Dynamic capabilities are central to firms' strategic decision-making and have received increasing attention in recent years due, in part, to the development of the information society. This research explores how web-based and human information sources interact and advance the development of dynamic capabilities. We use a mixed-methods approach executed in two studies. Study 1 reports insights gleaned from in-depth interviews with 12 senior managers. Study 2 provides the findings from a survey completed by 139 senior managers. The analysis indicates that the use of web-based and human information sources facilitates all dynamic capabilities types – sensing, seizing, and reconfiguring. Furthermore, sensing mediates the effects of the information sources on seizing and reconfiguring. This research highlights the strategic potential of using business information sources to advance dynamic capabilities while differentiating between the most commonly used information sources and inspecting their individual and synergistic effects on the advancement of dynamic capabilities.

We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations.

Luminal nutrient sensing by G-protein-coupled receptors (GPCR) expressed on the apical domain of enteroendocrine cells activates intracellular pathways leading to secretion of gut hormones that control vital physiological processes such as digestion, absorption, food intake and glucose homeostasis. The taste 1 receptor (T1R) family of GPCR consists of three members: T1R1; T1R2; T1R3. Expression of T1R1, T1R2 and T1R3 at mRNA and protein levels has been demonstrated in the intestinal tissue of various species. It has been shown that T1R2–T1R3, in association with G-protein gustducin, is expressed in intestinal K and L endocrine cells, where it acts as the intestinal glucose (sweet) sensor. A number of studies have demonstrated that activation of T1R2–T1R3 by natural sugars and artificial sweeteners leads to secretion of glucagon-like peptides 1&2 (GLP-1 and GLP-2) and glucose dependent insulinotropic peptide (GIP). GLP-1 and GIP enhance insulin secretion; GLP-2 increases intestinal growth and glucose absorption. T1R1–T1R3 combination co-expressed on the apical domain of cholecystokinin (CCK) expressing cells is a luminal sensor for a number of l-amino acids; with amino acid-activation of the receptor eliciting CCK secretion. This article focuses on the role of the gut-expressed T1R1, T1R2 and T1R3 in intestinal sweet and l-amino acid sensing. The impact of exploiting T1R2–T1R3 as a nutritional target for enhancing intestinal glucose absorption and gut structural maturity in young animals is also highlighted.