Measuring the effects of monetary policy in Pakistan: a factor-augmented vector autoregressive approach

This paper examines the effects of monetary policy on macroeconomic variables in Pakistan’s economy using a data-rich environment. We used the factor-augmented vector autoregressive (FAVAR) methodology, which contains 115 monthly variables for the period 1992:01 to 2010:12. We compared the results of VAR and FAVAR model and the results showed that FAVAR model explains the effects of monetary policy which are consistent with the theory and better than the VAR model. VAR model shows the existence of price puzzle and liquidity puzzle in Pakistan while FAVAR model did not provide any evidence of puzzles. Interest rate negatively influences prices, hence interest rate is a good instrument for controlling inflation in Pakistan but it takes a lag of 5 months. The transmission of monetary policy shock is faster in case of prices as compared to output in Pakistan. FAVAR model supports the effectiveness of interest rate channel in Pakistan.     

The establishment of industrial branding through dyadic logistics partnership success (LPS): The case of the Malaysian automotive and logistics industry

Logistics partnerships across dyadic and triadic relationship networks have been the basis of extensive research in the extant literature. It is well understood that competitive advantage within logistics and distribution and within supply chains are driven by value-adding aspects of not only the core competencies of each tier in the network, but also via tangential supporting factors. It is also well understood that there is a strong relationship between logistics and distribution and marketing functions, particularly in product-based organisations. In addition to deepening our understanding of how successful partnerships throughout the source–make–deliver continuum can be optimised, this research also seeks to identify how a supporting element in terms of industrial branding and marketing can lead to relational sustainability. Hence, this paper outlines the relationship between logistics partnership success (LPS) factors and the inherent link to industrial branding establishment and business sustainability within the Malaysian automotive industry (specifically, car manufacturers). Adopting a multiple case study approach, findings suggest that there is a strong association between logistics service performance (LSP) provided by the third party logistics provider (TPLP) and the development and support of a mutual brand image for both the TPLP and the car manufacturer. We contribute to the extant literature on logistics partnership and industrial branding through linking LSP with the creation and maintenance of an industrial branding strategy for both parties in the context of the Malaysian automotive industry.     

Multinationals from emerging economies: Strategies,challenges, and the way forward

Multinationals from emerging economies are relatively new players in the global economy. This Special Issue includes five articles that cover several important aspects of the market and nonmarket strategies pursued by emerging market multinational corporations (EMNCs). Given that only a few firms from emerging markets embrace the possibilities and challenges of going global, we highlight the need to examine country, industry, and firm level antecedents of the emergence of EMNCs. We also draw attention to the need to study the role played by immigrant populations in EMNC internationalization.     

Bank systemic risk: An analysis of the sovereign rating ceiling policy and rating downgrades

We investigate the impact that the sovereign ceiling policy has on financial stability. In the event of a sovereign rating downgrade, we find that the rating agencies' sovereign ceiling policy leads to a disproportionate downgrade of the most creditworthy financial institutions in the economy and results in increased systemic risk. This asymmetric variation in bank ratings also impairs equity growth that further exacerbates bank insolvency. Our results are robust to several matching techniques, such as propensity score matching and entropy balancing, falsification tests, subsample analyses, alternative empirical proxies and model specifications.     

Cultural influences on future transportation technology usage: The role of personal innovativeness,technology anxiety and desire

This study develops and validates a model, based on personal cultural values theory and psychological research, in relation to technology adoption. The model focuses specifically on the future use of on-demand air mobility (ODAM), which is expected to have significant implications for city commuting and personal well-being in the years ahead. We use a path modelling approach, in addition to recently advanced analytical methods such as the finite mixture partial least squares (FIMIX-PLS), measurement invariance of composite models (MICOM) and multi-group analysis, to validate the model using a data set of 627 young consumers from the Czech Republic. The research model explains 45.2% variation in the future use of ODAM using our global model. This variance explained in the future use of ODAM increases to 62.3% and 64.5% respectively, when we segment our data set into two groups. The results also show that tradition has significant influence on technology anxiety, personal innovativeness and desire to use ODAM. Independence positively affects personal innovativeness but not the desire to use ODAM. We also find that technology anxiety influences the desire to use, which in turn influences the future use of ODAM. However, we find group differences in the influence of ambiguity intolerance on technology anxiety, desire and personal innovativeness. Thus, the study also evaluates the existence of significant differences between two groups in our data set. Overall, the study suggests that individual cultural values play a particularly important role in influencing the future use of ODAM through psychological characteristics. The research implications of the study are discussed in the article.     

The role of social networks in the adoption of competing new technologies in Ghana

We use a detailed dataset to examine the impact of social networks, conditional on contextual and individual confounders, on farmers' adoption of competing improved soybean varieties in Ghana. Based on the contagion conceptual framework, we employ a spatial autoregressive multinomial probit model to examine how neighbours' varietal and cross-varietal adoption of improved varieties affect a farmer's adoption decision in the social network. Our results show that adoption decisions in a network tend to converge on one variety, such that beyond a threshold of adopting neighbours of that improved variety, the cross-varietal effects tend to lose significance in the network. If the shares of adopting neighbours of the improved varieties are equal, we find evidence that farmers are not more likely to adopt either improved variety compared to farmers with no neighbours who have adopted the improved varieties. The findings demonstrate the significance of neighbourhood effects in the adoption of competing technologies.     

Utilising farm-level panel data to estimate climate change impacts and adaptation potentials

We combine farm accounting data with high-resolution meteorological data, and climate scenarios to estimate climate change impacts and adaptation potentials at the farm level. To do so, we adapt the seminal model of Moore and Lobell (2014) who applied panel data econometrics to data aggregated from the farm to the regional (subnational) level. We discuss and empirically investigate the advantages and challenges of applying such models to farm-level data, including issues of endogeneity of explanatory variables, heterogeneity of farm responses to weather shocks, measurement errors in meteorological variables, and aggregation bias. Empirical investigations into these issues reveal that endogeneity due to measurement errors in temperature and precipitation variables, as well as heterogeneous responses of farms toward climate change may be problematic. Moreover, depending on how data are aggregated, results differ substantially compared to farm-level analysis. Based on data from Austria and two climate scenarios (Effective Measures and High Emission) for 2040, we estimate that the profits of farms will decline, on average, by 4.4% (Effective Measures) and 10% (High Emission). Adaptation options help to considerably ameliorate the adverse situation under both scenarios. Our results reinforce the need for mitigation and adaptation to climate change.