antiviral activity against the SARS-COVID-19 virus. Therefore, it was included in the COVID-19 treatment protocols and was consequently globally manufactured at large-scales during the pandemic. There is information indigence about FVR impurity profiling, and until now, no method has been reported for the simultaneous determination of FVR together with its PRIs. In this study, five advanced multi-level design models were developed and validated for the simultaneous determination of FVR and two PRIs, namely; (6-chloro-3-hydroxypyrazine-2-carboxamide) and (3,6-dichloro-pyrazine-2-carbonitrile). The five developed models were classical least square (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm-partial least squares (GA-PLS), and artificial neural networks (ANN). Five concentration levels of each compound, chosen according to the linearity range of the target analytes, were used to construct a five-level, three-factor chemometric design, giving rise to twenty-five mixtures. The models resolved the strong spectral overlap in the UV-spectra of the FVR and its PRIs. The PCR and PLS models exhibited the best performances, while PLS proved the highest sensitivity relative to the other models.

Exponentiated Odd Lomax Exponential distribution with application to COVID-19 death cases of Nepal.

This study suggested a new four-parameter Exponentiated Odd Lomax Exponential (EOLE) distribution by compounding an exponentiated odd function with Lomax distribution as a generator. The proposed model is unimodal and positively skewed whereas the hazard rate function is monotonically increasing and inverted bathtubs. Some important properties of the new distribution are derived such as quintile function and median; asymptotic properties and mode; moments; mean residual life, mean path time; mean deviation; order statistics; and Bonferroni & Lorenz curve. The value of the parameters is obtained from the maximum likelihood estimation, least-square estimation, and Cramér-Von-Mises methods. Here, a simulation study and two real data sets, "the number of deaths per day due to COVID-19 of the first wave in Nepal" and ''failure stresses (In Gpa) of single carbon fibers of lengths 50 mm", have been applied to validate the different theoretical findings. The finding of an order of COVID-19 deaths in 153 days in Nepal obey the proposed distribution, it has a significantly positive relationship between the predictive test positive rate and the predictive number of deaths per day. Therefore, the intended model is an alternative model for survival data and lifetime data analysis.

Modeling COVID-19 Impacts and Response Strategies in the Construction Industry: PLS-SEM Approach.

Policymakers are developing response strategies to reduce the impacts of COVID-19. However, developing response strategies without considering their relationships with the impacts of COVID-19 is ineffective. This study aims to model the causal relationships between COVID-19 impacts and response strategies in the construction industry, using Malaysia as a case study. To achieve this, a systematic literature review and semi-structured interviews with forty industry professionals were conducted, yielding 12 impacts and 22 response strategies. The impacts and strategies were inserted into a survey, and 107 valid responses were received. Exploratory factor analysis (EFA) was conducted to group the impacts and strategies. Then, partial least-squares structural equation modeling (PLS-SEM) was employed to identify the causal relationship between the impacts and strategies. The EFA results indicate that the underlying impacts are project- or material-related, and the underlying strategies are market stability and financial aid, supply chain and project support, and information and legislation. The PLS-SEM results indicate that supply chain and project support are required to address material-related impacts, and market stability and financial aid are required to address project-related impacts. This is the first paper that models the relationships between COVID-19 impacts and response strategies in the construction industry.

Impact of COVID-19-Related Stress and Lockdown on Mental Health Among People Living With HIV in Argentina.

BACKGROUND: The spread of severe acute respiratory syndrome coronavirus 2, causative agent of the coronavirus disease 2019 (COVID-19), has necessitated widespread lockdown to mitigate the pandemic. This study examines the influence of resilience on the impact of COVID-related stress and enforced lockdown on mental health, drug use, and treatment adherence among people living with HIV (PLWH) in Argentina. SETTING: PLWH residing predominantly in Buenos Aires Metropolitan Area and urban regions of Argentina were identified from a private clinic electronic database. METHODS: Participants completed an anonymous online survey to evaluate the impact of COVID-19 on economic disruption, resilience, mental health outcomes (depression, anxiety, stress, and loneliness), adherence to HIV treatment, and substance use. We performed ordinary least squares and logistic regressions to test whether resilient coping buffered the impact of economic disruption on mental health and drug use during quarantine. RESULTS: A total of 1336 PLWH aged 18-82 were enrolled. The impact of economic disruption on mental health ΔF(1,1321) = 8.86, P = 0.003 and loneliness ΔF(1,1326) = 5.77, P = 0.016 was buffered by resilience. A 3-way interaction between resilient buffering, stress, and sex was significant ΔF(1,1325) = 4.76, P = 0.029. Participants reported less than excellent adherence to medication (33%), disruption to mental health services (11%), and disruption to substance abuse treatment (1.3%) during lockdown. DISCUSSION: The impact of COVID-stress and lockdown on emotional distress seemed mitigated by resilience coping strategies, and the buffering impact of resilience on perceived stress was greater among women. Results highlight PLWH's capacity to adhere to treatment in challenging circumstances and the importance of developing resilience skills for better coping with stress and adversity.

Cross-Sectional Analysis of Impulse Indicator Saturation Method for Outlier Detection Estimated via Regularization Techniques with Application of COVID-19 Data.

Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator. However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data. Meanwhile, we are interested in forecasting performance and covariate selection in the presence of outliers. IIS method uses Autometrics techniques to estimate the covariates and outlier as the number of covariates P > n observations. Besides Autometrics, regularization techniques are a well-known method for covariate selection and forecasting in high-dimensional analysis. However, the efficiency of regularization techniques for the IIS method has remained unexplored. For this purpose, we explore the efficiency of regularization techniques for out-of-sample forecast in the presence of outliers with 6 and 4 standard deviations (SD) and orthogonal covariates. The simulation results indicate that SCAD and MCP outperform in forecasting and covariate selection with 4 SD (20% and 5% outliers) compared to Autometrics. However, LASSO and AdaLASSO select more covariates than SCAD and MCP and possess higher RMSE. Overall, regularization techniques possess the least RMSE than Autometrics, as Autometrics possesses the least average gauge at the cost of the least average potency. We use COVID-19 cross-sectional data collected from 1 July 2021 to 30 September 2021 for real data analysis. The SCAD and MCP select CRP level, gender, and other comorbidities as an important predictor of hospital stay with the least out-of-sample RMSE of 7.45 and 7.50, respectively.

COVID-19 related distress in the Swedish population: Validation of the Swedish version of the COVID Stress Scales (CSS).

BACKGROUND: The COVID Stress Scales (CSS) assess health- and contamination-related distress in the face of a medical outbreak like the ongoing COVID-19 pandemic. Though the CSS is translated into 21 languages, it has not been validated in a Swedish national sample. AIM: Our general objective is to provide a translation, replication, and validation of the CSS and test its convergent- and discriminant validity in relation to anxiety, health anxiety, depression, and stress in the general Swedish population. We also present latent psychometric properties by modelling based on item response theory. METHODS: Participants consisted of 3044 Swedish adults (> 18 years) from a pre-stratified (gender, age, and education) sample from The Swedish Citizen Panel. Mental health status was assessed by validated instruments, including the CSS, PHQ-4, SHAI-14, and PSS-10. RESULTS: Results indicate that our Swedish translation of CSS has good psychometric properties and consists of 5 correlated factors. DISCUSSION: The CSS is useful either as a unidimensional or multidimensional construct using the CSS scales to measure key facets of pandemic-related stress. CONCLUSIONS: The findings support the cross-cultural validity of the CSS and its potential utility in understanding many of the emotional challenges posed by the current and future pandemics.

Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.

Burnout in hospital staff using partial least squares path modeling for job-person fit: The case of a tertiary referral hospital in southwest Iran.

Recent studies on burnout (BO) have included both individual and situational factors, referred to as job-person fit (JPF). The present study aimed to evaluate the prevalence rate of BO in the hospital staff working at a tertiary referral hospital in southwest Iran and then to highlight the importance of the person in the context of his/her work life. This cross-sectional study was conducted in 2020 on all hospital staff using a three-part questionnaire comprised of personal and work-situational factors, the Perceived Stress Scale (PSS), and the Psychological Empowerment Scale (PES). The partial least squares (PLS) path modelling and the neural network (NN) model were used to identify the significant variables within the BO dimensions. A total of 358 staff completed the questionnaire and were recruited for the study. Emotional exhaustion (EE) was seen in 137 medical staff (38.3%) and depersonalization (DP) was observed in 75 individuals (20.1%). Thinking about job change was the most important factor positively correlated with EE. Positive stress and work experience were among the most significant factors negatively associated with PA and DP, respectively. The hospital staff experienced BO in a way comparable to the national results. Work-situational and personal variables interacted with the three dimensions of BO in the hospital staff. More experienced staff also felt more accomplished and successful, resulting in the identification of a decreased level of DP and elevated PA.

A model of factors influencing COVID-19 vaccine acceptance: A synthesis of the theory of reasoned action, conspiracy theory belief, awareness, perceived usefulness, and perceived ease of use.

The aim of this study is to investigate the key factors influencing the acceptance of COVID-19 vaccines and develop a model based on the theory of reasoned action, belief in conspiracy theory, awareness, perceived usefulness, and perceived ease of use. The authors created and distributed a self-administered online questionnaire using Google Forms. Data were collected from 351 respondents ranging in age from 19 to 30 years, studying at the graduate and postgraduate levels at various public universities in Bangladesh. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method was used to analyze the data. The results indicate that belief in conspiracy theory undermines COVID-19 vaccine acceptance, thereby negatively impacting the individual attitudes, subjective norms, and acceptance. Individual awareness, on the other hand, has a strong positive influence on the COVID-19 vaccine acceptance. Furthermore, the perceived usefulness of vaccination and the perceived ease of obtaining the vaccine positively impact attitude and the acceptance of immunization. Individuals' positive attitudes toward immunization and constructive subjective norms have a positive impact on vaccine acceptance. This study contributes to the literature by combining the theory of reasoned action with conspiracy theory, awareness, perceived usefulness, and perceived ease of use to understand vaccine acceptance behavior. Authorities should focus on campaigns that could reduce misinformation and conspiracy surrounding COVID-19 vaccination. The perceived usefulness of vaccination to prevent pandemics and continue normal education will lead to vaccination success. Furthermore, the ease with which people can obtain the vaccine and that it is free of cost will encourage students to get vaccinated to protect themselves, their families, and society.

Comparison of Five Methods to Estimate the Parameters for the Three-Parameter Lindley Distribution with Application to Life Data.

We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.

Psychological predictors of vaccination intentions among U.S. undergraduates and online panel workers during the 2020 COVID-19 pandemic.

OBJECTIVE: Availability of safe and effective vaccines against COVID-19 is critical for controlling the pandemic, but herd immunity can only be achieved with high vaccination coverage. The present research examined psychological factors associated with intentions to receive COVID-19 vaccination and whether reluctance towards novel pandemic vaccines are similar to vaccine hesitancy captured by a hypothetical measure used in previous research. METHOD: Study 1 was administered to undergraduate students when COVID-19 was spreading exponentially (February-April 2020). Study 2 was conducted with online panel workers toward the end of the first U.S. wave (July 2020) as a pre-registered replication and extension of Study 1. In both studies, participants (total N = 1,022) rated their willingness to receive the COVID-19 vaccination and to vaccinate a hypothetical child for a fictitious disease, and then responded to various psychological measures. RESULTS: In both studies, vaccination intentions were positively associated with past flu vaccine uptake, self-reported vaccine knowledge, vaccine confidence, and sense of collective responsibility. Complacency (not perceiving disease as high-risk), anti-vaccine conspiracy beliefs, perceived vaccine danger, and mistrust in science/scientists were negative correlates of vaccination intentions. Constraints (psychological barriers), calculation (extensive information-searching), analytical thinking, perceived disease vulnerability, self-other overlap, and conservatism were weakly associated with vaccination intentions but not consistently across both studies or vaccine types. Additionally, similar factors were associated with both real and hypothetical vaccination intentions, suggesting that conclusions from pre-COVID vaccine hesitancy research mostly generalize to the current pandemic situation. CONCLUSION: Encouraging flu vaccine uptake, enhancing confidence in a novel vaccine, and fostering a sense of collective responsibility are particularly important as they uniquely predict COVID-19 vaccination intentions. By including both actual pandemic-related hesitancy measures and hypothetical hesitancy measures from past research in the same study, this work provides key context for the generalizability of earlier non-pandemic research.

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris.

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

No one is safe! But who's more susceptible? Locus of control moderates pandemic perceptions' effects on job insecurity and psychosocial factors amongst MENA hospitality frontliners: a PLS-SEM approach.

BACKGROUND: The research aimed to formulate and test a model concerning COVID-19 perceptions effects on job insecurity and a set of psychosocial factors comprising anxiety, depression, job burnout and job alienation in the Middle East and North African (hereafter, MENA) regional context. Also, the study attempted to examine whether locus of control can moderate these hypothesised linkages amongst customer service employees working in MENA hospitality organisations. METHODS: The study is based on a sample of 885 responses to an online survey and Partial Least Square Structural Equation Modelling (PLS-SEM). RESULTS: The main findings show the existence of a significant correlation between COVID perceptions and job insecurity and all psychosocial factors, i.e., more intense COVID-19 perceptions accompany higher levels of job insecurity, anxiety, depression, job burnout and job alienation. Furthermore, our results revealed that, in pandemic time, hospitality customer service employees with external locus of control are more likely to suffer higher alienation, anxiety and depression than those with internal locus of control. CONCLUSIONS: The research originality centres on the establishment that COVID-19 has a severe negative impact within the hospitality customer service labour force (in the MENA region). These effects were more profound for participants who claimed external locus of control than those with internal locus of control.

Development and testing of an explorative BPM acceptance model: Insights from the COVID-19 pandemic.

When emerging technologies transform an organization's way of working, explorative business process management (BPM) becomes a new challenge. Although digital innovations can boost process efficacy and business productivity, employees do not necessarily accept the implied work changes. We therefore looked at the increased digitalization efforts during the COVID-19 lockdowns, during which employees were forced to drastically rethink work by heavily depending on technology for communication and almost all business tasks. This global setting allowed us to scrutinize disruptive work changes and how employees can cope with disruptive work adaptations. We also looked into the explorative skillset needed to adapt to these changes. To theorize about an explorative BPM acceptance model, eleven hypotheses were supported based on a solid theoretical foundation. We followed a quantitative research design using partial least squares for structural equation modeling (PLS-SEM) at the university administration settings in two regions, including purposive sampling. Data analysis covered both a measurement model assessment and structural model assessment. Our findings reveal that employees' perceived work modalities, feeling creative and feeling flexible are more promising features than perceived influence and attitude related to explorative work and skill development. We also offer novel insights into explorative business process management (BPM) skills, and which skills are more productive in uncertain or dynamic working conditions. This research is a learning path for managers struggling with flexible or competitive business environments, and more specifically to facilitate employee willingness.