Investor sentiment and management earnings forecast bias

This study investigates whether investor sentiment is associated with behavioral bias in managers’ annual earnings forecasts that are generally issued early in the year when uncertainty is relatively high. I provide evidence that management earnings forecast optimism increases with investor sentiment. Furthermore, I find that managers’ annual earnings forecasts are more pessimistic during low‐sentiment periods than during normal‐sentiment periods. Since managers lack incentives to further deflate stock prices during a low‐sentiment period, this evidence indicates that sentiment‐related management earnings forecast bias is likely to be unintentional. In addition, I find that the relationship between management earnings forecast bias and investor sentiment is stronger for firms with higher uncertainty, consistent with investor sentiment having a greater influence on management earnings forecasts when uncertainty is higher.     

How new venture initial public offerings benefit from international operations: a study of human resource value

When internationalization goes beyond simply having international sales to having international operations, organizations can benefit from diversity of ideas and knowledge. Our study focuses on a special class of companies called new venture firms. As younger organizations, they may be well equipped to embrace the unique benefits of international diversity. However, new ventures may not be equal in this regard; therefore, our study also explores the moderating effect of human resource value for these firms.     

Are their designs iterative or fixated? Investigating design patterns from student digital footprints in computer-aided design software

This paper investigates iteration and fixation in design by mining digital footprints left by designers. High school students used computer-aided design software to create buildings in an urban area, with the goal of applying passive solar design techniques to ensure optimal solar gains of the buildings throughout a year. Students were required to complete three different designs. Fine-grained data including design actions, intermediate artifacts, and reflection notes were logged. Computational analytics programs were developed to mine the logs through three indicators: (a) frequency of the action of using energy analysis tools; (b) solar performance of the final designs; and (c) difference in solar performance between the prototype and final designs. Triangulating results from the indicators suggests three types of iteration—efficacious, inadequate and ineffective. Over half of the participants were detected as being efficacious iterative during the first design and becoming more and more fixated toward the end of the project, which resonates with previous findings on fixation effect among college students and professional designers. Overall the results demonstrate the power of applying computational analytics to investigate complex design processes. Findings from this work shed light on how to quantitatively assess and research student performance and processes during design projects.     

Do Not‐For‐Profits Need Their Own Conceptual Framework?

This paper raises the issue of whether not‐for‐profit (NFP) oganisations require a conceptual framework that acknowledges their mission imperative and enables them to discharge their broader accountability. Relying on publicly available documentation and literature, it suggests the current Conceptual Frameworks for the for‐profit and public sectors are inadequate in meeting the accountability needs of NFPs. A NFP‐specific conceptual framework would allow the demonstration of broader NFP‐specific accountability and the formulation of NFP‐appropriate reporting practice, including the provision of financial and non‐financial reporting. The paper thus theoretically challenges existing financial reporting arrangements and invites debate on their future direction.