Creation of principal-agency relationship value : social capital and dynamic learning capability perspectives

In this \u27age of turbulence\u27 (Greenspan, 2007), businesses, in response to challenges of globalized competition, escalated customer expectation, and disruptive technological innovations, find innovative value propositions (Slater, 1997) critical for survival and sustained competitiveness. In lined with relationship marketing that suppliers need target valuable custome r to establish long-term relationship for survival in fierce competition (Gronroos, 2000), scholars (e.g. Walter, Ritter & Gemunden, 2001) looking from supplier perspective identify direct and indirect value as two dimensions for supplier-perceived relationship value. Direct value-based drivers of business relationships consist of higher profits from the product and service offering (i.e. profit function), growth of trade volumes (i.e. volume function), and the possibility to sell over-capacity (i.e. safeguard function). Indirect value-based drivers of business relationship consist of customers’ contribution in cooperative development of new products or processes (i.e. innovative function), intelligence about the markets and customers (i.e. market function and scout function), and facilitation of access to important third parties (i.e. access function). To extend prior literatures, this study tries to explore the antecedents of relationship value from both dynamic capability perspective and social capital perspective. Drawing upon a database of 411 manufacturer-channel partner relationships, this study examines the impacts of three dimensions of social capital (i.e. structural, relational, and cognitive dimensions: in the forms of extra- industry ties of principal managers, competence-based trust, and strategic consensus with a specific channel partner), and two types of learning (i.e. exploratory learning and exploitative learning) on the creation of relational value, that in turn, affects relationship performance. Specifically, the findings demonstrate that: (1) relationship value has impact on both relationship performance and market performance; (2) dynamic learning capabilities have significant impacts on the creation of relationship value; (3) social capital of principals contributes a lot to the creation of relationship value; (4) the impacts of social capital on relationship value are partially mediated by exploratory and exploitative learning; and finally (5) knowledge non-redundancy between principals and agents positively moderates the overall linkage between social capital and principal-agent learning. On the basis of current findings, managerial implications and future research directions are drawn

Universal holomorphic maps with slow growth I. An Algorithm

We design an Algorithm to fabricate universal holomorphic maps between any two complex Euclidean spaces, within preassigned transcendental growth rate. As by-products, universal holomorphic maps from $\mathbb{C}^n$ to $\mathbb{CP}^m$ ($n\leqslant m$) and to complex tori having slow growth are obtained. We take inspiration from Oka manifolds theory, Nevanlinna theory, and hypercyclic operators theory.Comment: final versio

Universal holomorphic maps with slow growth II. functional analysis methods

By means of hypercyclic operator theory, we complement our previous results on hypercyclic holomorphic maps between complex Euclidean spaces having slow growth rates,by showing {\it abstract abundance} rather than {\it explicit existence}. Next, we establish that, in the space of holomorphic maps from $\mathbb{C}^n$ to any connected Oka manifold $Y$, equipped with the compact-open topology, there exists a {\em dense} subset consisting of common {\em frequently hypercyclic} elements for all nontrivial translation operators. To our knowledge, this is new even for $n=1$ and $Y=\mathbb{C}$

The Diphoton Excess, Low Energy Theorem and the 331 Model

We interpret the diphoton anomaly as a heavy scalar $H_3$ in the so-called 331 model. The scalar is responsible for breaking the $SU(3)_C\otimes SU(3)_L\otimes U(1)_X$ gauge symmetry down to the standard model electroweak gauge group. It mainly couples to the standard model gluons and photons through quantum loops involving heavy quarks and leptons. Those quarks and leptons, in together with the SM quarks and leptons, form the fundamental representation of the 331 model. We use low energy theorem to calculate effective coupling of $H_3gg$, $H_3\gamma\gamma$, $H_3ZZ$, $H_3WW$ and $H_3Z\gamma$. The analytical results can be applied to new physics models satisfying the low energy theorem. We show that the heavy quark and lepton contribution cannot produce enough diphoton pairs. It is crucial to include the contribution of charged scalars to explain the diphoton excess. The extra neutral $Z^\prime$ boson could also explain the 2 TeV diboson excess observed at the LHC Run-I.Comment: To appear in PR

2-[1-(9-Anthrylmeth­yl)-1H-pyrazol-3-yl]pyridine

The title compound, C23H17N3, can be used in coordination chemistry. The anthracene ring makes dihedral angles of 86.08 (5) and 76.63 (6)°, respectively, with the pyridine and pyrazole rings. The dihedral angle between the pyrazole and pyrimidine rings is 11.79 (7)°. In the structure, weak inter­molecular C—H⋯N hydrogen bonds are observed

Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks

In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unmanned aerial vehicles (UAVs) are deployed to provide edge computing service. We first formulate a multi-objective optimization problem to minimize the beampattern performance of multi-input multi-output (MIMO) radars and the computation offloading energy consumption simultaneously. Then, we explore the prediction capability of DT to provide intelligent offloading decision, where the DT estimation deviation is considered. To track this challenge, we reformulate the original problem as a multi-agent Markov decision process and design a multi-agent proximal policy optimization (MAPPO) framework to achieve a flexible learning policy. Furthermore, the Beta-policy and attention mechanism are used to improve the training performance. Numerical results show that the proposed method is able to balance the performance tradeoff between sensing and computation functions, while reducing the energy consumption compared with the existing studies.Comment: 14 pages, 11 figures