Market orientation research: a qualitative synthesis and future research agenda

The prominence of market orientation as a core organizational concept, as well as recent discussions about its contributions and shortcomings, suggest that it is time to assess the large and expanding body of research surrounding the construct. Our research takes a systematic qualitative approach to assessing extant research on market orientation, identifying problems, patterns, and paradoxes in the literature. Specifically, we provide an integration of the burgeoning empirical research on market orientation, based on a thorough synthesis of articles published over more than 25 years of inquiry. Bringing together the different strands of inquiry that have guided knowledge accumulation on market orientation, we are able to identify major theoretical gaps and omissions in the literature. We also find that while market orientation research has made considerable strides over the years, much work still needs to be done as voids remain that need to be addressed in future investigations.

     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

ENTERTAINING MALTHUS: BREAD,CIRCUSES, AND ECONOMIC GROWTH

Motivated by the basic adage that man does not live by bread alone, we offer a theory of historical economic growth and population dynamics where human beings need food to survive, but enjoy other things, too. Our model imposes a Malthusian constraint on food, but introduces a second good to the analysis that affects living standards without affecting population growth. We show that technological change does a good job explaining historical consumption patterns and population dynamics, including the Neolithic Revolution, the Industrial Revolution, and the Great Divergence. Our theory stands in contrast to models that assume a single composite good and a Malthusian constraint. These models generate negligible growth prior to the Industrial Revolution. However, recent revisions to historical data show that historical living standards—though obviously much lower than today's—varied over time and space much more than previously thought. These revisions include updates to Maddison's dataset, which served as the basis for many papers taking long‐run stagnation as a point of departure. This new evidence suggests that the assumption of long‐run stagnation is problematic. Our model shows that when we give theoretical accounting of these new observations the Industrial Revolution is much less puzzling. (JEL B10, I31, J1, N1, O30)     

Optimum covariate designs in partially balanced incomplete block (PBIB) design set-ups

The use of covariates in block designs is necessary when the covariates cannot be controlled like the blocking factor in the experiment. In this paper, we consider the situation where there is some flexibility for selection in the values of the covariates. The choice of values of the covariates for a given block design attaining minimum variance for estimation of each of the parameters has attracted attention in recent times. Optimum covariate designs in simple set-ups such as completely randomised design (CRD), randomised block design (RBD) and some series of balanced incomplete block design (BIBD) have already been considered. In this paper, optimum covariate designs have been considered for the more complex set-ups of different partially balanced incomplete block (PBIB) designs, which are popular among practitioners. The optimum covariate designs depend much on the methods of construction of the basic PBIB designs. Different combinatorial arrangements and tools such as orthogonal arrays, Hadamard matrices and different kinds of products of matrices viz. Khatri–Rao product, Kronecker product have been conveniently used to construct optimum covariate designs with as many covariates as possible.     

Pointwise and uniform convergence of multivariate kernel density estimators using random bandwidths

We obtain the rates of pointwise and uniform convergence of multivariate kernel density estimators using a random bandwidth vector obtained by some data-based algorithm. We are able to obtain faster rate for pointwise convergence. The uniform convergence rate is obtained under some moment condition on the marginal distribution. The rates are obtained under i.i.d. and strongly mixing type dependence assumptions.     

D-Optimal Designs for Covariate Models

The problem of finding D-optimal designs in the presence of a number of covariates has been considered in the one-way set-up. This is an extension of Dey and Mukerjee (2006 Dey , A. , Mukerjee , R. ( 2006 ). D-optimal designs for covariate models . Statistics 40 : 297 – 305 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) in the sense that for fixed replication numbers of each treatment, an alternative upper bound to the determinant of the information matrix has been found through completely symmetric C-matrices for the regression coefficients; this upper bound includes the upper bound given in Dey and Mukerjee (2006 Dey , A. , Mukerjee , R. ( 2006 ). D-optimal designs for covariate models . Statistics 40 : 297 – 305 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) obtained through diagonal C-matrices. Because of the fact that a smaller class of C-matrices was used at the intermediate stage where the replication numbers were fixed, ultimately some optimal designs remained unidentified there. These designs have been identified here and thereby the conjecture made in Dey and Mukerjee (2006 Dey , A. , Mukerjee , R. ( 2006 ). D-optimal designs for covariate models . Statistics 40 : 297 – 305 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) has been settled.     

Studying Crisis Communication From the Subaltern Studies Framework: Grassroots Activism in the Wake of Hurricane Katrina

The dominant crisis communication literature has 3 limitations: its managerial bias, functionalistic orientation, and the erasure of marginalized voices by focusing on restituting the status quo. Due to these limitations, studies that aim to understand crisis experiences, interpretive processes, and communicative responses of the politically less powerful and resource-poor are scarce in the crisis communication literature. Drawing upon the subaltern studies literature, this article suggests an alternative approach (i.e., the subaltern studies framework) to aid crisis communication researchers to (a) expand the scope of the literature to nonmanagerial contexts, (b) speak to the discursive nature of crisis communication, and (c) attend to the issues of structure and agency in the interpretations of and responses to crises. A case study of grassroots activism in New Orleans is presented to illustrate how the application of the subaltern studies framework can provide a theoretical entry point for conceptualizing crisis communication from below and for facilitating academic self-reflexivity in crisis communication scholarship.     

On intertemporal poverty measures: the role of affluence and want

This paper proposes classes of intertemporal poverty measures which take into account both the debilitating impact of prolonged spells in poverty and the mitigating effect of periods of affluence on subsequent poverty. The weight assigned to the level of poverty in each time period depends on the length of the preceding spell of poverty or of non-poverty. The proposed classes of intertemporal poverty measures are quite general and allow for a range of possible judgements as to the overall impact on a poor period of preceding spells of poverty or affluence. We discuss the properties of the proposed classes of measures and axiomatically characterize these measures.