The ASC 606 standard,revenue informativeness,and analysts forecast quality

We examine the adoption effect of ASC 606 on revenue informativeness, analyst forecast dispersion, and forecast errors. We find that the adoption of ASC 606 is associated with increases in revenue informativeness but decreases in analyst forecast accuracy and consensus. Such adoption effects are mainly temporary and focused in firms that are more affected by the new standard. Last, we find that firms using the full retrospective adoption method are associated with higher revenue informativeness and lower analyst forecast error than firms using the modified retrospective method.     

亭南煤矿205工作面运输顺槽超前支护支架应用研究

亭南煤矿205综采工作面运输顺槽顶板采用2架ZCZ12700/24/40和1架ZCZ12700/24/40A型超前支护支架进行支护。通过实际应用证明,超前支护支架支护性能优越,安全性高,便于操作,适合于大采高综采工作面超前支护中的应用。     

自然资源全要素概念模型构建

研究目的:在自然资源部国土空间统一规划和自然资源统一登记管理的需求下,各类自然资源的差异性造成了统筹的困难,自然资源全要素概念模型能够有效地构建统筹基础。研究方法:通过探究测绘地理空间信息与自然资源信息化的关系,分析从"土地"到"土地空间"及"地理空间"的转变、从"要素"到"全要素"的统一和"自然资源数据"与"泛地图"的关联,提出自然资源全要素概念模型待解决的问题,并以此建立自然资源全要素概念模型。研究结果:以自然资源全要素概念模型为基础,通过建立4个具有时空特征的概念模型实例,例证了自然资源全要素概念模型的可行性和通用性。研究结论:自然资源全要素概念模型有助于自然资源全要素统一建模,以支撑自然资源全要素的统一登记、数据库建设等。     

V-Matrix: A wave theory of value creation for big data

This paper examines the “V-Matrix” and provides a wave theory life cycle model of organizations’ adoption of big data. The V-Matrix is based on the big data five “V’s”: Volume, Velocity, Variety, Veracity, and Value and captures and enumerates the different potential states that an organization can go through as part of its adoption and evolution towards big data. We extend the V-Matrix to a state space approach in order to provide a characterization of the adoption of big data technologies in an organization. We develop and use a wave theory of implementation to accommodate a firm’s movement through the V-Matrix. Accordingly, the V-Matrix provides a life cycle model of organizational use of the different aspects of big data. In addition, the model can help organizations’ plan for decision-making use of big data as they anticipate movement from one state to another, as they add big data capabilities. As part of this analysis, the paper examines some of the issues that occur in the different states, including synergies and other issues associated with co-occurrence of different V’s with each other. Finally, this paper integrates the V-Matrix with other data analytic life cycles and examines some of the implications of those models.     

Synthesis of strategies in post-COVID-19 public sector supply chains under an intuitionistic fuzzy environment

Entities in public sector supply chains (SCs) often operate independently despite having interdependent objectives. Such a fragmented operational design poses several problems magnified by the presence of necessary public health measures fueled by COVID-19. This work contributes to the domain literature by introducing an overarching framework for synthesizing strategies in public sector SCs. The underlying component is the translation of information from the upstream to the downstream entities of the SCs, which is carried out by a Kano-enhanced quality function deployment. The proposed framework introduces intuitionistic fuzzy (IF) decision maps with the aid of the full consistency method to incorporate inherent interrelationships among strategies in the translation agenda. Under an IF environment that better captures judgment uncertainties, an actual case study of a multi-level public sector SC motivated by a government-funded project under the COVID-19 pandemic is demonstrated in this work. Findings of the case suggest that the government prioritizes meeting all project objectives. This requirement is reflected in the downstream SC. The project planning entity focuses on creating an overarching plan of operations, material request entity on complying with government procurement protocols, and maintaining public health and safety in operations for the procurement entity. Results show the effective synthesis of strategies across the SC, ensuring SC integration and collaboration. The case study demonstrates that maintaining public health and safety is a significant component of post-COVID-19 public sector SCs. Several practical insights on the synthesis of public sector SC strategies are also provided in this work.     

Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing

Changes to the General Ledger (GL) represent a link between transactional business events from Journal Entries and prepared financial statements. Errors in these very large datasets can result in material misstatements or account misbalance. Unfortunately, a plethora of conditions renders traditional statistical and non-statistical sampling less effective. As a full-population examination procedure, Multidimensional Audit Data Sampling (MADS) mitigates these issues. In conjunction with top practitioners, we utilize a design science approach in applying the full-population MADS methodology to a real dataset of GL account balance changes. Issues such as the effectiveness of internal controls, detection of low-frequency high-risk errors, and earnings management concerns are addressed. This paper demonstrates how vital insights can be gained using MADS. More importantly, this approach also highlights the exact portion of the population that is error-free with respect to the auditors' tests.