PurposeThe purpose of this study is to analyze the impact of technostress on the teachers’ willingness to use online Teaching Modes, with the moderating role of job insecurity in Pakistan.Design/methodology/approachHolistically, this study collected 242 samples using the convenient sampling technique for data collection. The response rate was 69.1%. The respondents of the study are academic staff working in private colleges and universities. The data are essentially collected by using the scales of technostress, job insecurity and willingness to utilize online teaching modes.FindingsThe results reveal a significant and negative relationship between technostress and the teachers’ willingness to use online modalities. Interestingly, job insecurity moderates the relationship between technostress and the teachers’ willingness to use online modalities.Research limitations/implicationsOnly academic staff of colleges and universities is considered in this study. In later studies, researchers may consider the school teachers as their potential respondents.Originality/valueThe results of the study provide important insight for the higher management of the academic institutes to motivate their employees to use online resources by using effective leadership and management skills during unforeseen events in the future.

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Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders. The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 firms with 45 distressed and 245 healthy firms for the period 2006-2016 and covers all sectors of Pakistan Stock Exchange. The study provides important insights on the role of different financial ratio in predicting financial distress and shows that estimated logit model produces higher accuracy rate in predicting financial distress. JEL Classifications: G01