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Visuals are a crucial part of strategizing, whether it be through the use of body gesture, the crafting of strategy presentations, or the use of new media technologies from videoconferencing through to data visualization. Yet studying these aspects of the strategy process is methodologically challenging and requires careful attention to how the data is collected and what questions the data analysis can address. In this chapter, we lay out choices for scholars and the opportunities these afford to new and promising agendas in strategy and management research.

Micro and small enterprises (MSEs) represent 99% of Peruvian firms, contribute 42% of Peru's Gross Domestic Product, and employ half of the country's labor force. Despite their relevance for the Peruvian Economy, they have low survival rates and are characterized by low productivity and processes inefficiencies. This chapter explores whether the adoption of Business and Supply Chain Management (SCM) practices influences MSEs' performance. We conducted a field study using data from 50 MSEs located in Piura, Peru, specifically from trade, service, and manufacturing sectors. We used the data collection guidelines from the MIT GeneSys to measure the firms' adoption of Business and SCM practices. Our results show that MSEs with higher adoption of Financial Planning, Supply Chain Planning, Supplier Relationship Management, Marketing, Procurement, and Stock Control practices are more likely to have higher revenue (i.e., sales). In addition, a multiple regression analysis reveals that while SCM practices do not seem to directly explain productivity growth in MSEs (as business practices do), they, however, seem to influence the performance of business practices, and, thus, have an indirect effect on the productivity growth of MSEs.

This chapter covers total quality management (TQM) with respect to the Lean and Six Sigma methods used to improve the effectiveness and efficiency of hospitality operations. In the first section, TQM is discussed. In the second section, Lean and Six Sigma techniques are examined individually. Then, Lean and Six Sigma are connected with an example of a housekeeping case study that applies both methodologies. In the third section, business analytics are explored and statistical process control analysis is demonstrated using a hotel room cleanliness example. The fourth section summarizes the concepts of change management, which is critical for embracing the philosophies of TQM. Finally, project management is discussed in the fifth and last section.

Low productivity in construction is typically blamed on the seemingly complex and chaotic nature of construction, which emerges as the stakeholders do not have an adequate picture of the evolving situation. The ever-increasing volume of situation data owing to the recent advances in IoT devices and reality capture platforms provide a unique opportunity to capture the actual situation data of construction projects accurately at a fraction of the cost compared to manual status tracking and reporting. This paper aims to investigate the concept of a situation picture, challenges in collecting situation data and its benefits.

Empirical data is collected through interviews in California and Finland, and by organizing workshops.

We contribute to literature on managing operational information by defining the concept of a situation picture in the context of construction, specifically from the blue-collar’s perspective during on-site activities. We present the key components of a conceptual information model that represents a situation picture in construction.

The applicability of conceptual information model of situation picture is not tested in practice, but the model will provide a starting point for research to comprehensively integrate social and digital information exchange for improving workflow.

The paper claims that designing and building comprehensive information management infrastructure would contribute to solving the problems of low productivity, quality and safety in construction projects.

Research on situation picture and situation awareness is scarce in the context of construction. The study links various information management technologies and practices to actual construction productivity.

In this chapter, the authors discuss how visual artifacts may support the analysis and interpretation of qualitative data in organization studies. They draws on their own experience as well as other scholars’ published work to explore the distinctive affordances of visual forms. In particular, the authors identify four roles – namely “mapping,” “analyzing,” “conceptualizing,” and “communicating” – that visual artifacts play to help us move from raw qualitative data to a compelling conceptual product.

Specifically, the use of visuals for “mapping” involves directly coding data into visual forms such as cognitive maps, flow charts or relational diagrams, an approach that may offer a useful complement to traditional verbal coding. Using visuals for “analyzing” implies either comparing, aggregating or decomposing previously constructed visual maps, or drawing directly on verbal data to develop visuals such as analytical flow charts, process replication maps, and trend charts. Using visuals for “conceptualizing” involves rising above the data to develop more abstract representations of concepts and relationships, while maintaining recognizable connections to empirical phenomena. While conceptual models can take a wide variety of forms, the authors illustrate, in particular, the use of visuals to represent linear, dialectic and multi-level process theories. Finally, the authors consider the importance of visualizations for “communicating” insights as well as for developing them, and the inextricable linkages between the two.

The authors conclude by discussing some of the strengths and weaknesses of visualization and by considering how new technologies may offer further possibilities for useful and insightful visual representations of qualitative data that can enhance theory-building.

This study investigates the relationship between lean manufacturing practices, management accounting and performance measurement (MAC & PM) practices, organizational strategy, structure, and culture, and facility performance. We extended past research by examining the relationships between lean manufacturing, MAC & PM practices and performance in a broader organizational context. Our study was performed using survey data provided by managers and executives at 368 facilities that had contacted the Shingo Institute for information or that had entered a Shingo Prize competition. Consistent with past research we found a significant positive association between lean manufacturing practices and lean MAC & PM practices. We found that greater employee empowerment, use of process performance measures, and use of lean accounting practices were driven primarily by lean strategy and secondarily by the extent of lean manufacturing practices. We also found that changes in organization structure to support lean are driven primarily by lean strategy and secondarily by lean manufacturing practices. Change toward lean culture, on the other hand, is driven by the extent of lean manufacturing practices. Further, we found that emphasizing process performance measures does not reduce emphasis on results performance measures and emphasizing results performance measures leads to improved financial performance. Process and results measures are being used in tandem and value stream costing has not replaced traditional accounting. The results of our study provide important insights for managers of companies engaged in lean transformation and for academics who teach or research lean accounting.

Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences has received much attention. In this work we make a surprising claim. Clustering of time series subsequences is completely meaningless. More concretely, clusters extracted from these time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work.