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Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.

Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.

Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.

The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.

The aim of this article is to assess the macroeconomic consequences of some specific aspects of financialization (i.e. share buy-back) using a hybrid post-Keynesian model of growth and distribution based on Kaldorian and Kaleckian characteristics.

The study follows a post-Keynesian approach and deals with financialization issues by implementing several numerical simulations.

The numerical simulations reveal the negative real impacts of massive share repurchases on the rate of accumulation because they immediately siphon off revenues directly intended for investment projects. Moreover, the negative effect of share buy-backs is reinforced especially when firms' investment decisions are more sensitive to a variation in retained earnings. Next, this macro-model also reproduces several well-known figures of the Kaleckian tradition and the paradox of costs.

The present article can be considered as a starting point for further theoretical extensions and requires empirical validation.

The Kaldor-Kalecki macro-model could be useful for policymakers who are interested in containing some of the negative excesses of financialization.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

The main purpose of this study is to examine the impact of imported inputs on firms' productivity in selected South Asian economies, namely Pakistan, India and Bangladesh. Furthermore, this study explores the complementarity between firms' capabilities and imported inputs in an augmented productivity framework.

A dataset comprising 7117 manufacturing firms of selected South Asian economies was taken from the World Bank for 2013 and 2014. The empirical analysis was based on stochastic frontier models, the ordinary least square method and instrumental variable estimation techniques.

The empirical results show that imported inputs have positive and significant effects on the firms' productivity in the selected countries. Moreover, the study findings demonstrate that firms' capabilities play a complementary role in expanding the firms' production frontier.

The study outcomes suggest that reducing tariffs on imported inputs will enhance the firms' productivity in the selected emerging economies. However, the study further finds that the potential gain of imported inputs is conditional on the firm's capabilities. It implies that firms operating in these countries can improve their performance by allocating more resources to capabilities, such as workers’ training, management and internal R&D effort.

The existing literature on the subject is sceptical about the positive impact of imported inputs on firms' productivity in the case of developing countries. In this regard, the shortage of skilled labour and firms' capabilities are compelling rationales that need to be explored. Thus, the potential contribution of the study lies in explaining the moderating role of firm's capabilities operating in the selected emerging economies in the nexus of imported inputs and productivity.

The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).

To determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.

The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).

This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.

This study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.