Michael Obal

Improving banner ad strategies through predictive modeling

The purpose of this exploratory, data-driven study is to identify the optimal banner advertising strategies for achieving different business metric goals, such as effective cost per activity, via unique predictive modelling methods.,The k-fold cross-validation method is used to build predictive models to analyze 18,956 online banner advertising records.,Banner ads with high visual complexity and attractive offers tend to draw users to participate in online activities, whereas voluntary banner ads with low visual complexity tend to draw user clicks. Further, banner ads with lower visual complexity tend to have lower costs. Finally, the third quarter of a year is the most important period for online advertising campaigns in terms of achieving the optimal effectiveness and cost for running internet banner ads.,As only visual and temporal characteristics of internet banner ads are covered in this study, future research should concentrate on the specific language within each banner ad message. Further, this study does not specifically tie internet-specific metrics, such as activities, costs and clicks to business metrics, such as revenue and profit.,Advertisers can use the findings from this study to create an effective and cost-efficient banner advertising strategy. Specifically, firms should use larger banner ads with features and offers, advertise at the end of the year and use caution with rich media expandable banners and banner ad videos as they can significantly increase costs.,This is one of the first exploratory studies to use the k-fold cross-validation method to build predictive models to identify visual and temporal factors that significantly impact the effectiveness and cost of internet banner ads.

Trust and New B2B Technologies: An Empirical Study Identifying the Trust Issues Related to the Adoption Rates of New Technology

This paper analyzes the role of interorganizational trust in the adoption of new technology in the B2B environment. Using the Technology Adoption Model (TAM), we believe that supplier characteristics will moderate the perceived usefulness, perceived ease of use and interorganizational trust, therefore impacting the likelihood of new technology adoption.

Investigating Social Media Activity as a Firm’s Signaling Strategy Through an Initial Public Offering

Initial public offerings (IPOs) are a crucial step for entrepreneurial firms. Despite the growing popularity of social media among a variety of audiences including potential investors, limited studies have been conducted to investigate how firms can utilize social media to attract financial capitals during the IPO process. We attempt to shed light on this area through the signaling theoretical lens as well as the prior literature on electronic word of mouth (eWOM). Our study, based on Twitter and other data on 423 firms that went public in the US market from 2014 to 2015, provides significant evidence in support of a positive relationship between social media use by a firm and its IPO value. Furthermore, the effectiveness of a firm’s tweets is mediated by public responses to its tweets, and such effectiveness is also found to be stronger for B2C firms.

Improving Banner Ad Strategies Through Predictive Modeling

In this study, we apply unique predictive modeling and data mining methods to identify visual and temporal factors that have significant impacts on both the effectiveness and pricing of Internet banner ads. An analysis of 18,956 online advertising records aims to identify the optimal banner advertising strategies for achieving different business metrics, including effective cost per activity (eCPA). Specifically, we find that banner ads with high visual complexity and attractive offers tend to draw users to participate in online activities, while voluntary banner ads with low visual complexity tend to draw user clicks. Further, banner ads with lower visual complexity tend to have lower costs. The size and shape of banner ads also play a key role as larger banner ads are more effective. Finally, we find that the third quarter of a year is the most important period for online advertising campaigns. Advertisers can use the findings from this study to create an effective and cost-efficient banner advertising strategy. Specifically, firms should use larger banner ads with features and offers, advertise at the end of the year, and use caution with banner ad animations as they can significantly increase costs.

The Counterintuitive Influence of Buyer–Supplier Relationships on Disruptive Technology Adoption: An Extended Abstract

While there has long been a focus on the antecedents of product adoption, there has been significantly less research that has taken a look at the antecedents of quality decision making in an adoption scenario (Bhattacherjee 2001). How does an IT manager know that the technology he/she is buying will provide the correct solution for their current issue? Furthermore, how does the buying manager know that he/she is adopting the ideal technology that will be used continuously by his/her firm and prove to be a fruitful investment?

TYPE OF KNOWLEDGE SHARING AND ITS IMPACT ON COLLABORATIVE NEW PRODUCT DEVELOPMENT

While integrating suppliers into producer new product development (NPD) would appear to be beneficial, there lacks a consensus as to what knowledge should be shared between producers and suppliers, especially within turbulent environments. This paper examines how in-depth and complex the knowledge sharing should be between producers and suppliers, given varying environmental conditions. To this point, we propose that small-scale technical exchanges are generally more effective in collaborative NPD efforts than more complex technology transfers. We also analyze two forms of environmental uncertainty, market turbulence and technology turbulence, as they influence the knowledge sharing measures. A survey of project managers reveals that while technical exchanges positively influence NPD performance, technology transfers have no impact (except in radical product development projects) and thus may not always be appropriate in producer–supplier NPD collaborations. We also find that market turbulence and technology turbulence influence technical exchanges, but in differing manners.

The Influence of Producer–Supplier Exchanges and Environmental Dynamics on NPD

The influence of producer–supplier collaboration on new product development (NPD) performance has increasingly become a popular topic in academe and practice. However, our understanding of what leads to collaboration success remains limited. While it is clear that suppliers are becoming an integral part of the design team (Enkel et al. 2009), research regarding performance benefits is fragmented (Johnsen 2009). Many studies find a positive relationship between supplier integration and performance (Lawson et al. 2009) while others show either no relationship or a negative relationship (Koufteros et al. 2005). Further, while external environmental factors may influence internal strategic decisions, it remains unclear how these antecedents influence producer–supplier relationship factors, such as trust and knowledge sharing, which subsequently influence NPD success (Bstieler 2006). A more nuanced understanding of how producer–supplier collaborations impact NPD performance is necessary.

Customer Participation in New Product Development and New Product Performance: The Moderating Role of Expertise

New product developers have increasingly utilized input from customers to co-create innovative new products. However, while much of the extant literature focuses on the positive side of customer participation on the new product development (NPD) process, significantly less work has focused on the negative side of co-creation. This study contends that a customer participation paradox exists: while customer participation in NPD may potentially help product performance, it could also lead to the development of products that are overly radical and are too difficult for potential customers to understand. To resolve this paradox, we argue that firms with higher levels of expertise will be able to rein in the negative aspects of extreme product newness to create products that will be in high demand by the marketplace. A survey of 305 firms involved in NPD activities was used to test our conceptual model. The results show that customer participation in the NPD process leads to overly new products that do not perform well on the market. Further, a multi-group analysis reveals that customer participation in NPD only has a positively influence on product performance for firms with high levels of expertise. The findings from this study demonstrate that while customer participation in NPD is beneficial for firms with high expertise, it may actually have a detrimental impact on product performance for those firms with low ACAP.

Sales Force Acceptance of Disruptive Technologies

In this study, we measure the roles of individual performance motives, team goal commitment motives, and managerial support on the acceptance of disruptive technologies amongst sales force members. Disruptive technologies are unique in that they require the end user to drastically change their work processes in order to fully integrate the technology. As this transition to a disruptive technology takes both time and effort, a salesperson is likely to be distracted from their primary goals (e.g., hitting deadlines or goals). We explore the interaction of these factors with an online survey of 163 sales force members. A confirmatory factor analysis was run to confirm the validity and reliability of the data while OLS regression was used to analyze the results. To facilitate this analysis, we develop a new, five-item scale that measures technology disruptiveness at the employee level.

An Investigation on the Role of Disruptive Technology Adoption on New Product Market Performance and Launch Timeliness

In order to increase productivity and efficiency during new product development (NPD), innovation managers are often faced with the question of adopting new technologies (Barczak et al. 2007). This could range from adopting newer physical systems used for product development (e.g. rapid prototyping systems, CRM software, etc.) to the adoption of quality methodologies (e.g. Six Sigma or Lean) in order to optimize productivity across different phases of product development. The benefits of integrating new technology into the NPD process appear obvious as previous literature has found that the adoption of new technologies can drastically impact innovation processes and services (Lyytinen and Rose 2003). However, as employees tend to meet procedural changes with reluctance, technology adoption can be risky (Morgan and Inks 2001; Lyytinen and Rose 2003). Further complicating this dilemma is the disruptiveness of the technology. By definition, disruptive technologies differ drastically from a preceding generation’s technologies, leading to a steeper learning curve, more short-term sacrifices, and increased reluctance from employees and end users (Morgan and Inks 2001; Sherif et al. 2006).