The antecedents of digital collaboration through an enhanced digital platform for destination management: A micro-DMO perspective

While the tourism sector shifts towards digital transformation, Destination Management Organisations (DMOs) often struggle to adapt to their changing technological environment. This study explores the antecedents of digital collaboration and develops a framework for micro-DMOs to enhance effective destination management through digital technologies. An integrated sequential qualitative approach was adopted by conducting multi-phase interviews, in addition to designing and trialling a real-world trial digital platform. The research provides empirical evidence that digital collaboration is essential for micro-DMOs, necessitating them to transform their current “websites” into digital platforms which act as a hub for business stakeholders to actively be involved in. Antecedents of successful digital collaboration include mutuality, trust, control, and leadership which may be manifested differently from non-digital collaboration. Additionally, the study identifies three aspects for digital collaboration; marketing, networking and knowledge sharing that demands specific attention. Our results have theoretical, methodological, and practical implications for academia, industry and policymakers.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

Toward a new business model of retail industry: The role of brand experience and brand authenticity

While the customer-to-manufacturer (C2M) business model has received increasing attention as a new business model for e-commerce and retail industry, little is still known about it and the effect of its approach. This study aims to understand how brand-related stimuli in C2M environments affect customer responses as the worldwide COVID-19 pandemic. The outcomes reveal that the Sensory, affective, and intellectual aspects of brand experience positively influence brand authenticity. Brand authenticity has a positive effect on behavioral intention, such as reuse intention and word-of-mouth. Additionally, this research finds that social presence moderates the association between the sensory aspect of brand experience. Thus, this study can suggest a C2M business model as a means of sustainable operation of the retail industry to both researchers and practitioners in relation to the retail industry.     

A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

The effect of mandatory CSR disclosure on firm profitability and social externalities: Evidence from China

We examine how mandatory disclosure of corporate social responsibility (CSR) impacts firm performance and social externalities. Our analysis exploits China's 2008 mandate requiring firms to disclose CSR activities, using a difference-in-differences design. Although the mandate does not require firms to spend on CSR, we find that mandatory CSR reporting firms experience a decrease in profitability subsequent to the mandate. In addition, the cities most impacted by the disclosure mandate experience a decrease in their industrial wastewater and SO₂ emission levels. These findings suggest that mandatory CSR disclosure alters firm behavior and generates positive externalities at the expense of shareholders.     

Debt contract strictness and auditor specialization

The conflicts of interest among managers, shareholders and creditors resulting in agency costs, can be mitigated by restricting managers’ adverse behavior, through financial covenants to better align the various stakeholder interests. Thus, debt contract strictness represents an important aspect of agency costs between creditors, shareholders, and management that is not always captured by interest rates. The contract setting provides a unique opportunity to investigate how creditors may rely on auditors to alleviate information uncertainty stemming from reliance on management's financial reporting and thus alleviate the creditor's potential loss of invested capital. After controlling for borrower risks, loan characteristics, and audit factors, we show that auditor industry specialization is significantly associated with a reduction in the strictness of debt contracts, consistent with creditors viewing certain industry expert auditors as effective monitors against financial reporting manipulation aimed at the avoidance of debt covenant triggers that protect creditors against potential loss. Further, we find that the association between loan strictness and auditor specialization is attenuated by stronger corporate governance systems, external monitors, and prior lender relationships.     

Novel approaches to the regulatory control of financial services providers: The importance of cultural context

This study investigates the effectiveness of a public sector financial management initiative. Specifically, the powers awarded to the Irish Financial Services Ombudsman (FSO) in 2013 to name and shame malfeasance by financial service providers (FSPs) in its annual reports. As the first country to award such powers to its public sector financial ombudsman, Ireland represents a novel setting in which to test the impact of regulatory disclosure as a way to promote accountability and transparency. Our results show that the number of complaints lodged against malfeasants dropped in the immediate aftermath of this and, following a one-year lag, so did the percentage of complaints lodged that proceeded to a full investigation and legally binding finding. Despite the failure of such strategies in some jurisdictions, the Irish experience indicates that regulatory disclosure can, in line with Neo-Durkheimian institutional theory and consistent with the accounting and accountability literature, have considerable impact where and when contextual preconditions are met. These findings have important implications for the operationalisation of regulatory disclosure as an accountability enhancing measure in other jurisdictions.     

Disappearing investment-cash flow sensitivities: Earnings have not become a worse proxy for cash flow

According to a recent conjecture in the literature, earnings have become a poorer proxy for cash flow from operations over time. We find that since 1988, when cash flow statements started to be consistently reported in Compustat, the cash effectiveness of earnings has actually increased for a large sample of US manufacturing firms. This occurs despite the introduction of fair value accounting and increasing accounting accruals during the last three decades. Also contrary to the conjecture, using more comprehensive measures of cash flow does not restore the investment-cash flow sensitivity, which continues to be around 0.05 in more recent periods.     

Profit shifting and tax-rate uncertainty

Using firm-level data for 1,084 parent firms in 24 countries and for 9,497 subsidiaries in 54 countries, we show that tax-motivated profit shifting is larger among subsidiaries in countries that have stable corporate tax rates over time. Our findings further suggest that firms move away from transfer pricing and toward intragroup debt shifting that has lower adjustment costs. Our results are robust to several identification methods and respecifications, and they highlight the important role of tax-rate uncertainty in the profit-shifting decision while pointing to an adjustment away from more costly transfer pricing and toward debt shifting.     

M5 competition uncertainty: Overdispersion,distributional forecasting,GAMLSS, and beyond

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data face strong overdispersion and sporadic demand, especially zero demand. We discuss modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fail to address the data characteristics, due to the considered objective functions. Distributional forecasting provides a suitable modeling approach to overcome those problems. The GAMLSS framework allows for flexible probabilistic forecasting using low-dimensional distributions. We illustrate how the GAMLSS approach can be applied to M5 competition data by modeling the location and scale parameters of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawbacks, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.